Relevant bibliographies by topics / Faults of rotating machines

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Faults of rotating machines'

Author: Grafiati
Published: 28 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Faults of rotating machines"

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Mogal, S. P., and D. I. Lalwani. "A Brief Review on Fault Diagnosis of Rotating Machineries." Applied Mechanics and Materials 541-542 (March 2014): 635–40. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.635.

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Vibration in any rotating machines is due to faults like misalignment, unbalance, crack, mechanical looseness etc. Identification of these faults in rotor systems, model and vibration signal based methods are used. Signal processing techniques such as Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Wigner-Ville Distribution (WVD) and Wavelet Transform (WT) are applied to vibration data for faults identification. The intent of the paper is to present a review and summarize the recent research and developments performed in condition monitoring of rotor system with the purpose of rotor faults detection. In present paper discuss the different signal processing techniques applied for fault diagnosis. Vibration response measurement has given information concerning any fault within a rotating machine and many of the methods utilizing this technique are reviewed. A detail review of the subject of fault diagnosis in rotating machinery is presented.
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Sinha, Jyoti K. "Quantification of Faults in Rotating Machines." Noise & Vibration Worldwide 38, no. 9 (October 2007): 20–29. http://dx.doi.org/10.1260/095745607782689836.

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Over some decades, vibration based condition monitoring has become well accepted and widely used identifying faults in for rotating machines. However the quantification of faults may require a number of experiments to be carried out, which can be time consuming and exorbitant, if not impossible by experiments alone. Experience shows that the combined approach (Experiment and Analysis often Finite Element Analysis) is efficient in quantifying the fault in a much quicker and reliable manner. A few case studies are discussed here to bring out the usefulness of the combined approach.
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Kovaleski, J. L., A. A. Susin, M. Negreiros, and R. F. M. Marcal. "Detecting faults in rotating machines." IEEE Instrumentation & Measurement Magazine 3, no. 4 (2000): 24–26. http://dx.doi.org/10.1109/5289.887456.

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Espinoza-Sepulveda, Natalia, and Jyoti Sinha. "Mathematical Validation of Experimentally Optimised Parameters Used in a Vibration-Based Machine-Learning Model for Fault Diagnosis in Rotating Machines." Machines 9, no. 8 (August 7, 2021): 155. http://dx.doi.org/10.3390/machines9080155.

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Mathematical models have been widely used in the study of rotating machines. Their application in dynamics has eased further research since they can avoid time-consuming and exorbitant experimental processes to simulate different faults. The earlier vibration-based machine-learning (VML) model for fault diagnosis in rotating machines was developed by optimising the vibration-based parameters from experimental data on a rig. Therefore, a mathematical model based on the finite-element (FE) method is created for the experimental rig, to simulate several rotor-related faults. The generated vibration responses in the FE model are then used to validate the earlier developed fault diagnosis model and the optimised parameters. The obtained results suggest the correctness of the selected parameters to characterise the dynamics of the machine to identify faults. These promising results provide the possibility of implementing the VML model in real industrial systems.
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Altaf, S., M. S. Mehmood, and M. W. Soomro. "Advancement of Fault Diagnosis and Detection Process in Industrial Machine Environment." Journal of Engineering Sciences 6, no. 2 (2019): d1—d8. http://dx.doi.org/10.21272/jes.2019.6(2).d1.

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Machine fault diagnosis is a very important topic in industrial systems and deserves further consideration in view of the growing complexity and performance requirements of modern machinery. Currently, manufacturing companies and researchers are making a great attempt to implement efficient fault diagnosis tools. The signal processing is a key step for the machine condition monitoring in complex industrial rotating electrical machines. A number of signal processing techniques have been reported from last two decades conventionally and effectively applied on different rotating machines. Induction motor is the one of widely used in various industrial applications due to small size, low cost and operation with existing power supply. Faults and failure of the induction machine in industry can be the cause of loss of throughput and significant financial losses. As compared with the other faults with the broken rotor bar, it has significant importance because of severity which leads to a serious breakdown of motor. Detection of rotor failure has become significant fault but difficult task in machine fault diagnosis. The aim of this paper is indented to summarizes the fault diagnosis techniques with the purpose of the broken rotor bar fault detection. Keywords: machine fault diagnosis, signal processing technique, induction motor, condition monitoring.
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Walker, Ryan, Sureshkumar Perinpanayagam, and Ian K. Jennions. "Rotordynamic Faults: Recent Advances in Diagnosis and Prognosis." International Journal of Rotating Machinery 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/856865.

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Diagnosis and condition monitoring in rotating machinery has been a subject of intense research for the last century. Recent developments indicate the drive towards integration of diagnosis and prognosis algorithms in future integrated vehicle health management (IVHM) systems. With this in mind, this paper concentrates on highlighting some of the latest research on common faults in rotating machines. Eight key faults have been described; the selected faults include unbalance, misalignment, rub/looseness, fluid-induced instability, bearing failure, shaft cracks, blade cracks, and shaft bow. Each of these faults has been detailed with regard to sensors, fault identification techniques, localization, prognosis, and modeling. The intent of the paper is to highlight the latest technologies pioneering the drive towards next-generation IVHM systems for rotating machinery.
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Tvorić, Stjepan, Miroslav Petrinić, Ante Elez, and Mario Brčić. "STATIC ECCENTRICITY FAULT DETECTION METHOD FOR ELECTRICAL ROTATING MACHINES BASED ON THE MAGNETIC FIELD ANALYSIS IN THE AIR GAP BY MEASURING COILS." Journal of Energy - Energija 69, no. 4 (December 30, 2020): 3–7. http://dx.doi.org/10.37798/202069451.

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Electrical rotating machines have a great economic significance as they enable conversion of energy between mechanical and electrical state. Reliability and operation safety of these machines can be greatly improved by implementation of continuous condition monitoring and supervisory systems. Especially important feature of such systems is the ability of early fault detection. For this reason, several methods for detection and diagnosis of the machine faults have been developed and designed. As fault detection methods can largely differ in the types of detectable faults, machine adoptability and price of the system, a novel method was developed that can be used for cost-effective detection of various faults of electrical machine. Machine fault detection technique presented in this paper is based on the measurement of magnetic field in the air gap. Numerous studies have proven that crucial information about the machine condition can be determined based on measurement and analysis of the magnetic field in the air gap. It has also been confirmed that analysis of the air gap magnetic field can be used to detect, diagnose and recognize various electrical faults in their very early stage. Proposed method of positioning and installation of the measuring coils on ferromagnetic core parts within the air gap region of the machine enables differentiation of various faults. Furthermore, different faults can be detected if measuring coils are placed on the stator teeth then when placed on the rotor side. The paper presents method on how to analyse and process the measured voltages acquired from measuring coils placed within the machine, especially in the case of rotor static eccentricity detection. The methodology is explained by means of finite element method (FEM) calculations and verified by measurements that were performed on the induction machine. FEM calculation model was used to predict measurement coil output of the induction motor for healthy and various faulty states (at different amounts of static eccentricity). These results were then confirmed by measurements performed in the laboratory on the induction traction motor that was specially modified to enable measurements of faulty operation states of the machine. Measurements comprised of several machine fault conditions broken one rotor bar, broken multiple rotor bars, broken rotor end ring and various levels of rotor static eccentricity. Other methods used for faults detection are primarily based on the monitoring of quantities such as current and vibration and their harmonic analysis. This new system is based on the tracing the changes of induced voltage of the measuring coils installed on the stator teeth. Faults can be detected and differentiated based on RMS value of these voltages and the number of voltage spikes of voltage waveform i.e. without the need of harmonic analyses. If these coils are installed on the rotor it is possible to detect the stator winding faults in a similar manner.
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Jiang, Xiaomo, Fumin Wang, Haixin Zhao, Shengli Xu, and Lin Lin. "Novel Orbit-based CNN Model for Automatic Fault Identification of Rotating Machines." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 7. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1147.

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Various faults in high-fidelity turbomachinery such as steam turbines and centrifugal compressors usually result in unplanned outage thus lowering the reliability and productivity while largely increasing the maintenance costs. Condition monitoring has been increasingly applied to provide early alerting on component faults by using the vibration signals. However, each type of fault in different types of rotating machines usually require an individual model to isolate the damage for accurate condition monitoring, which require costly computation efforts and resources due to the data uncertainties and modeling complexity. This paper presents a generalized deep learning methodology for accurately automatic diagnostics of various faults in general rotating machines by utilizing the shaft orbits generated from vibration signals, considering the high non-linearity and uncertainty of the sensed vibration signals. The sensor anomalies and environmental noise in the vibration signals are first addressed through waveform compensation and Bayesian wavelet noise reduction filtering. Shaft orbit images are generated from the cleansed vibration data collected from different turbomachinery with various fault modes. A multi-layer convolutional neural network model is then developed to classify and identify the shaft orbit images of each fault. Finally, the fault diagnosis of rotating machinery is realized through the automated identification process. The proposed approach retains the fault information in the axis trajectory to the greatest extent, and can adeptly extract and accurately identify features of various faults. The effectiveness and feasibility of the proposed methodology is demonstrated by using the sensed vibration signals collected from real-world centrifugal compressors and steam turbines with different fault modes.
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Luwei, Kenisuomo C., Akilu Yunusa-Kaltungo, and Yusuf A. Sha’aban. "Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks." Machines 6, no. 4 (November 20, 2018): 59. http://dx.doi.org/10.3390/machines6040059.

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The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines.
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Yunusa-kaltungo, Akilu, and Jyoti K. Sinha. "Effective vibration-based condition monitoring (eVCM) of rotating machines." Journal of Quality in Maintenance Engineering 23, no. 3 (August 14, 2017): 279–96. http://dx.doi.org/10.1108/jqme-08-2016-0036.

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Purpose The purpose of this paper is mainly to highlight how a simplified and streamlined approach to the condition monitoring (CM) of industrial rotating machines through the application of frequency domain data combination can effectively enhance the eMaintenance framework. Design/methodology/approach The paper commences by providing an overview to the relevance of maintenance excellence within manufacturing industries, with particular emphasis on the roles that rotating machines CM of rotating machines plays. It then proceeds to provide details of the eMaintenance as well as its possible alignment with the introduced concept of effective vibration-based condition monitoring (eVCM) of rotating machines. The subsequent sections of the paper respectively deal with explanations of data combination approaches, experimental setups used to generate vibration data and the theory of eVCM. Findings This paper investigates how a simplified vibration-based rotating machinery faults classification method based on frequency domain data combination can increase the feasibility and practicality of eMaintenance. Research limitations/implications The eVCM approach is based on classifying data acquired under several experimentally simulated conditions on two different machines using combined higher order signal processing parameters so as to reduce CM data requirements. Although the current study was solely based on the application of vibration data acquired from rotating machines, the knowledge exchange platform that currently dominates present day scientific research makes it very likely that the lessons learned from the development of eVCM concept can be easily transferred to other scientific domains that involve continuous CM such as medicine. Practical implications The concept of eMaintenance as a cost-effective and smart means of increasing the autonomy of maintenance activities within industries is rapidly growing in maintenance-related literatures. As viable as the concept appears, the achievement of its optimum objectives and full deployment to the industry is still subjective due to the complexity and data intensiveness of conventional CM practices. In this paper, an eVCM approach is proposed so that rotating machine faults can be effectively detected and classified without the need for repetitive analysis of measured data. Social implications The main strength of eVCM lies in the fact that it permits the sharing of historical vibration data between identical rotating machines irrespective of their foundation structures and speed differences. Since eMaintenance is concerned with driving maintenance excellence, eVCM can potentially contribute towards its optimisation as it cost-effectively streamlines faults diagnosis. This therefore implies that the simplification of vibration-based CM of rotating machines positively impacts the society with regard to the possibility of reducing how much time is actually spent on the accurate detection and classification of faults. Originality/value Although the currently existing body of literature already contains studies that have attempted to show how the combination of measured vibration data from several industrial machines can be used to establish a universal vibration-based faults diagnosis benchmark for incorporation into eMaintenance framework, these studies are limited in the scope of faults, severity and rotational speeds considered. In the current study, the concept of multi-faults, multi-sensor, multi-speed and multi-rotating machine data combination approach using frequency domain data fusion and principal components analysis is presented so that faults diagnosis features for identical rotating machines with different foundations can be shared between industrial plants. Hence, the value of the current study particularly lies in the fact that it significantly highlights a new dimension through which the practical implementation and operation of eMaintenance can be realized using big data management and data combination approaches.
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Dissertations / Theses on the topic "Faults of rotating machines"

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Walker, Ryan. "Localising imbalance faults in rotating machinery." Thesis, Cranfield University, 2013. http://dspace.lib.cranfield.ac.uk/handle/1826/8606.

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This thesis presents a novel method of locating imbalance faults in rotating machinery through the study of bearing nonlinearities. Localisation in this work is presented as determining which discs/segments of a complex machine are affected with an imbalance fault. The novel method enables accurate localisation to be achieved using a single accelerometer, and is valid for both sub and super-critical machine operations in the presence of misalignment and rub faults. The development of the novel system for imbalance localisation has been driven by the desire for improved maintenance procedures, along with the increased requirement for Integrated Vehicle Health Management (IVHM) systems for rotating machinery in industry. Imbalance faults are of particular interest to aircraft engine manufacturers such as Rolls Royce plc, where such faults still result in undesired downtime of machinery. Existing methods of imbalance localisation have yet to see widespread implementation in IVHM and Engine Health Monitoring (EHM) systems, providing the motivation for undertaking this project. The imbalance localisation system described has been developed primarily for a lab-based Machine Fault Simulator (MFS), with validation and verification performed on two additional test rigs. Physics based simulations have been used in order to develop and validate the system. An Artificial Neural Network (ANN) has been applied for the purposes of reasoning, using nonlinear features in the frequency domain originating from bearing nonlinearities. The system has been widely tested in a range of situations, including in the presence of misalignment and rub faults and on a full scale aircraft engine model. The novel system for imbalance localisation has been used as the basis for a methodology aimed at localising common faults in future IVHM systems, with the aim of communicating the results and findings of this research for the benefit of future research. The works contained herein therefore contribute to scientific knowledge in the field of IVHM for rotating machinery.
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Gubran, Ahmed. "Vibration diagnosis of blades of rotating machines." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/vibration-diagnosis-of-blades-of-rotating-machines(40f1d466-b393-42f6-a65a-e16801f06920).html.

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Rotating blades are considered to be the one of the most common cause of failures in rotating machinery. Blade failure modes normally occur as a result of cracks due to unexpected operating conditions, which are normally caused by accidents of foreign objects damage, high cycle fatigue, blade rubbing, blade root looseness, and degradation from erosion and corrosion. Thus, detection of blade faults has an important role in reducing blade related failures and allowing repairs to be scheduled for the machinery. This in turn will lead to reduction in maintenance costs and thus raise productivity and safety aspects of operation. To maintain vital components of rotating machines, such as blades, shafts, bearings and gear boxes, at optimal levels, detection of failures in such components is important, because this will prevent any serious damage that could affect performance. This research study involves laboratory tests on a small rig with a bladed disc rotor that applied vibration measurements and analysis for blade fault detection. Three measurements: shaft torsional vibration, on-bearing vibration (OBV) and on-casing vibration (OCV), are used. A small test rig of a single stage bladed disc holding 8-blades was designed and manufactured, to carry out this research study to assess the usefulness and capability of each vibration technique in detection of incipient defects within machine blades. A series of tests was conducted on a test rig for three different cases of blade health conditions: (a) healthy blade(s) with mistuned effects, (b) blade root looseness and (c) cracks in a blade on two different blade sizes (long and short blades) in order to discover changes in blades' dynamic behaviour during the machine running-up operation. The data were collected using the three measurements during machine run-up and then recorded. The measured vibration data were analysed by computing the blades' resonance at different engine orders (EOs) related to the blade(s) resonance frequencies and their higher harmonics, to understand the blade(s) dynamics behaviour for the cases of healthy and faulty blade(s). Data have been further processed using a polar plot presentation method which provides clear results that can be used for monitoring blade integrity. To validate the obtained experimental results, a simplified mathematical model was also developed. Finally, a comparative study between three methods was undertaken to understand the relative advantages and limitations in the blade heath monitoring.
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Elnady, Maged Elsaid. "On-shaft vibration measurement using a MEMS accelerometer for faults diagnosis in rotating machines." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/onshaft-vibration-measurement-using-a-mems-accelerometer-for-faults-diagnosis-in-rotating-machines(cf9b9848-972d-49ff-a6b0-97bef1ad0e93).html.

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The healthy condition of a rotating machine leads to safe and cheap operation of almost all industrial facilities and mechanical systems. To achieve such a goal, vibration-based condition monitoring has proved to be a well-accepted technique that detects incipient fault symptoms. The conventional way of On-Bearing Vibration Measurement (OBVM) captures symptoms of different faults, however, it requires a relatively expensive setup, an additional space for the auxiliary devices and cabling in addition to an experienced analyst. On-Shaft Vibration Measurement (OSVM) is an emerging method proposed to offer more reliable Faults Diagnosis (FD) tools with less number of sensors, minimal processing time and lower system and maintenance costs. The advancement in sensor and wireless communications technologies enables attaching a MEMS accelerometer with a miniaturised wireless data acquisition unit directly to the rotor without altering the machine dynamics. In this study, OSVM is analysed during constant speed and run-up operations of a test rig. The observations showed response modulation, hence, a Finite Element (FE) analysis has been carried out to help interpret the experimental observations. The FE analysis confirmed that the modulation is due to the rotary motion of the on-shaft sensor. A demodulation method has been developed to solve this problem. The FD capability of OSVM has been compared to that of OBVM using conventional analysis where the former provided more efficient diagnosis with less number of sensors. To incorporate more features, a method has been developed to diagnose faults based on Principal Component Analysis and Nearest Neighbour classifier. Furthermore, the method is enhanced using Linear Discriminant Analysis to do the diagnosis without the need for a classifier. Another faults diagnosis method has been developed that ensures the generalisation of extracted faults features from OSVM data of a specific machine to similar machines mounted on different foundations.
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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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Obaid, Ramzy R. "Detection of rotating mechanical asymmetries in small induction machines." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/13527.

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D'Elia, Gianluca <1980&gt. "Fault detection in rotating machines by vibration signal processing techniques." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/952/.

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Machines with moving parts give rise to vibrations and consequently noise. The setting up and the status of each machine yield to a peculiar vibration signature. Therefore, a change in the vibration signature, due to a change in the machine state, can be used to detect incipient defects before they become critical. This is the goal of condition monitoring, in which the informations obtained from a machine signature are used in order to detect faults at an early stage. There are a large number of signal processing techniques that can be used in order to extract interesting information from a measured vibration signal. This study seeks to detect rotating machine defects using a range of techniques including synchronous time averaging, Hilbert transform-based demodulation, continuous wavelet transform, Wigner-Ville distribution and spectral correlation density function. The detection and the diagnostic capability of these techniques are discussed and compared on the basis of experimental results concerning gear tooth faults, i.e. fatigue crack at the tooth root and tooth spalls of different sizes, as well as assembly faults in diesel engine. Moreover, the sensitivity to fault severity is assessed by the application of these signal processing techniques to gear tooth faults of different sizes.
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Johnson, David Ewen. "Use of noise for the detection of gear faults in rotating machinery." Thesis, University of the West of England, Bristol, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358312.

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Carlson, David K. "Artificicial [i.e. Artificial] neural networks and their applications in diagnostics of incipient faults in rotating machinery." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/28000.

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Wang, Xian Bo. "A novel fault detection and diagnosis framework for rotating machinery using advanced signal processing techniques and ensemble extreme learning machines." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3951596.

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Martínek, Marek. "Tvorba SW pro generování signálu simulující závady rotačních systémů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-442837.

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This diploma thesis deals with the design and creation of an algorithm for generating simulated signal data from a vibration diagnostics device. The first part is focused on theoretical acquaintance with vibration diagnostics and characteristics of individual defects of rotary machines. The next part deals with the possibilities of mathematical and kinematic simulations using a computer software. The main part of this work is dedicated to design and creation of software for generating simulated signal data. In the last part, the principle of simulation of specific defects of rotary machines is clearly demonstrated.
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Books on the topic "Faults of rotating machines"

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Le Doeuff, René, and Mohamed El Hadi Zaïm. Rotating Electrical Machines. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118620649.

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Design of rotating electrical machines. Chichester, West Sussex, United Kingdom: Wiley, 2008.

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Electrical transformers and rotating machines. Albany: Delmar Publishers, 1999.

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Herman, Stephen L. Electrical transformers and rotating machines. 2nd ed. Clifton Park, NY: Thomson Delmar Learning, 2005.

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1937-, Jokinen Tapani, and Hrabovcová Valeria, eds. Design of rotating electrical machines. Chichester, West Sussex, United Kingdom: Wiley, 2014.

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Stone, Greg. Electrical Insulation for Rotating Machines. New York: John Wiley & Sons, Ltd., 2004.

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Pyrhönen, Juha, Tapani Jokinen, and Valéria Hrabovcová. Design of Rotating Electrical Machines. Chichester, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118701591.

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Lang, Jeffrey, ed. Multi-Wafer Rotating MEMS Machines. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-77747-4.

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Stone, Greg C., Ian Culbert, Edward A. Boulter, and Hussein Dhirani. Electrical Insulation for Rotating Machines. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118886663.

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Electrical transformers and rotating machines. 3rd ed. Clifton Park, NY: Delmar, Cengage Learning, 2012.

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Book chapters on the topic "Faults of rotating machines"

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Xu, Min, Chu-Zhen Xu, Jin-Yuan Shi, and Heng-Tao Zhang. "The Happening Possibilities of Main Vibration Faults Coming from More Than 160 Example Cases in Rmispp (Rotating Machines in Steam Power Plants)." In Diagnostics of Rotating Machines in Power Plants, 177–85. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-2706-3_13.

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Yunusa-Kaltungo, Akilu, Jyoti K. Sinha, and Keri Elbhbah. "HOS Analysis of Measured Vibration Data on Rotating Machines with Different Simulated Faults." In Lecture Notes in Mechanical Engineering, 81–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39348-8_6.

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Nakhaeinejad, Mohsen, Jaewon Choi, and Michael D. Bryant. "Model-Based Diagnostics and Fault Assessment of Induction Motors with Incipient Faults." In Rotating Machinery, Structural Health Monitoring, Shock and Vibration, Volume 5, 439–49. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9428-8_37.

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Xingwei, Jiang, and Wang Shujian. "Intellectualization of faults diagnosis of rotating machinery — Expert System." In Condition Monitoring and Diagnostic Engineering Management, 281–86. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0431-6_43.

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Benati, A., C. Frigeri, and G. C. Silvestri. "Vibration Monitoring and Mechanical Fault Diagnostics on Large Rotating Machinery in ENEL Power Plants." In Diagnostics of Rotating Machines in Power Plants, 261–78. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-2706-3_18.

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Adamsab, Khadersab. "Numerical Investigation of Forcing Function for Rotating Machinery Bearing Faults." In Lecture Notes in Mechanical Engineering, 687–94. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4795-3_62.

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Lim, Gang-Min, Younus Ali, and Bo-Suk Yang. "The Fault Diagnosis and Monitoring of Rotating Machines by Thermography." In Engineering Asset Management and Infrastructure Sustainability, 557–65. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-493-7_43.

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Xue, Hongtao, Huaqing Wang, Liuyang Song, and Peng Chen. "Structural Fault Diagnosis of Rotating Machinery Based on Distinctive Frequency Components and Support Vector Machines." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 341–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25944-9_44.

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Giantomassi, Andrea, Francesco Ferracuti, Sabrina Iarlori, Gianluca Ippoliti, and Sauro Longhi. "Signal Based Fault Detection and Diagnosis for Rotating Electrical Machines: Issues and Solutions." In Complex System Modelling and Control Through Intelligent Soft Computations, 275–309. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12883-2_10.

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Dien, Nguyen Phong, and Nguyen Trong Du. "Fault Detection for Rotating Machines in Non-stationary Operations Using Order Tracking and Cepstrum." In Advances in Engineering Research and Application, 349–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37497-6_41.

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Conference papers on the topic "Faults of rotating machines"

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Walker, R. B., S. Perinpanayagam, and I. K. Jennions. "Localizing Unbalance Faults in Rotating Machinery." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94455.

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Excessive levels of unbalance in rotating machinery continue to contribute to machine downtime and unscheduled and costly maintenance actions. Whilst unbalance as a rotordynamic fault has been studied in great detail during the last century, the localization of unbalance within a complex rotating machine is today often performed in practice using little more than ‘rules of thumb’. In this work, localizing excessive unbalance has been studied from an experimental perspective through the use of two rotordynamic test rigs fitted with multiple disks. Sub-synchronous non-linear features in the frequency domain have been identified and studied as a method of aiding the localization of unbalance faults, particularly in situations where sensor placement options are limited. The results of the study are discussed from the perspective of next-generation Integrated Vehicle Health Management (IVHM) systems for rotating machines.
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Parlos, Alexander G., Kyusung Kim, and Raj M. Bharadwaj. "Sensorless Early Detection of Mechanical Faults: Developments in Smart Rotating Machines." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21750.

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Abstract Practical early fault detection and diagnosis systems must exhibit high level of detection accuracy and while exhibiting acceptably low false alarm rates. Such designs must have applicability to a large class of machines, require installation of no additional sensors, and require minimal detailed information regarding the specific machine design. Electromechanical systems, such as electric motors driving dynamic loads like pumps and compressors, often develop incipient failures that result in downtime. There is a large number of such failure modes, with a large majority being of mechanical nature. The precise signatures of these failure modes depend on numerous machine-specific factors, including variations in the electric power supply and driven load. In this paper the development and experimental demonstration of a sensorless, detection and diagnosis system is presented for incipient machine faults. The developed fault detection and diagnosis system uses recent developments in dynamic recurrent neural networks in implementing an empirical model-based approach, and multi-resolution signal processing for extracting fault information from transient signals. The signals used by the system are only the multi-phase motor current and voltage sensors, whereas the transient mechanical speed is estimated from these measurements using a recently developed speed filter. The effectiveness of the fault diagnosis system is demonstrated by detecting stator, rotor and bearing failures at early stages of development and during different levels of deterioration. Experimental test results from small machines, 2.2 kW, and large machines, 373 kW and 597 kW, are presented demonstrating the effectiveness of the proposed approach. Furthermore, the ability of the diagnosis system to discriminate between false alarms and actual incipient failure conditions is demonstrated.
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Elbhbah, Keri, and Jyoti K. Sinha. "Bispectrum: A Tool for Distinguishing Different Faults in Rotating Machine." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-68010.

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The current state-of-the-art in vibration-based condition monitoring of rotating machines requires a number of vibration transducers at each bearing pedestal of a rotating machine to identify any faults, in the machine. In this paper, the use of the bispectrum has been proposed for fault diagnosis in rotating machines. The reason for this is that it may reduce the number of vibration transducers at each bearing pedestal in rotating machines in the future. The paper presents a comparison of the bispectrum results for four cases, namely; Healthy, Misaligned shaft, Crack Shaft and Shaft Rub on an experimental rig consisting of two rigidly coupled shafts supported through 4 ball bearings. Only one accelerometer has been used for this purpose at each bearing and the initial results observed are encouraging.
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Yunusa-Kaltungo, Akilu, and Jyoti K. Sinha. "Faults Diagnosis in Rotating Machines Using Higher Order Spectra." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-25090.

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Higher order spectra (HOS) and higher order coherences (HOC) are two classes of higher order signal processing tools that have gained recent attention in the area of rotating machines’ condition monitoring (CM). Hence, the current study compares and presents the results of the performances of both HOS and HOC in the diagnosis of rotating machines’ faults, through the numerically simulated vibration signals and the experimentally measured vibration response on a rotating rig with healthy condition and the rotor with a transverse crack.
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Sinha, Jyoti K. "Integrated Approach for Vibration-Based Condition Monitoring of Rotating Machines." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47122.

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Conventional Vibration-based Condition Monitoring (VCM) is well known and well accepted in industries to identify the fault(s), if any, in rotating machine since decades. However over the last 3 decades, significant advancement in both computational and instrumentation technologies has been noticed which resulted in number of research studies to find the alternate and efficient methods for fault(s) diagnosis. But most of the research studies may not be leading to an Integrated Modern VCM (IMVCM). It may be because of mainly 2 reasons; (a) the recent proposed methods in the literature are based on numerically simulated studies and a very limited experimental studies and (b) none of the recent studies applied on all kind of faults. In this paper, a summary of a couple of methods proposed and published earlier by author to meet the requirement of the IMVCM is presented.
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Suhacˇ, Blazˇ, Jozˇe Vizˇintin, Pavle Bosˇkoski, and Dani Juricˇic´. "Development of an Intelligent Rotating Machinery Diagnostics Programme." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59190.

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Rotating machines are one of the most wide spread items of equimpnet in the industrial plants; hence the reliable operation is of great practical importance. Analyses show that when a run-to-failure philosophy is adopted in rotating machinery maintenance, their downtime is usually three to four times longer comparing to a periodic or proactive maintenance approach. A successful proactive maintenance program requires an integration of several diagnostic procedures into an intelligent data processing system. Such a system allows detection of a broad range of faults in an early stage. The main aim of this paper is to present current results of our development of an intelligent rotating machinery diagnostics program for detecting a broad range of faults from signals which can be measured non-destructively and on-line. The main motivation is to develop computationally efficient algorithm that can be implemented on a standard (low-cost) platform. In that respect we have developed a test rotating machine equipped with accelerometers, temperature sensors and sensors for lubricating oil characterization. In this paper we focus on gear-box faults and a feature extraction procedure based on non-parametric statistical concepts as suggested and demonstrated on experimental data.
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Vilchis-Rodriguez, Damian S., Sinisa Djurović, and Alexander C. Smith. "Sensitivity Assessment of Wound Rotor Induction Generator Bearing Fault Detection Using Machine Electrical Quantities." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94586.

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This paper investigates the sensitivity of machine electrical quantities when employed as a means of bearing fault detection in wound rotor induction generators. Bearing failure is the most common failure mode in rotating AC machinery. With the widespread use of wound rotor induction machines in modern wind power generation, achieving effective detection of bearing faults in these machines is becoming increasingly important in order to minimize wind turbine maintenance related downtime. Current signature analysis has been demonstrated to be an effective technique for achieving detection of different fault types in ac machines. However, this technique lacks sensitivity when used for detection of bearing failures and therefore sophisticated post processing techniques have recently been suggested to improve its performance. As an alternative, this paper investigates the sensitivity of a range of machine electrical quantities to bearing faults, with the aim of examining the possibility of achieving improved bearing fault detection based on identifying a clear fault spectral signature. The reported signatures can be subjected potentially to refined processing techniques to further improve fault detection.
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Yang, Tachung, and Ming-Wei Hsu. "An Efficient Diagnosis Technique for Variations of Shaft-Bow and Unbalance." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86711.

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This paper focuses on the diagnosis of two types of faults, unbalance and shaft-bow, which are the common faults occurring in rotating machines. An efficient fault diagnostic method is proposed to identify the locations and amounts of faults based on the vibration responses of the rotor without prior knowledge of the numbers and locations of the faults. The proposed method achieves fast diagnosis by orderly searching the possible locations of faults, avoiding exploring all possible combinations of faults, and fully utilizing previous search results. Based on the unique search strategy, the proposed method is suitable for on-line monitoring and diagnosis of the gradual variations of shaft-bow and unbalance of large rotating machinery of fixedly rotating speeds, like steam turbine-generators of power plants.
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Vania, A., P. Pennacchi, and S. Chatterton. "Effects of the Shaft Normal Modes on the Model-Based Identification of Unbalances in Rotating Machines." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-46184.

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Model-based methods can be applied to identify the most likely faults that cause the experimental response of a rotating machine. Sometimes, the objective function, to be minimized in the fault identification method, shows multiple sufficiently low values that are associated with different sets of the equivalent excitations by means of which the fault can be modeled. In these cases, the knowledge of the contribution of each normal mode of interest to the vibration predicted at each measurement point can provide useful information to identify the actual fault. In this paper, the capabilities of an original diagnostic strategy that combines the use of common fault identification methods with innovative techniques based on a modal representation of the dynamic behavior of rotating machines is shown. This investigation approach has been successfully validated by means of the analysis of the abnormal vibrations of a large power unit.
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Vania, A., P. Pennacchi, and S. Chatterton. "Dynamic Effects Caused by the Non-Linear Behavior of Oil-Film Journal Bearings in Rotating Machines." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-69457.

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Many common faults and malfunctions in rotating machines mainly cause synchronous vibrations (1X). Very high 1X vibration levels can occur, in case of severe faults. Large journal orbits inside oil-film journal bearings may generate non-linear effects in the oil-film forces, whose presence can be detected by means of the appearance of not negligible super-synchronous vibrations of the shafts. In this paper, a model-based method has been used to study the effects of non-linear oil-film forces on the machine dynamic behavior that may occur during runups and rundowns. In general, it is possible to suppose that the importance of the non-linear behavior of oil-film journal bearings, and then the level of the super-synchronous vibrations, increases with the amplitude of the 1X vibrations caused by the primary fault. However, the numerical results of this study and the experimental evidences found in the monitoring data of a real machine have shown that the super-synchronous harmonic components of the oil-film forces may excite resonances of the shaft-train causing unexpected amplifications of the super-synchronous vibrations. This may make difficult the recognition of the presence of non-linear effects in the machine dynamic behavior and the identification of the actual cause of abnormal vibrations.
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Reports on the topic "Faults of rotating machines"

1

Case, Scott W., and Kenneth L. Reifsnider. Advanced Composite Performance: Material Behavior and Life Cycle Prediction for Rotating Machines. Fort Belvoir, VA: Defense Technical Information Center, March 2003. http://dx.doi.org/10.21236/ada414609.

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