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1
Moussa, Wael. „Thermography-Assisted Bearing Condition Monitoring“. Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31379.
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Abstract
Despite the large amount of research work in condition based maintenance and condition monitoring methods, there is still a need for more reliable and accurate methods.
The clear evidence of that need is the continued dependence on time based maintenance, especially for critical applications such as turbomachinery and airplane engines. The lack of accurate condition monitoring systems could lead to not only the unexpected failures as well as the resulting hazards and repair costs, but also a huge waste of material and time because of unnecessary replacement due to false alarms and unnecessary repair and maintenance.
Temperature change is a phenomenon that accompanies every dynamic activity in the universe. However, it has not been adequately exploited for mechanical system condition monitoring. The reason is the slow response of current temperature monitoring systems compared to other condition monitoring methods such as vibration analysis. Many references inferred that the change in temperature is not sensible until approaching the end of the monitored component life and even the whole system life (Kurfess, et al., 2006; Randall, 2011; Patrick, et al., March 7-14, 2009).
On the other hand, the most commonly used condition monitoring method, i.e., vibration analysis, is not free from pitfalls. Although vibration analysis has shown success in detecting some bearing faults, for other faults like lubrication problems and gradual wear it is much less effective. Also, it does not give a reliable indication of fault severity for many types of bearing faults.
The advancement of thermography as a temperature monitoring tool encourages the reconsideration of temperature monitoring for mechanical system fault detection. In addition to the improved accuracy and responsiveness, it has the advantage of non-contact monitoring which eliminates the need for complex sensor mounting and wiring especially for rotating components.
Therefore, in current studies the thermography-based monitoring method is often used either as a distinct method or as a complementary tool to vibration analysis in an integrated condition monitoring system. The main objectives of this study are hence to:
1. Define heat sources in the rolling element bearings and overview two of the most famous bearing temperature calculation methods.
2. Setup a bearing test rig that is equipped with both vibration and temperature monitoring systems.
3. Develop a temperature calculation analytical model for rolling element bearing that include both friction calculation and heat transfer models. The friction calculated by the model will be compared to that calculated using the pre-defined empirical methods. The heat transfer model is used for bearing temperature calculation that will be compared to the experimental measurement using different temperature monitoring devices.
4. Propose a new in-band signal enhancement technique, based on the synchronous averaging technique, Autonomous Time Synchronous Averaging (ATSA) that does not need an angular position measuring device. The proposed method, in addition to the Spectral Kurtosis based band selection, will be used to enhance the bearing envelope analysis.
5. Propose a new method for classification of the bearing faults based on the fault severity and the strength of impulsiveness in vibration signals. It will be used for planning different types of tests using both temperature and vibration methods.
6. Develop and experimentally test a new technique to stimulate the bearing temperature transient condition. The technique is supported by the results of finite element modeling and is used for bearing temperature condition monitoring when the bearing is already running at thermal equilibrium condition.
2
Chen, Ping. „Bearing condition monitoring and fault diagnosis“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/mq64993.pdf.
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3
Gouws, Rupert. „Condition monitoring of active magnetic bearing systems / R. Gouws“. Thesis, North-West University, 2007. http://hdl.handle.net/10394/1305.
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4
Johnston, Andrew Beaton. „Condition monitoring of reciprocating compressors and rolling element bearings“. Thesis, University of Aberdeen, 1985. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU365562.
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The prefailure detection of faults in operating plant can effect major rewards in both safety and economy. A successful on-condition maintenance philosophy would pay great dividends particularly in the offshore oil industry where -until recently, only token methods have been employed. Many techniques are available for monitoring mechanical plant and several of these are considered in general terms. Industrial methods are subsequently evaluated on reciprocating compressor and rolling element bearing faults. Bearing fault analysis is considered in two stages. Initially, a series of vibration based techniques are evaluated on a large relatively noise free rotating machine. The techniques of greatest worth carrier spectra, autospectra, time signature analysis and statistical assessments - are then applied to bearings in the hostile environment of a reciprocating machine. It is shown that while discrete faults often produce predictable periodic vibrational patterns, a monitoring system aimed solely at such vibrational phenomena cannot be relied upon. To this end, a diagnostic system must encompass a series of techniques, including carrier spectrum, time signature and statistical analyses. A series of valve and piston faults in reciprocating machines are also studied. By using a number of monitoring techniques, a catalogue of fault characteristics is constructed, and the methods of greatest worth are high-lighted. It is noted that due to the complexities of a reciprocating machine, fault characteristics vary with load, and this must be borne in mind when interpreting the various parameter displays. No single technique can provide a complete cover for all compressor faults, and it is shown that those of greatest worth are acoustic emission, combined pressure and vibration plots, temperature and performance analysis. An indication of compressor temperature and internal cylinder pressure can greatly ease the detection and diagnostic process, and for the latter, bolt load determinations may be a valuable aid.
5
Feng, Yanhui. „Novel acoustic emission signal processing methods for bearing condition monitoring“. Thesis, University of Leicester, 2008. http://hdl.handle.net/2381/8613.
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Rolling Element Bearing is one of the most common mechanical components to be found in critical industrial rotating machinery. Since the failure of bearings will cause the machine to malfunction and may quickly lead to catastrophic failure of the machinery, it is very important to detect any bearing deterioration at an early stage. In this thesis, novel signal processing methods based on Acoustic Emission measurement are developed for bearing condition monitoring. The effectiveness of the proposed methods is experimentally demonstrated to detect and diagnose localised defects and incipient faults of rolling element bearings on a class of industrial rotating machinery – the iGX dry vacuum pump. Based on the cyclostationary signal model and probability law governing the interval distribution, the thesis proposes a simple signal processing method named LocMax-Interval on Acoustic Emission signals to detect a localised bearing defect. By examining whether the interval distribution is regular, a localised defect can be detected without a priori knowledge of shaft speed and bearing geometry. The Un-decimated Discrete Wavelet Transform denoising is then introduced as a pre-processing approach to improve the effective parameter range and the diagnostic capability of the LocMax-Interval method. The thesis also introduces Wavelet Packet quantifiers as a new tool for bearing fault detection and diagnosis. The quantifiers construct a quantitative time-frequency analysis of Acoustic Emission signals. The Bayesian method is studied to analyse and evaluate the performance of the quantifiers. This quantitative study method is also performed to investigate the relationships between the performance of the quantifiers and the factors which are important in implementation, including the wavelet order, length of signal segment and dimensionality of diagnostic scheme. The results of study provide useful directions for real-time implementation.
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Gómez, Adrián (Gomez ́Velázquez) 1977. „Condition monitoring of bearing damage : test implementation and data acquisition“. Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/89288.
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Chen, Su Liang. „Development of automated bearing condition monitoring using artificial intelligence techniques“. Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/195557/.
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A recent series of tapered roller bearing tests have been conducted at the University of Southampton to evaluate the effectiveness of using multiple sensing technologies to detect incipient faults. The test rig was instrumented with on-line sensors including vibration, temperature and electrostatic wear and oil-line debris sensors. Off-line techniques were also used such as debris analysis and bearing surface examination. The electrostatic sensors, in particular, have the potential to detect early decay of tribological contacts within rolling element bearings. These sensors have the unique ability to detect surface charge associated with surface phase transformations, material transfer, tribofilm breakdown and debris generation. Thus, they have the capability to detect contact decay before conventional techniques such as vibration and debris monitoring. However, precursor electrostatic events can not always be clearly seen using time and frequency based techniques. Therefore, an intelligent system that can process signals from multiple sensors is needed to enable early and automatic detection of novel events and provide reasoning to these detected anomalies. Operators could then seek corroborative trends between sensors and set robust alarms to ensure safe running. This has been the motivation of this study.
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Billington, Scott Alexander. „Sensor and machine condition effects in roller bearing diagnostics“. Thesis, Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/17796.
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Kaewkongka, Tonphong. „Bearing condition monitoring using acoustic emission and vibration : the systems approach“. Thesis, Brunel University, 2002. http://bura.brunel.ac.uk/handle/2438/7862.
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This thesis proposes a bearing condition monitoring system using acceleration and acoustic emission (AE) signals. Bearings are perhaps the most omnipresent machine elements and their condition is often critical to the success of an operation or process. Consequently, there is a great need for a timely knowledge of the health status of bearings. Generally, bearing monitoring is the prediction of the component's health or status based on signal detection, processing and classification in order to identify the causes of the problem. As the monitoring system uses both acceleration and acoustic emission signals, it is considered a multi-sensor system. This has the advantage that not only do the two sensors provide increased reliability they also permit a larger range of rotating speeds to be monitored successfully. When more than one sensor is used, if one fails to work properly the other is still able to provide adequate monitoring. Vibration techniques are suitable for higher rotating speeds whilst acoustic emission techniques for low rotating speeds. Vibration techniques investigated in this research concern the use of the continuous wavelet transform (CWT), a joint time- and frequency domain method, This gives a more accurate representation of the vibration phenomenon than either time-domain analysis or frequency- domain analysis. The image processing technique, called binarising, is performed to produce binary image from the CWT transformed image in order to reduce computational time for classification. The back-propagation neural network (BPNN) is used for classification. The AE monitoring techniques investigated can be categorised, based on the features used, into: 1) the traditional AE parameters of energy, event duration and peak amplitude and 2) the statistical parameters estimated from the Weibull distribution of the inter-arrival times of AE events in what is called the STL method. Traditional AE parameters of peak amplitude, energy and event duration are extracted from individual AE events. These events are then ordered, selected and normalised before the selected events are displayed in a three-dimensional Cartesian feature space in terms of the three AE parameters as axes. The fuzzy C-mean clustering technique is used to establish the cluster centres as signatures for different machine conditions. A minimum distance classifier is then used to classify incoming AE events into the different machine conditions. The novel STL method is based on the detection of inter-arrival times of successive AE events. These inter-arrival times follow a Weibull distribution. The method provides two parameters: STL and L63 that are derived from the estimated Weibull parameters of the distribution's shape (y), characteristic life (0) and guaranteed life (to). It is found that STL and 43 are related hyperbolically. In addition, the STL value is found to be sensitive to bearing wear, the load applied to the bearing and the bearing rotating speed. Of the three influencing factors, bearing wear has the strongest influence on STL and L63. For the proposed bearing condition monitoring system to work, the effects of load and speed on STL need to be compensated. These issues are resolved satisfactorily in the project.
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Nembhard, Adrian. „On-bearing vibration response integration for condition monitoring of rotating machinery“. Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/onbearing-vibration-response-integration-for-condition-monitoring-of-rotating-machinery(f713f156-11f3-4e10-846e-0b9b709f0ff9).html.
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Vibration-based fault diagnosis (FD) with a simple spectrum can be complex, especially when considering FD of rotating machinery with multiple bearings like a multi-stage turbine. Various studies have sought to better interpret fault spectra, but the process remains equivocal. Consequently, it has been accepted that the simple spectra requires support from additional techniques, such as orbit analysis. But even orbit analysis can be inconclusive. Though promising, attempts at developing viable methods that rival the failure coverage of spectrum analysis without gaining computational complexity remain protracted. Interestingly, few researchers have developed FD methods for transient machine operation, however, these have proven to be involved. Current practices limit vibration data to a single machine, which usually requires a large unique data history. However, if sharing of data between similar machines with different foundations was possible, the need for unique histories would be mitigated. From readily available works, this has not been encountered. Therefore, a simple but robust vibration-based approach is warranted. In light of this, a novel on-bearing vibration response integration approach for condition monitoring of shaft-related faults irrespective of speed and foundation type is proposed in the present study. Vibration data are acquired at different speeds for: a baseline, unbalance, bow, crack, looseness, misalignment, and rub conditions on three laboratory rigs with dynamically different foundations, namely: rigid, flexible support 1 (FS1) and flexible support 2 (FS2). Testing is done on the rigid rig set up first, then FS1, and afterwards FS2. Common vibration features are computed from the measured data to be input to the proposed approach for further processing. First, the proposed approach is developed through its application to a machine at a steady speed in a novel Single-speed FD technique which exploits a single vibration sensor per bearing and fusion of features from different bearings for FD. Initially, vibration features are supplemented with bearing temperature readings with improved classification compared to vibration features alone. However, it is observed that temperature readings are insensitive to faults on the FS1 and FS2 rigs, when compared to vibration features, which are standardised for consistent classification on the different rigs tested. Thus, temperature is not included as a final feature. The observed fault classifications on the different rigs at different speeds with the standardised vibration features are encouraging. Thereafter, a novel Unified Multi-speed FD technique that is based on the initial proposed approach and which works by fusion of vibration features from different bearings at different speeds in a single analysis step for FD is proposed. Experiments on the different rigs repeatedly show the novel Multi-speed technique to be suitable for transient machine operation. Then, a novel generic Multi-foundation Technique (also based on the proposed approach) that allows sharing of vibration data of a wide range of fault conditions between two similarly configured machines with similar speed operation but different foundations is implemented to further mitigate data requirements in the FD process. Observations made with the rigs during steady and transient speed tests show this technique is applicable in situations where data history is available on one machine but lacking on the other. Comparison of experimental results with results obtained from theoretical simulations indicates the approach is consistent. Thus, the proposed approach has the potential for practical considerations.
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Soltani, Bozchalooi Iman. „Bearing vibration and oil debris signal enhancement for machinery condition monitoring“. Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27486.
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Vibration signal and lubricant oil condition are two major sources of information for machine health condition monitoring. Though vibration signal is an indirect indicator of machine conditions, it contains very rich information. On the other hand, the lubricating oil analysis provides a direct indicator of machine health conditions. The joint use of the two sources of information would compensate for their limitations and thus better maintenance actions can be expected. However, this alone is not sufficient since the two sources are often severely contaminated by background and machine interference noises. Using such contaminated data without careful de-noising will inevitably cause misleading maintenance decisions and hence premature machine failure as well as lost productivity. As such, this thesis addresses the de-noising issues for both vibration and oil condition signals. Due to different natures of the vibration signals and signals measured through oil debris monitoring sensors, different approaches will be developed in this study for the enhancement of the two types of signals. In de-noising vibration signals, this research focuses on bearings since they are one of the most vulnerable and frequently used components in rotating machinery. The results obtained based on bearings could be applied to other rotating machine components with some modifications.
Wavelet transform, in particular the Gabor wavelet transform, has been used for de-noising impulsive signals measured from faulty bearings. However, it has been a challenging task to select proper wavelet parameters. This work introduces a method to guide the selection process by a smoothness index (SI). The SI is defined as the ratio of the geometric mean to the arithmetic mean of the wavelet coefficient moduli of the vibration signal. For the signal contaminated by Gaussian white noise, we have shown that the modulus of the wavelet coefficients follows Rician distribution. Based on this observation, we then prove that the SI converges to a constant number (0.8455...) in the absence of mechanical faults or for very low signal to noise ratio. This result provides a dimensionless SI upper bound corresponding to the most undesirable case. We have also shown that the SI value decreases in the presence of impulses with properly selected parameters.
However, this approach is based on the assumption that the most impulsive components of the measured vibration are due to the faults. This assumption may not be valid in general. On the other hand, the proposed method requires a global search for the minimum SI for all combinations of wavelet parameters in the chosen discretized ranges which is a computationally demanding task. In addition, through bandpass filtering the signal, the in-band noise with frequency content in the range covered by the daughter wavelet is not eliminated. As a result, the performance of the wavelet filter based de-noising method deteriorates as the background noise intensity increases.
To mitigate the above difficulties, a novel scale selection method is proposed. In this approach we incorporated our knowledge of the resonance frequency excitation phenomenon in the scale selection algorithm. Furthermore, to improve the efficiency of the method, spectral subtraction is applied prior to wavelet transform. The proposed spectral subtraction method leads to improvements in both the final result of the process and the capability of the wavelet filter based de-noising method for lower SNR vibration signals. The proposed joint spectral subtraction and wavelet de-noising method has been successfully tested using experimental data.
For the oil condition signals, the main issue is that the oil debris sensor is not only sensitive to the metal debris or particles but the structural vibrations as well. The weak signals of small particles are often concealed in the vibration signals. This either causes false alarm (since the shape of a particle signal resembles that of a vibration signal in certain ways) or leaves existent machine faults undetected. Adaptive Line Enhancement technique is proposed to remove such interferences. The method has been tested on both simulated and experimental data.
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Cease, Barry T. „Multi-feature signature analysis for bearing condition monitoring using neural network methodology“. Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/19328.
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13
Faghidi, Hamid. „Non-parametric and Non-filtering Methods for Rolling Element Bearing Condition Monitoring“. Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/30689.
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Rolling element bearings are one of the most significant elements and frequently-used components in mechanical systems. Bearing fault detection and diagnosis is important for preventing productivity loss and averting catastrophic failures of mechanical systems. In industrial applications, bearing life is often difficult to predict due to different application conditions, load and speed variations, as well as maintenance practices. Therefore, reliable fault detection is necessary to ensure productive and safe operations.
Vibration analysis is the most widely used method for detection and diagnosis of bearing malfunctions. A measured vibration signal from a sensor is often contaminated by noise and vibration interference components. Over the years, many methods have been developed to reveal fault signatures, and remove noise and vibration interference components.
Though many vibration based methods have been proposed in the literature, the high frequency resonance (HFR) technique is one of a very few methods have received certain industrial acceptance. However, the effectiveness of the HFR methods depends, to a great extent, on some parameters such as bandwidth and centre frequency of the fault excited resonance, and window length. Proper selection these parameters is often a knowledge-demanding and time-consuming process. In particular, the filter designed based on the improperly selected bandwidth and center frequency of the fault excited resonance can filter out the true fault information and mislead the detection/diagnosis decisions. In addition, even if these parameters can be selected properly at beginning of each process, they may become invalid in a time-varying environment after a certain period of time. Hence, they may have to be re-calculated and updated, which is again a time-consuming and error-prone process. This undermines the practical significance of the above methods for online monitoring of bearing conditions.
To overcome the shortcomings of existing methods, the following four non-parametric and non-filtering methods are proposed:
1. An amplitude demodulation differentiation (ADD) method,
2. A calculus enhanced energy operator (CEEO) method,
3. A higher order analytic energy operator (HO_AEO) approach, and
4. A higher order energy operator fusion (HOEO_F) technique.
The proposed methods have been evaluated using both simulated and experimental data.
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Aini, Reza. „Vibration monitoring and modelling of shaft/bearing assemblies under concentrated elastohydrodynamic condition“. Thesis, Kingston University, 1990. http://eprints.kingston.ac.uk/20759/.
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A five degrees of freedom analysis of a perfect precision grinding spindle supported by a pair of back to back angular contact ball bearings is performed. The ball to race contacts are simulated by a non-linear contact spring, representing the elastic deformation of the mating rolling members. Major frequencies associated with various degrees of freedom are observed and a number of design curves, suggesting the best zones of operation for the simulated spindle under radial/ axial loading are also presented. The gyroscopic contribution of an ideal precision spindle was found to be insignificant. The model was further expanded to study the response characteristics of the spindle under lubricated contact conditions. A regression formula is used to model the non-linear spring/ damper arrangement,corresponding to the contact elastohydrodynamic oil film thickness. It is noted that the presence of the oil film along the line of contacts do not significantly alter the position of the major modes of the system. However, its contribution in damping the amplitude of oscillation are found to be significant. Various graphs indicating the overall system response, subjected to varying oil film viscosity, number of balls and the spindle mass are also presented. Furthermore, experimental investigations are conducted to validate the employed methodology. Good agreement is observed between the results of the simulation and the experimental spectra for the fundamental modes of response. Although manufacturing anamolies are not simulated,the formulated models incorporate sufficient versatility to forsee various spindle/bearing configurations, different loading arrangement as well as various geometrical features of a system to be modelled.
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Sandström, Tobias. „Condition Monitoring of Ceramic Ball Bearings in an Engine Testing Dynamometer“. Thesis, KTH, Maskinkonstruktion (Inst.), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183126.
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The choice of the topic addressed in this thesis aims to improve the service and maintenance on ceramic ball bearings in a specific test dynamometer and through an engineering approach develop tools for condition monitoring. The company connected to this thesis, AVL, is the world's largest privately owned company for development, simulation and testing technology of powertrains for passenger cars, trucks and large engines. Engine testing is a critical part of the business at AVL Sweden and unexpected bearing failure can result in long repair times and great economic losses due to loss of the testing time. In short terms, the methodological approach followed the following steps; first a thorough information retrieval regarding bearings and analysis was conducted. The search was deepened around areas such as hybrid ball bearings, bearing failure mechanisms, bearing defect frequencies, signal analysis and condition monitoring. After this a table for bearing damage detection was developed and a “step by step” guidance for condition monitoring. The tools where afterwards verified by simple testing to detect complications within the chosen system. The existing condition monitoring system that is used today revealed weaknesses as it lacked the feature of taking preventive measures. The system that is based on temperature measurements isn’t satisfactory enough, especially when it’s missing visual clarity. Service and maintenance according to specifications from the manufacturer should be scheduled to ensure operational and guarantees. Currently mounted accelerometers on the housing of the Dynas3 engine should be connected for collecting data and the total sum of energy should be calculated for simple monitoring of historical progression. This should be done by following the guidance in order to ensure proper data acquisition. The best way to implement condition monitoring showed to be by performing multi-parameter monitoring. The design of the condition monitoring system is highly connected to what to monitor and at what stage. One main consideration to keep in mind is that it’s very rare that manufacturing defects are the reason for bearing failure. Instead it derives from improper storage, transport, handling or dimensional errors and even in some cases by improperly implemented force analysis prior to bearing selection.Huvudämnet som behandlas i detta examensarbete syftar till att förbättra service och underhåll på keramiska kullager i en viss testdynamometer och genom ett ingenjörsmässigt tillvägagångsätt utveckla verktyg för tillståndsövervakning. Företaget som är ansluten till detta examensarbete är AVL som är världens största privatägda företag för utveckling, simulering och testteknik för drivlinor för personbilar, lastbilar och stora motorer. Motorprovning är en viktig del av verksamheten vid AVL Sverige, och ett oväntat lagerhaveri kan leda till långa reparationstider och stora ekonomiska förluster på grund av utebliven test tid. I korta termer följde den metod som använts följande steg, först genomfördes en grundlig informationssökning om lager och tillhörande analyser. Efter det fördjupades sökande kring områden som hybrida kullager, lagerskademekanismer, frekvenser kopplade till lagerskador, signalanalys och tillståndsövervakning. Efter detta framställdes en tabell för detektering av lagerskador, samt en ”steg för steg” guide för tillståndsövervakning. Verktygen för tillståndsövervakning kontrolleras efteråt, genom att enkla tester genomfördes för att upptäcka komplikationer inom det valda systemet. Det övervakningssystem som används idag avslöjade svagheter genom att sakna funktionen att vidta förebyggande åtgärder. System som är baserat på temperaturmätningar är inte tillräckligt tillfredsställande, speciellt när det saknar en visuell tydlighet. Den service och underhåll som enligt tillverkarens föreskrifter påvisas bör planeras för att säkerställa drift och garantier. Nuvarande monterade accelerometrar fästa vid motorhöljet bör anslutas för att insamla data, och den totala summan av energin bör beräknas för en enkel övervakning av det historiska utvecklingsförloppet. Detta bör göras genom att följa de riktlinjer som framställts för att säkerställa korrekt datainsamling. Det bäst passande sättet att genomföra tillståndsövervakning på i detta fall visade sig vara att utföra multiparameterövervakning. Framställningen av tillståndsövervakningssystemet är starkt förknippat med vad som skall övervakas och i vilket skede. En huvudsaklig bidragande faktor att komma ihåg är att det är mycket ovanligt att fabrikationsfel är orsaken till lagerhaveri. Istället härstammar haveriet från felaktig förvaring, transportering, hantering eller dimensioneringsfel och i vissa fall av felaktigt genomförd kraftanalys inför lagerval.
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Hemmati, Farzad. „Rolling element bearing condition monitoring using acoustic emission technique and advanced signal processing“. Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43190.
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Acoustic emission (AE) signals generated from defects in rolling element bearings are investigated using simulated defects and experimental measurements in this thesis. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, fault diagnostics, defect size calculation, and prognostics.
In this thesis the effect of defect size, operating speed, and loading conditions on statistical parameters of AE signals, using design of experiment method (DOE), have been investigated to select the most sensitive parameters for diagnosing incipient faults and defect growth on rolling element bearings.
A novel signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals masked by background noise under different operating conditions.
To experimentally measure the defect size on rolling element bearings using acoustic emission technique, the proposed method along with spectrum of squared Hilbert transform are performed under different rotating speeds, loading conditions, and defect sizes to measure the time difference between the double AE impulses. Measurement results show the power of the proposed method for experimentally measuring size of different fault shapes using acoustic emission signals.
Fatigue life estimation of rolling element bearing has also been investigated utilizing defect size measurements combined with an adaptive algorithm. Experimental results show the effectiveness of recursive least square algorithm for predicting the future defect size on the outer race.
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Mirhadizadeh, S. A. „Monitoring hydrodynamic bearings with acoustic emission and vibration analysis“. Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7888.
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Acoustic emission (AE) is one of many available technologies for condition
health monitoring and diagnosis of rotating machines such as bearings. In
recent years there have been many developments in the use of Acoustic
Emission technology (AET) and its analysis for monitoring the condition of
rotating machinery whilst in operation, particularly on high speed machinery.
Unlike conventional technologies such as oil analysis, motor current signature
analysis (MCSA) and vibration analysis, AET has been introduced due to its
increased sensitivity in detecting the earliest stages of loss of mechanical
integrity.
This research presents an experimental investigation that is aimed at
developing a mathematical model and experimentally validating the influence of
operational variables such as film thickness, rotational speed, load, power loss,
and shear stress for variations of load and speed conditions, on generation of
acoustic emission in a hydrodynamic bearing. It is concluded that the power
losses of the bearing are directly correlated with acoustic emission levels. With
exponential law, an equation is proposed to predict power losses with
reasonable accuracy from an AE signal.
This experimental investigation conducted a comparative study between AE
and Vibration to diagnose the rubbing at high rotational speeds in the
hydrodynamic bearing. As it is the first known attempt in rotating machines. It
has been concluded, that AE parameters such as amplitude, can perform as a
reliable and sensitive tool for the early detection of rubbing between surfaces of
a hydrodynamic bearing and high speed shaft.
The application of vibration (PeakVue) analysis was introduced and compared
with demodulation. The results observed from the demodulation and PeakVue
techniques were similar in the rubbing simulation test. In fact, some defects on
hydrodynamic bearings would not have been seen in a timely manner without
the PeakVue analysis.In addition, the application of advanced signal processing and statistical
methods was established to extract useful diagnostic features from the acquired
AE signals in both time and frequency domain. It was also concluded that the
use of different signal processing methods is often necessary to achieve
meaningful diagnostic information from the signals. The outcome would largely
contribute to the development of effective intelligent condition monitoring
systems which can significantly reduce the cost of plant maintenance.
To implement these main objectives, the Sutton test rig was modified to assess
the capability of AET and vibration analysis as an effective tool for the detection
of incipient defects within high speed machine components (e.g. shafts and
hydrodynamic bearings).
The first chapter of this thesis is an introduction to this research and briefly
explains motivation and the theoretical background supporting this research.
The second and third chapters, summarise the relevant literature to establish
the current level of knowledge of hydrodynamic bearings and acoustic emission,
respectively. Chapter 4 describes methodologies and the experimental
arrangements utilized for this investigation. Chapter 5 discusses different NDT
diagnosis. Chapter 6 reports on an experimental investigation applied to
validate the relationship between AET on operational rotating machines, such
as film thickness, speed, load, power loss, and shear stress. Chapter 7 details
an investigation which compares the applicability of AE and vibration
technologies in monitoring a rubbing simulation on a hydrodynamic bearing.
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Rzeszucinski, Pawel. „Development of reliable vibration-based condition indicators and their data fusion for the robust health diagnosis of gearboxes“. Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/development-of-reliable-vibrationbased-condition-indicators-and-their-data-fusion-for-the-robust-health-diagnosis-of-gearboxes(fa25db2f-89a5-420f-ba56-68ef7da874f9).html.
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Performing condition monitoring related tasks on any machinery is an essential element of their rational maintenance. Endeavours to detect an incipient fault within a system serve multiple purposes from increasing the safety of people responsible for operating the machines through decreasing the running and operational costs, allowing time to plan for the inevitable repairs and making sure that the downtime of the machine is kept to an absolute minimum. All these tasks gain extra importance in a case when machines are operated in dangerous conditions putting people's lives in potential jeopardy - for instance in the field of operating a helicopter. The robust assessment of the condition of gearboxes used by helicopters has recently been given an increased attention due to a number of accidents which followed an undetected drive train component failure. The majority of the on-board mounted condition monitoring systems use vibration response signals which are specifically processed to obtain a single number which is representative of a condition of a given monitored drive train component. Those signal processing methods are called Condition Indicators (CIs). There are a number of such CIs which are already in use and they seem to adequately indicate faults in most of the cases. However in a number of instances it has been observed that the most popular parameters like Crest Factor or FM4 failed to dependably reflect the true condition of the gear causing serious accidents, some of which resulted in a number of lives being lost. For this reason the presented research is focused on investigating the limitations of the existing CIs and designing a set of improved CIs. The development process is based on overcoming the drawbacks of thetechniques used in existing CIs combined with the intelligence gathered while analysing the acceleration vibration signals which contained a gear or a bearing fault. Five new CIs are proposed and the details of their design are documented. Both the existing and the proposed CIs are applied on the available, uncorrelated datasets. The results of the comparison show that the newly developed CIs are capable of indicating a gear or a bearing fault in a more robust and dependable fashion. Each proposed CI alone may not be the most robust indicator of the actual condition of the monitored component hence the output from all proposed CIs is combined into a single indication through use of a novel data fusion model. The Combined CI created based on the data fusion model is observed to be more robust compared to each CI alone, hence it may increase the confidence level of the decision making routine and is expected to decrease the number of false alarms. The methods of the existing CIs, the proposed CIs and the data fusion techniques as well as the results of the comparison between the different approaches are present in this thesis.
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Johnson, Jason Eric. „Identifying Common Ultrasonic Predictive Failure Signatures in Bearing Elements for the Development of an Automated Condition Based Ultrasonic Monitoring Controller“. Digital Commons @ East Tennessee State University, 2005. https://dc.etsu.edu/etd/1097.
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This thesis presents a new method for Condition Based Ultrasonic Monitoring to be applied in conjunction with a lubrication distribution controller. As part of this thesis, algorithms were developed using ultrasonic sensors to control the application of lubrication to machinery. The controller sensors detect an ultrasonic signal from rolling or sliding machine elements. This signal then alerts the controller to dispense the proper amount of lubrication when needed, as opposed to a time schedule based on average performance or history. The work from this thesis will be used to help reduce equipment downtime and maintenance cost when utilized in an industrial environment.
20
Aliwan, Mustafa. „Exploration of a condition monitoring system for rolling bearing based on a wireless sensor network“. Thesis, University of Huddersfield, 2016. http://eprints.hud.ac.uk/id/eprint/29080/.
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In recent years, wireless sensor networks (WSN) have attracted attention in machine condition monitoring (CM) fields for a more efficient system based on the inherent advantages of WSN, including ease of installation and relocation, lower maintenance cost and the ability to be installed in places not easily accessible. As critical components of rotating machines, bearings account for more than 40% of the various types of failures, causing considerable unpredicted breakdowns of a plant. Thus, this thesis intends to develop a cost-effective and reliable wireless measurement system for rolling bearing condition monitoring. Based on the investigation of various wireless protocols, Zigbee has been taken as a the most promising candidate for establishing the wireless condition monitoring system as it can have an acceptable bandwidth at low power consumption. However, a comparison made between wired and wireless measurement system has found that the Zigbee based wireless measurement system is deficient in streaming long continuous data of raw vibration signals from typical application environment with inevitable ambient interference. As a result, data loss can happen from time to time. To solve this issue, an on-board processing scheme is proposed by implementing advanced signal processing algorithms on the sensor side and only transmitting the processed results with a much smaller data size via the wireless sensor network. On this basis, a wireless sensor node prototype based on the state-of-the-art Cortex-M4F is designed to embed customizable signal processing algorithms. As an extensively employed algorithm for bearing fault diagnosis, envelope analysis is chosen as the on-board signal processing algorithm. Therefore, the procedure of envelope analysis and considerations for implementing it on a memory limited embedded processor are discussed in detail. With the optimization, an automatic data acquisition mechanism is achieved, which combines Timer, ADC and DMA to reduce the interference of CPU and thus to improve the efficiency for intensive computation. A 2048-point envelope analysis in single floating point format is realized on the processor with only 32kB memory. Experimental evaluation results show that the embedded envelope analysis algorithm can successfully diagnose the simulated bearing faults and the data transmission throughput can be reduced by at least 95% per frame compared with that of the raw data; this allows a large number of sensor nodes to be deployed in the network for real time monitoring. Furthermore, a computation efficient amplitude based optimal band selection algorithm is proposed for choosing an optimal band-pass filter for envelope analysis. Requiring only a small number of arithmetical operations, it can be embedded on the wireless sensor node to yield the desired performance of bearing fault detection and diagnosis.
21
Alshimmeri, Fiasael. „Diagnosis of low-speed bearing degradation using acoustic emission techniques“. Thesis, Cranfield University, 2017. http://dspace.lib.cranfield.ac.uk/handle/1826/12324.
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It is widely acknowledged that bearing failures are the primary reason for
breakdowns in rotating machinery. These failures are extremely costly,
particularly in terms of lost production. Roller bearings are widely used in
industrial machinery and need to be maintained in good condition to ensure the
continuing efficiency, effectiveness, and profitability of the production process.
The research presented here is an investigation of the use of acoustic emission
(AE) to monitor bearing conditions at low speeds.
Many machines, particularly large, expensive machines operate at speeds below
100 rpm, and such machines are important to the industry. However, the
overwhelming proportion of studies have investigated the use of AE techniques
for condition monitoring of higher-speed machines (typically several hundred
rpm, or even higher). Few researchers have investigated the application of these
techniques to low-speed machines (<100 rpm), This PhD addressed this
omission and has established which, of the available, AE techniques are suitable
for the detection of incipient faults and measurement of fault growth in low-speed
bearings.
The first objective of this research program was to assess the applicability of AE
techniques to monitor low-speed bearings. It was found that the measured
statistical parameters successfully monitored bearing conditions at low speeds
(10-100 rpm).
The second objective was to identify which commonly used statistical parameters
derived from the AE signal (RMS, kurtosis, amplitude and counts) could identify
the onset of a fault in either race. It was found that the change in AE amplitude
and AE RMS could identify the presence of a small fault seeded into either the
inner or the outer races. However, the severe attenuation of the signal from the
inner race meant that, while AE amplitude and RMS could readily identify the
incipient fault, kurtosis and the AE counts could not. Thus, more attention needs
to be given to analysing the signal from the inner race. The third objective was to identify a measure that would assess the degree of
severity of the fault. However, once the defect was established, it was found that
of the parameters used only AE RMS was sensitive to defect size.
The fourth objective was to assess whether the AE signal is able to detect defects
located at either the centre or edge of the outer race of a bearing rotating at low
speeds. It is found that all the measured AE parameters had higher values when
the defect was seeded in the middle of the outer race, possibly due to the shorter
path traversed by the signal between source and sensor which gave a lower
attenuation than when the defect was on the edge of the outer race. Moreover,
AE can detect the defect at both locations, which confirmed the applicability of
the AE to monitor the defects at any location on the outer race.
22
Lindsay, Tara Reeves. „Applying Adaptive Prognostics to Rolling Element Bearings“. Thesis, Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7568.
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Rolling element bearing failure can cause problems for industries ranging from mild inconveniences such as simple replacement to catastrophic damage such as large production-line equipment failure. Rolling element bearing failure has plagued industries for many years. Bearings are currently monitored to determine whether or not there is a defect in the bearing, but the remaining lifetime of the bearing remains unknown. This research estimates the bearings remaining lifetime through digital signal processing in conjunction with a modified version of Pariss equationa fatigue-failure equation well known in rotating machinery prognostics.
An energy quantity, coined the Power Spectrum Value (PSV), is the maximum amplitude of the frequencies within a relatively small band around the resonant frequency of the system. The current PSV is estimated and updated using a chronologically weighted least squares algorithm. It is this PSV which is implemented in the modified Paris equation to determine the remaining lifetime of the bearing. This research presents a non-intrusive method of determining the lifetime of the bearing so that the bearings utility is maximized and reactive maintenance procedures are minimized.
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Shrestha, Dilesh Raj. „Bearing condition monitoring : An investigation on the possibility of monitoring aging of the lubricating grease by means of acoustic emission and temperature“. Thesis, Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-87220.
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Grease is among the most widely used lubricants in rolling element bearings. Proper understanding of the effect of lubrication due to grease aging can provide a significant increase in the life of the engineering systems. However, at present, there is no sufficient understanding of the grease aging effect in rolling elements bearing. This restricts the optimal usage of the bearing and timely monitoring of the grease. The current research work tries to address this issue with an experimental investigation. This project studies the behavior of 4 types of greases in rolling elements bearings for various operating conditions by recording the temperature and acoustic emission data. The aged samples were prepared to keep in the oven at 150 °C for a series of time duration letting it go through the chemical changes and thermal degradation. Tests were carried out in a test rig with the different levels of oxidized greases for 5 hrs time. And the effects in bearing temperature, acoustic emission were recorded. This is an investigation to analyze the effects of grease composition and aging in rolling elements lubrication by means of acoustic emission and bearing temperature. The IR spectroscopy was carried from the samples collected from the oven in order to understand the change in lubricant composition. The results show that the grease with di-urea thickener and base oil of synthetic ether and polyolester gives the best bearing temperature and acoustic emission behavior compared to the other grease type. The possibility of using the acoustic emission and temperature data to monitor the grease aging is also presented. Along with this, the possibility of using the AE statistical methods, AE count method, and energy plot were also explored to relate with the degree of aging.
24
Craig, Mark. „Advanced condition monitoring to predict rolling element bearing wear using multiple in-line and off-line sensing“. Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/185079/.
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SHI, Juanjuan. „Morphology-based Fault Feature Extraction and Resampling-free Fault Identification Techniques for Rolling Element Bearing Condition Monitoring“. Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33422.
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As the failure of a bearing could cause cascading breakdowns of the mechanical system and then lead to costly repairs and production delays, bearing condition monitoring has received much attention for decades. One of the primary methods for this purpose is based on the analysis of vibration signal measured by accelerometers because such data are information-rich.
The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault-generated impulses, interferences from other machine components, and background noise, where fault-induced impulses are further modulated by various low frequency signal contents. The compounded effects of interferences, background noise and the combined modulation effects make it difficult to detect bearing faults. This is further complicated by the nonstationary nature of vibration signals due to speed variations in some cases, such as the bearings in a wind turbine. As such, the main challenges in the vibration-based bearing monitoring are how to address the modulation, noise, interference, and nonstationarity matters. Over the past few decades, considerable research activities have been carried out to deal with the first three issues. Recently, the nonstationarity matter has also attracted strong interests from both industry and academic community. Nevertheless, the existing techniques still have problems (deficiencies) as listed below:
(1) The existing enveloping methods for bearing fault feature extraction are often adversely affected by multiple interferences. To eliminate the effect of interferences, the prefiltering is required, which is often parameter-dependent and knowledge-demanding. The selection of proper filter parameters is challenging and even more so in a time-varying environment.
(2) Even though filters are properly designed, they are of little use in handling in-band noise and interferences which are also barriers for bearing fault detection, particularly for incipient bearing faults with weak signatures.
(3) Conventional approaches for bearing fault detection under constant speed are no longer applicable to the variable speed case because such speed fluctuations may cause “smearing” of the discrete frequencies in the frequency representation. Most current methods for rotating machinery condition monitoring under time-varying speed require signal resampling based on the shaft rotating frequency. For the bearing case, the shaft rotating frequency is, however, often unavailable as it is coupled with the instantaneous fault characteristic frequency (IFCF) by a fault characteristic coefficient (FCC) which cannot be determined without knowing the fault type. Additionally, the effectiveness of resampling-based methods is largely dependent on the accuracy of resampling procedure which, even if reliable, can complicate the entire fault detection process substantially.
(4) Time-frequency analysis (TFA) has proved to be a powerful tool in analyzing nonstationary signal and moreover does not require resampling for bearing fault identification. However, the diffusion of time-frequency representation (TFR) along time and frequency axes caused by lack of energy concentration would handicap the application of the TFA. In fact, the reported TFA applications in bearing fault diagnosis are still very limited.
To address the first two aforementioned problems, i.e., (1) and (2), for constant speed cases, two morphology-based methods are proposed to extract bearing fault feature without prefiltering. Another two methods are developed to specifically handle the remaining problems for the bearing fault detection under time-varying speed conditions. These methods are itemized as follows:
(1) An efficient enveloping method based on signal Fractal Dimension (FD) for bearing fault feature extraction without prefiltering,
(2) A signal decomposition technique based on oscillatory behaviors for noise reduction and interferences removal (including in-band ones),
(3) A prefiltering-free and resampling-free approach for bearing fault diagnosis under variable speed condition via the joint application of FD-based envelope demodulation and generalized demodulation (GD), and
(4) A combined dual-demodulation transform (DDT) and synchrosqueezing approach for TFR energy concentration level enhancement and bearing fault identification.
With respect to constant speed cases, the FD-based enveloping method, where a short time Fractal dimension (STFD) transform is proposed, can suppress interferences and highlight the fault-induced impulsive signature by transforming the vibration signal into a STFD representation. Its effectiveness, however, deteriorates with the increased complexity of the interference frequencies, particularly for multiple interferences with high frequencies. As such, the second method, which isolates fault-induced transients from interferences and noise via oscillatory behavior analysis, is then developed to complement the FD-based enveloping approach. Both methods are independent of frequency information and free from prefiltering, hence eliminating the tedious process for filter parameter specification. The in-band vibration interferences can also be suppressed mainly by the second approach. For the nonstationary cases, a prefiltering-free and resampling-free strategy is developed via the joint application of STFD and GD, from which a resampling-free order spectrum can be derived. This order spectrum can effectively reveal not only the existence of a fault but also its location. However, the success of this method relies largely on an effective enveloping technique. To address this matter and at the same time to exploit the advantages of TFA in nonstationary signal analysis, a TFA technique, involving dual demodulations and an iterative process, is developed and innovatively applied to bearing fault identification.
The proposed methods have been validated using both simulation and experimental data collected in our lab. The test results have shown that the first two methods can effectively extract fault signatures, remove the interferences (including in-band ones) without prefiltering, and detect fault types from vibration signals for constant speed cases. The last two have shown to be effective in detecting faults and discern fault types from vibration data collected under variable speed conditions without resampling and prefiltering.
26
Moodie, Craig Alexander Simpson. „An investigation into the condition monitoring of large slow speed slew bearings“. Access electronically, 2009. http://ro.uow.edu.au/theses/3035.
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27
Williams, Tracy Denise. „Remote condition monitoring of rolling element bearings with natural crack development“. Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/17243.
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28
Van, der Merwe Nicolaas Theodor. „The application of signal processing and artificial intelligence techniques in the condition monitoring of rotating machinery / Nicolaas Theodor van der Merwe“. Thesis, North-West University, 2003. http://hdl.handle.net/10394/68.
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Condition monitoring of critical machinery has many economic benefits. The primary
objective is to detect faults, for example on rolling element bearings, at an early stage to
take corrective action prior to the catastrophic failure of a component. In this context, it is
important to be able to discriminate between stable and deteriorating fault conditions. A
number of conventional vibration analysis techniques exist by which certain faults in
rotating machinery may be identified. However, under circumstances involving multiple
fault conditions conventional condition monitoring techniques may fail, e.g. by indicating
deteriorating fault conditions for stable fault situations or vice versa. Condition monitoring
of rotating machinery that may have multiple, possibly simultaneous, fault conditions is
investigated in this thesis. Different combinations of interacting fault conditions are
studied both through experimental methods and simulated models. Novel signal
processing techniques (such as cepstral analysis and equidistant Fourier transforms) and
pattern recognition techniques (based on the nearest neighbour algorithm) are applied to
vibration problems of this nature. A set of signal processing and pattern recognition
techniques is developed for the detection of small incipient mechanical faults in the
presence of noise and dynamic load (imbalance). In the case investigated the dynamic
loading consisted of varying degrees of imbalance. It is demonstrated that the proposed
techniques may be applied successfully to the detection of multiple fault conditions.Thesis (Ph.D. (Electronical Engineering))--North-West University, Potchefstroom Campus, 2004.
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Yang, Da-Ming. „Development of novel intelligent condition monitoring procedures for rolling element bearings“. Thesis, University of Aberdeen, 2001. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU151909.
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The primary aim of this thesis is to develop a novel procedure for an intelligent automatic diagnostic condition monitoring system for rolling element bearings. The applicability of this procedure is demonstrated by its implementation in a particular electric motor drive system. The novel bearing condition diagnostic procedure developed involves three stages combining the merits of advanced signal processing techniques, feature extraction methods and artificial neural networks. This procedure is the effective combination of these techniques and methods in a holistic approach to the rolling element bearing problem which provides the novelty in this thesis. Maintenance costs account for an extremely large proportion of the operating costs of machinery. In addition, machine breakdowns and consequent downtime can severely affect the productivity of factories and the safety of products. It is therefore becoming increasingly important for industries to monitor their equipment systematically in order to reduce the number of breakdowns and to avoid unnecessary costs and delays caused by repair. The rolling element bearing is an extremely widespread component in industrial rotating machinery and a large number of problems arise from faulty bearings. Therefore, proper monitoring of bearing condition is highly cost-effective in reducing operating cost. The advanced signal processing techniques used here are bispectral-based and wavelet-based analyses. The bispectral-based procedures examined are the bis-pectrum, the bicoherence, the bispectrum diagonal slice, the bicoherence diagonal slice, the summed bispectrum and the summed bicoherence. The wavelet-based procedure uses the Morlet wavelet. These methods greatly enhance the ability of an automated diagnostic process by linking the increased capability for signal analysis to the predictive capability of artificial neural networks. The bearing monitoring scheme based on bispectral analysis is shown to provide greater insight into the structure of bearing vibration signals and to offer more diagnostic information than conventional power spectral analysis. The wavelet analysis provides a multi-resolution, time-frequency approach to extract information from the bearing vibration signatures. In order to effectively interpret the wavelet map, the time-frequency domain is used instead of the time-scale domain by plotting the associated time trace and power spectrum.
30
Ayandokun, O. K. „The incremental motion encoder : a sensor for the integrated condition monitoring of rotating machinery“. Thesis, Nottingham Trent University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245075.
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Preuss, Jason Lee. „Design and analysis of a composite flywheel preload loss test rig“. Thesis, Texas A&M University, 2004. http://hdl.handle.net/1969.1/100.
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Flywheel energy storage units have become a viable alternative to electrochemical batteries in applications such as satellites, uninterrupted power supplies, and hybrid vehicles. However, this performance is contingent upon safe operation since these flywheels can release their stored energy almost instantaneously upon failure. The research presented here investigates a health monitoring technology that may give an early indication of degraded material properties in a concentric ring composite flywheel. The existence of degraded material properties is manifested as a change in mass eccentricity due to asymmetric growth of the outermost flywheel ring. A test rig concept to investigate the technology is developed in detail using a systems engineering design process. Successful detection of the change in mass eccentricity was verified analytically through dynamic modeling of the flywheel rotor and magnetic suspension system. During steady state operation detection was determined to be feasible via measurements of the magnetic bearing currents and shaft position provided by the magnetic suspension feedback sensors. A rotordynamic analysis was also conducted and predicts successful operation to the maximum operating speed of 50,000 Rpm.
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Khan, A. F. „Condition monitoring of rolling element bearings : a comparative study of vibration-based techniques“. Thesis, University of Nottingham, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292225.
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33
Salvan, Sebastien M. E. „A cost-effective approach to the condition monitoring of multiple rolling-element bearings“. Thesis, Loughborough University, 2004. https://dspace.lboro.ac.uk/2134/34754.
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This thesis describes a methodology to monitor the condition of rolling-element bearings rotating inside an industrial machine. More precisely, the machine looked at is a mail processing machine which has to sort mail for Royal Mail continuously over many hours every day (twenty-two hours per day on average). For such utilisation, machine availability is critical hence the necessity to monitor the condition of every rolling-element bearing during the production time. This would enable maintenance action to be taken on a rolling-element bearing detected as damaged before its critical failure which would cause the machine to stop (safe mode).
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Hassin, Osama A. A. „Condition monitoring of journal bearings for predictive maintenance management based on high frequency vibration analysis“. Thesis, University of Huddersfield, 2017. http://eprints.hud.ac.uk/id/eprint/34161/.
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Journal bearings are widely used as rotor supports in many machinery systems such as engines, motors, turbines and huge pumps. The journal bearing is simply designed, highly efficient, has a long life, low cost and doesn’t fail easily. Based on preventive maintenance strategies, many monitoring techniques are developed for monitoring journal bearings such as lubricant analysis, vibration analysis, noise and acoustic emission analysis. Vibration monitoring techniques have been developed and it can be implemented online or offline without interrupting the machine operations. The vibration phenomena in a journal bearing is complicated which combined between different types of signals created by different sources. To understand this phenomenon, a vibration model is established for fault diagnosis, which includes not only conventional hydrodynamic forces but also excitations of both asperity collisions and churns. However, mis-operations and oil degradation in the journal bearings might cause unexpected and sudden failure which is risky in machines and operators. Consequently, clustering technique is used to investigate into vibration responses of journal bearings for identifying different lubrication regimes as categorised by the classic Stribeck curve. High frequency clustering allows different lubricant oils and different lubrication regimes to be identified appropriately, providing feasible ways for online monitoring of bearing conditions. Additionally, modulation signal bispectrum magnitude results represent the nonlinear vibration responses with two distinctive bifrequency patterns corresponding to instable lubrication and asperity interactions. Using entropy measures, these instable operating conditions are classified to be the low loads cases. Furthermore, average MSB magnitudes are used to differentiate the asperity interactions between asperity collisions and the asperity churns. In addition, the oil starvation of a journal bearing has been found by MSB analysis that the instable frequency can affect the measured vibration responses. Moreover, the structural resonances in the high frequency range can better reflect the separation of different oil levels under wide operating conditions. Finally, As a result of worn bearings, shaft fluctuation increases and asperity collisions decreases. Thus a worn bearing is not all the time good because of instability.
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Zhou, Wei. „Incipient Bearing Fault Detection for Electric Machines Using Stator Current Noise Cancellation“. Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19706.
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The objective of this research is to develop a bearing fault detection scheme for electric machines via stator current. A new method, called the stator current noise cancellation method, is proposed to separate bearing fault-related components in the stator current. This method is based on the concept of viewing all bearing-unrelated components as noise and defining the bearing detection problem as a low signal-to-noise ratio (SNR) problem. In this method, a noise cancellation algorithm based on Wiener filtering is employed to solve the problem. Furthermore, a statistical method is proposed to process the data of noise-cancelled stator current, which enables bearing conditions to be evaluated solely based on stator current measurements. A detailed theoretical analysis of the proposed methods is presented. Several online tests are also performed in this research to validate the proposed methods. It is shown in this work that a bearing fault can be detected by measuring the variation of the RMS of noise-cancelled stator current by using statistical methods such as the Statistical Process Control. In contrast to most existing current monitoring techniques, the detection methods proposed in this research are designed to detect generalized-roughness bearing faults. In addition, the information about machine parameters and bearing dimensions are not required in the implementation.
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Popara, Nikola. „Využití umělé inteligence k monitorování stavu obráběcího stroje“. Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-444960.
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This thesis is focus on monitoring state of machine parts that are under the most stress. Type of artificial intelligence used in this work is recurrent neural network and its modifications. Chosen type of neural network was used because of the sequential character of used data. This thesis is solving three problems. In first problem algorithm is trying to determine state of mill tool wear using recurrent neural network. Used method for monitoring state is indirect. Second Problem was focused on detecting fault of a bearing and classifying it to specific category. In third problem RNN is used to predict RUL of monitored bearing.
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Leggat, Brad. „The development of a rule based expert system to automate the digital analysis of condition monitoring parameters captured on rolling element bearings subjected to simulated failure“. Master's thesis, University of Cape Town, 1991. http://hdl.handle.net/11427/22497.
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This synopsis provides a brief summary of the development of a rule based expert system to diagnose bearing failure. Firstly it covers the proposal of a generic, expert system based industrial condition monitoring system. It then discusses in more detail the development of a specific aspect ofthe system, viz. the analysis of rolling element bearing condition. The bearing test rig and data capture system are described, followed by primary research to define the bearing analysis solution space. This includes the use of vibration parameters, measured and derived operating conditions and the bearing running condition. It then explains the development of rulebases for the three analysis tasks of detection, diagnosis and prognosis. Included is a discussion on techniques used to normalise and adjust the vibration parameters to allow analysis under any operating conditions. Finally the synopsis is concluded with a discussion on the performance of the system and contributions made to the developing field of condition monitoring using expert systems.
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KEHLENBACH, JOSUA. „Fault diagnosis of axlebox roller bearings of high speed rail vehicles based on empirical mode decomposition and machine learning“. Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299774.
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Axlebox bearings are one of the most critical components of a rail vehicle with regard to safety. An axlebox bearing that breaks during operation can be dangerous for the passengers and expensive for the operator. In-service failure of axlebox bearings has been the cause of many catastrophic accidents. Thus, it is of utmost importance to predict bearing failures as early as possible. This will increase reliability and safety of the vehicle as well as reduce the vehicle maintenance cost. Monitoring of roller bearings is an active research eld, and many methods have been proposed by other researchers. Many of these methods employ complex algorithms to make the most use of the given measurements. The algorithms often lack interpretability and have high computational costs, making them dicult to employ in an on-board system. This thesis proposes an interpretable and transparent algorithm that predicts bearing damages with high accuracy. Meanwhile, it tries to retain interpretability as much as possible. The algorithm is based on Empirical Mode Decomposition (EMD) and Singular Value Decomposition (SVD). These two techniques extract essential and meaningful information from the axlebox accelerations. The algorithm is benchmarked on two benchmark datasets, and the results are compared to the respective literature. Then the algorithm is employed on the railway axlebox acceleration measurements that were taken on an axlebox test bench available at SWJTU. The proposed algorithm can be extended to incorporate additional measurements of dierent types, e.g. sound or temperature measurements. The incorporation of other types of measurements will improve the performance of the algorithm even further.Axelbox lager är en av de viktigaste komponenterna i ett järnvägsfordon när det berör säkerheten. Ett axelbox lager som havererar under drift kan vara farligt for passagerarna och även dyrt för operatören. Driftfel av lagren har varit orsaken till många katastrofala olyckor. Därför är det av yttersta vikt att förutsäga lagerfel så tidigt som möjligt. Detta ökar fordonets tillförlitlighet och säkerhet samt minskar underhållskostnaderna. Mycket forskning har utförts inom övervakning av rullager. Många metoder använder komplexa algoritmer för att maximalt utnyttja matningarna. Algoritmerna saknar ofta tolkbarhet och har höga beräkningskostnader, vilket gör dem svåra att använda i ett integrerat system. Denna avhandling kombinerar era metoder för databehandling och maskininlärning till en algoritm som kan förutsäga lagerskador med hög precision, samtidigt som tolkningsförmågan bibehalls. Bland andra välkända metoder sa använder algoritmen Empirical Mode Decomposition (EMD) och Singular Value Decomposition (SVD) för att extrahera väsentlig information for vibrationsmätningarna. Algoritmen testas sedan med tre olika vibrationsdatamängder, varav en mättes specikt med tanke på simulering av axelbox lager. Ett annat mål med algoritmen är att göra den tillämpad för ytterligare mätningar. Det bör vara möjligt att inkludera mätningar av olika slag, dvs ljud- eller temperaturmätningar, och därigenom förbättra resultaten. Detta skulle minska implementeringskostnaden avsevärt eftersom befintliga sensorer används för detta ändamål. I händelsen av att de föreslagna metoderna inte fungerar med nya mätningar är det även möjligt att integrera ytterligare funktioner i algoritmen.
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Bruand, Guillaume. „Surveillance préventive des roulements par analyse multi-capteurs“. Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT113.
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La surveillance préventive est une approche courante permettant de réduire les coûts associés à la maintenance en milieu industriel. En effet, un diagnostic précoce peut prévenir des dommages critiques sur une machine donnée, et permet à l'utilisateur de planifier la maintenance afin de minimiser le temps d'immobilisation du moyen de production. Dans cette thèse il est montré que les capteurs d'angle sont particulièrement adaptés au diagnostic des machines tournantes, et plus spécifiquement à la détection des défauts de roulement. Ils sont combinés de manière avantageuse afin d'étudier l'orbite d'un arbre tournant, celle-ci donnant des informations pertinentes sur l'état de fonctionnement de la machine. Un modèle mécanique original est proposé afin de décrire les déplacements d'un arbre en rotation en présence d'un roulement défectueux. Des caractéristiques sont extraites de l'orbite étudiée, et utilisées comme un indicateur de sévérité de défaillance. Les résultats montrent que la méthode proposée a de multiples avantages sur les approches plus conventionnelles, telles que celles basées sur les accéléromètres, et représente ainsi une alternative intéressante dans un contexte industrielCondition monitoring is a common approach to reduce costs in industrial environments. Indeed, an early diagnosis of an incoming failure can prevent a machine from serious damages, and allows the customer to plan maintenance operations to minimize the downtime of the production apparatus. In this thesis angle measurement sensors are shown to be particularly suited to rotating machines diagnosis, and especially to bearing fault detection. They are combined advantageously to study the orbit of a rotating shaft, from which useful information about the health state of the machine can be retrieved. An original mechanical model is proposed to describe the rotating shaft displacements in the presence of a faulty bearing. Features are derived from the studied orbit, and are then used as a fault severity indicator. Results show that the proposed method has multiple advantages over more conventional ones, such as accelerometer-based approaches, and thus is an appealing alternative in an industrial context
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Samouhos, Stephen V. (Stephen Vincent) 1982. „Building condition monitoring“. Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61611.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 201-206).
The building sector of the United States currently consumes over 40% of the United States primary energy supply. Estimates suggest that between 5 and 30% of any building's annual energy consumption is unknowingly wasted due to pathologically malfunctioning lighting and comfort conditioning systems. This thesis is focused on developing analytical methods embodied within useful software tools to quickly identify and evaluate those building system faults that cause large building energy inefficiencies. The technical contributions of this work include expert rules that adapt to HVAC equipment scale and operation, a general framework for applying probabilistic inference to HVAC fault detection and evaluation, and methods for sorting fault signals according to userdefined interests such as annual cost of energy inefficiencies. These contributions are particularly unique in their treatment of model and measurement uncertainty within the fault inference, and the careful consideration of user interests in fault evaluation. As a first step to developing this general framework for fault detection, I targeted first order faults such as simultaneous heating and cooling and imbalanced air flows within several large air-handling units in three buildings on the MIT campus. Experiments included the purposeful implementation of mechanical and software control programming faults on otherwise fault-free equipment. Between the five pieces of equipment, the software system successfully identified all previously known and experimentally implemented faults, as well as additional faults that had not been previously identified or imposed during the experiment. User testing and experiments show that embracing uncertainty within HVAC fault detection and evaluation is not only paramount to judicious fault inference but it is also central to gaining the trust and buy-in of system users who ultimately can apply fault detection information to actually fix and improve building operations.
by Stephen Samouhos.
Ph.D.
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Danielson, Hugo, und Schmuck Benjamin von. „Robot Condition Monitoring : A first step in Condition Monitoring for robotic applications“. Thesis, Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-66011.
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The industrial world is in constant demand for faster, cheaper and higher quality manufacturing. Robot utilisation and automation has evolved to become a necessary asset to master in order to stay competitive in the global market. With the growing dependency on robots, unexpected downtime and brakedowns can cause devastating loss of revenue. Consequently, this has lead to an increased importance for an accurate condition based way of performing robotic maintenance. As of writing, robots are predominantly maintained through time dependent maintenance. Part replacement is based on statistical models where maintenance is performed without taking the actual robot condition into consideration. As a result an overall level of uncertainty is ensued, where lacking the ability to properly diagnose the robot, also leads to superfluous repairs. Because of the costly impact this has on production, a condition based maintenance approach to robots would yield increased reliability at a lower cost of maintenance. This research focuses on trying to monitor vibrations in a robot, so as to infer about wear and to provide a first step in vibration based Robot Condition Monitoring. This research has been of multidisciplinary nature where robotics, tribology, mechanical component, signal analysis and diagnosis theory have overlapped in several areas throughout the project. The research has provided a vibration baseline and trends of the theoretical bearing defect frequencies for a hypocycloid gearbox installed on an ABB IRB6600 robot. The gearbox was not worn to a level that a severe gearbox degradation was irrefutably detectable and analysable. Accelerometers normally used on wind turbines were used for the project, and are believed to be sufficiently successful in capturing bearing related signals to accredit it for continued use at the preliminary stages of Robot Condition Monitoring development. A worn RV410F hypocycloid gearbox, was dismantled and analysed. Bearings found inside indicate high degrees of moisture corrosion and extensive surface wear. These findings had decisive roles in what future work recommendations where presented. Areas with great potential are condition monitoring through the use of Acoustic Emission and lubrication analysis. Further recommendations include investigating signal analysis techniques such as cepstrum pre-whitening and discrete wavelet transforms.
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Blakeley, Bruce. „Audio plant condition monitoring“. Thesis, Swansea University, 2001. https://cronfa.swan.ac.uk/Record/cronfa42239.
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Accelerometers are widely used in plant condition monitoring (PCM) to diagnose faults in rotating machinery. This can be expensive, and is typically only used to monitor the condition of critical machines. The objective of this project is to develop a system, using microphones, that could screen less critical machines for faults. Microphones are non-contact sensors that can be placed away from the machines, to avoid damage. If the data gathered by the microphone is reduced to a single parameter, that increases with wear, then analysis would be greatly simplified. This system could be used to provide basic PCM screening for equipment not considered important enough for routine vibration monitoring. To achieve this objective, a test-rig was designed and constructed, consisting of a motor, gearbox and load. Various faults were introduced into the test-rig, and a microphone used to record the sound. These results were then compared to accelerometer readings. Time synchronous averaging (TSA) was employed to increase the signal to noise ratio. It was proven that Kurtosis and crestfactor of a microphone signal both increase, if used with a high pass filter, when an impacting fault such as a broken gearbox tooth was introduced into the test-rig. It proved harder to reduce the sound of other non-impacting faults, such as misalignment, into a single parameter. The technique was tested in an industrial environment with a 100 dB background noise level. It was shown that the technique was capable of detecting faults even with a signal to noise ratio of -15 dB. A one dimensional FEA model was created, with six degrees of freedom, modelling the test-rig's vibrational behaviour. This was used to investigate the affect of a broken tooth, and to explain the increase in noise as the tooth passing frequency coincided with a resonance.
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Shen, Chia-Hsuan. „Acoustic Based Condition Monitoring“. University of Akron / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=akron1341797408.
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Li, Yawei. „Dynamic prognostics of rolling element bearing condition“. Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15847.
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Birch, David James. „Impulsive vibration measurement for rolling bearing condition assessment“. Thesis, University of Manchester, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635645.
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Rolling element bearings, an essential part of virtually all rotating machinery, fail unpredictably. Systems to measure bearing condition are therefore essential to avoid the financial and human consequences of bearing failure. An impulsive vibration condition monitoring technique known as shock pulse measurement has been used commercially for over thirty years. Its success is well documented yet it is based purely on empirical evidence. No attempt to understand the theory behind it was academically published until the work of Ho (1998). This research extends Ho's work to better understand the determination of rolling element bearing condition from the measurement of impulsive vibration. A rolling element bearing continually produces small impulsive shocks due to the interaction of roughness asperities on the rolling surfaces. A theoretical, stochastic model has been developed that accurately predicts the pattern of such pulses. It predicts patterns produced by various lubricant film thickness in good condition bearings, and those that will be produced by bearings with damage. It can consequently predict the deterioration of a bearing as film thickness decreases through to the onset of damage. A major contribution to the understanding of shock pulse measurement has been made in recognising that the asperity shock pulse patterns are different to the pattern of enveloped pulses produced by the resonant transducers and instrumentation typically used to measure them. Theory has been developed to successfully explain this difference in terms of asperity pulse shape. Appropriate compensation has been included in the model to adjust its output for the various shapes of pulse produced by different asperity interactions. Experiments covering a practical range of bearing sizes and operating conditions, for both bearings in good condition and bearings with damage, have been conducted to validate the model. Low cost, portable instrumentation and resonant transducers have been designed and manufactured to obtain the shock measurements. These devices are capable of being developed into cost effective commercial products with a minimum of additional effort. A better understanding has been gained of the characteristics of piezoelectric transducers when used for shock pulse measurement. This understanding allows transducers to be optimally designed for a given application. A rig and procedure have been developed to obtain the calibration values for resonant transducers used for impulsive vibration measurement. This calibration technique takes into account the pulse shape theory and can be developed into a commercial calibration process with a minimum of effort. Collaboration with a world leading manufacturer of tapered roller bearings, including full access to their existing resources, was a major contribution to the success of this research.
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Sæther, Jørgen Hagemo. „Choke condition and performance monitoring“. Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for marin teknikk, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11623.
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Sand production is a common complex problem in the oil and gas industry, and choke valves is typically suffering for this in form of erosive damage. The degree of erosive damage is decided by many different factors where the flow rate velocity and the sand rate are the most important ones. Much effort has been spent on ways of reducing the choke erosion to be able to maintain the oil and gas production at an optimal level with attention to increased profit, safety and availability. Use of Computational Fluid Dynamics (CFD) has been essential in this work by simulating flow through the choke valve for optimizing the choke design, choosing the optimal erosion resistant material, coming up with improved erosion-related models, and optimal operational procedures of the choke. Producing with Acceptable Sand Rate (ASR), which means allowing a certain degree of sand erosion in chokes, have proven to be a successful way of maintaining the oil and gas production at an optimal level. To satisfy ASR-production, demands are made on an optimal use of condition and performance monitoring equipment and tools. The use of the condition and performance monitoring tool INSIGHT (from ABB) has in general proven to be successful for satisfying the ASR-production on different Statoil fields, including Statfjord which is in this thesis the area of focus regarding the use of INSIGHT. Important condition monitoring data such as sand rate, flow rate and pressure necessary to say something about the choke erosion status in INSIGHT must be as good as possible, because the quality of the results are limited by quality of the input data. In this thesis, INSIGHT has been presented, discussed and tested to be able to come up with possible limitations and improvements with special attention to condition monitoring (input) data used in INSIGHT.
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Horch, Alexander. „Condition Monitoring of Control Loops“. Doctoral thesis, Stockholm : Tekniska högsk, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3050.
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Sandberg, Erik. „Condition monitoring in steel industry“. Thesis, University of Manchester, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.489505.
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This thesis presents investigations about the applicability of different multivariate statistical methods (OLS, PCA, PCR, PLS) for condition monitoring of steel industry processes (electric arc furnaces and blast furnaces). The work has been focused on three main areas; condition monitoring of blast furnaces (BF), charge material mix optimisation for electric arc furnaces (EAF) and batch-monitoring of EAFs.
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Macintyre, John. „Condition monitoring and neural networks“. Thesis, University of Sunderland, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297129.
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Brown, S. A. „Condition monitoring using stable isotopes“. Thesis, Imperial College London, 1987. http://hdl.handle.net/10044/1/38246.
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