Добірка наукової літератури з теми "Defective bearings"
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Статті в журналах з теми "Defective bearings"
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Bogdevičius, Marijonas, and Viktor Skrickij. "Investigation of Dynamic Processes in Ball Bearings with Defects." Solid State Phenomena 198 (March 2013): 651–56. http://dx.doi.org/10.4028/www.scientific.net/ssp.198.651.
Повний текст джерелаАнотація:
The paper considers the dynamics of ball bearings with defects. A mathematical model of a ball bearing with defects is offered. The performed theoretical and experimental investigations of ball bearings with defects are described. Five cases of various defects are investigated, including the defective outer race, the defective inner race, the defective rolling element, the defective inner and outer races, the rolling element and a separator, the worn-out ball bearing.
2
Larizza, Francesco, Alireza Moazen-Ahmadi, Carl Q. Howard, and Steven Grainger. "The importance of bearing stiffness and load when estimating the size of a defect in a rolling element bearing." Structural Health Monitoring 18, no. 5-6 (October 25, 2018): 1527–42. http://dx.doi.org/10.1177/1475921718808805.
Повний текст джерелаАнотація:
The change in the static stiffness of a bearing assembly is an important discriminator when determining the size of a defect in a rolling element bearing. In this article, the force–displacement relationships for defective bearings under various static radial loadings at various cage angular positions are analytically estimated and experimentally measured and analyzed. The study shows that the applied load has a significant effect on the static stiffness variations in defective rolling element bearings. The experimental measurements of the effect of the defect size on the varying stiffness of the bearing assembly, which has not been shown previously, provides valuable knowledge for developing methods to distinguish between defective bearings with defects that are smaller or larger than one angular ball spacing. The methods and results presented here contribute to the wider experimental investigation of the effects of loadings on the varying static stiffness of defective bearings and its effects on the measured vibration signatures. A large data set was obtained and has been made publicly available.
3
Tong, Van-Canh, and Seong-Wook Hong. "Study on the stiffness and fatigue life of tapered roller bearings with roller diameter error." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 231, no. 2 (August 5, 2016): 176–88. http://dx.doi.org/10.1177/1350650116649889.
Повний текст джерелаАнотація:
Geometric imperfection is a common problem in manufacturing of rolling element bearings. In particular, roller geometric errors frequently occur because a large number of rollers with relatively small size are engaged in a bearing. However, computational tools for rolling bearing characteristics take into account ideal bearings without any geometric error. In this study, the stiffness and fatigue life of tapered roller bearings were investigated with consideration for the effects of roller diameter errors possibly induced during manufacturing process. To this end, a general model for tapered roller bearings having rollers with diameter error (or defective rollers) was developed that can reflect the time-varying stiffness due to the roller error effects. The effects of the number of defective rollers, error magnitude, and position of defective rollers on the stiffness and fatigue life were investigated. Computational results showed that even small roller diameter errors appreciably alter the tapered roller bearings internal load distributions and therefore the stiffness and fatigue life of tapered roller bearings.
4
Kong, Fanzhao, Wentao Huang, Yunchuan Jiang, Weijie Wang, and Xuezeng Zhao. "Research on effect of damping variation on vibration response of defective bearings." Advances in Mechanical Engineering 11, no. 3 (March 2019): 168781401982773. http://dx.doi.org/10.1177/1687814019827733.
Повний текст джерелаАнотація:
This article presents a dynamic model of ball bearings with a localised defect on the outer raceway to analyse the effect of damping variation on the vibration response of defective bearings. First, a dynamic model is built based on the Hertzian contact theory, the interaction between the bearing inner ring, outer ring, and rolling element; the effect of damping and stiffness is considered; and the vibration equation of the bearing system is solved by the fourth-order Runge–Kutta algorithm. Then, the damping ratios of the experimental bearings using different types of viscosity lubricating grease are measured and compared with the damping ratios of the dynamic model; in addition, the viscous damping coefficient of the experimental bearings are calculated. Finally, the numerical analysis and experimental results show that the grease with a different level of viscosity affects the vibration signal of the defective bearing.
5
Zuber, Ninoslav, and Dragan Cvetkovic. "Rolling Element Bearings Fault Identification in Rotating Machines, Existing Methods of Vibration Signal Processing Techniques and Practical Considerations." Applied Mechanics and Materials 430 (September 2013): 70–77. http://dx.doi.org/10.4028/www.scientific.net/amm.430.70.
Повний текст джерелаАнотація:
This paper addresses the suitability of vibration monitoring and analysis techniques to detect different types of defects in roller element bearings. Processing techniques are demonstrated on signals acquired from the test rig with defective bearings. As a result it is shown that there is no reliable universal method for bearing failure monitoring from its early occurence up to bearings failure. Two real life case studies with different types of bearing failures are presented with practical considerations on methods used for failure identification.
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Hu, Lei, Yuandong Xu, Fengshou Gu, Jing He, Niaoqing Hu, and Andrew Ball. "Autocorrelation Ensemble Average of Larger Amplitude Impact Transients for the Fault Diagnosis of Rolling Element Bearings." Energies 12, no. 24 (December 12, 2019): 4740. http://dx.doi.org/10.3390/en12244740.
Повний текст джерелаАнотація:
Rolling element bearings are one of the critical elements in rotating machinery of energy engineering systems. A defective roller of bearing moves in and out of the load zone during each revolution of the cage. Larger amplitude impact transients (LAITs) are produced when the defective roller passes the load zone centre and the defective area strikes the inner or outer races. A series of LAIT segments with higher signal to noise ratio are separated from a continuous vibration signal according to the bearing geometry and kinematics. In order to eliminate the phase errors between different LAIT segments that can arise from rotational speed fluctuations and roller slippages, unbiased autocorrelation is introduced to align the phases of LAIT segments. The unbiased autocorrelation signals make the ensemble averaging more accurate, and hence, archive enhanced diagnostic signatures, which are denoted as LAIT-AEAs for brevity. The diagnostic method based on LAIT separation and autocorrelation ensemble average (AEA) is evaluated with the datasets captured from real bearings of two different experiment benches. The validation results of the LAIT-AEAs are compared with the squared envelope spectrums (SESs) yielded based on two state-of-the-art techniques of Fast Kurtogram and Autogram.
7
Veselovska, Nataliya, Serhiy Shargorodskiy, Bohdan Bratslavets, and Olha Yalina. "RESEARCH OF FEATURES OF DEVELOPMENT OF BEARING DEFECTS ON THE BASIS OF WAVELET ANALYSIS." ENGINEERING, ENERGY, TRANSPORT AIC, no. 4(111) (December 18, 2020): 5–13. http://dx.doi.org/10.37128/2520-6168-2020-4-1.
Повний текст джерелаАнотація:
Today the vibrodiagnostic method achieves the highest efficiency and manufacturability for the operation of the technical condition of the technological equipment of the agro-industrial complex. At the same time, this method is one of the most modern methods of technical diagnostics, indicating the kinematic warehouses of diagnostic objects. Vibration analysis is a fundamental tool for diagnostic monitoring of bearings. The vibration signal of defective rolling bearings and its spectrum contain characteristic features by which it is possible to fairly correctly identify the type and location of the defect. At the moment the defective element passes through the loaded zone of the rolling bearing, a pronounced peak, an energy impulse, appears in the vibration. Thus, when a bearing with internal defects is operating, characteristic components appear in vibration - harmonics with natural frequencies, the numerical values of which can be calculated using theoretical formulas using the geometric dimensions of the bearing elements and the rotor speed of the mechanism. In a loaded bearing, four characteristic frequencies can be distinguished that are used for diagnostics - the frequency of the outer bearing cage, the frequency of the inner cage, the cage frequency and the rolling element frequencies. The complexity of the analysis of vibration signals of rolling bearings for the purpose of their diagnostics lies in the fact that the signs of a defective bearing are distributed over a wide range of frequencies, have low vibrational energy and are somewhat random in nature. In addition, the vibration signal is, of course, removed from the body of the equipment containing the bearing, and therefore contains not only information useful from the point of view of bearing diagnostics, but also noise - vibrations produced by other parts of the mechanism. The analysis of methods for diagnosing bearing defects based on wavelet analysis of their vibration signals allows us to single out the most promising direction, which consists in the fact that the bearing vibration signal is decomposed into coefficients using wavelet analysis, after which the most significant coefficients are selected from these coefficients.
8
Patel, V. N., N. Tandon, and R. K. Pandey. "Experimental Study for Vibration Behaviors of Locally Defective Deep Groove Ball Bearings under Dynamic Radial Load." Advances in Acoustics and Vibration 2014 (May 18, 2014): 1–7. http://dx.doi.org/10.1155/2014/271346.
Повний текст джерелаАнотація:
Rolling element bearings are used in many mechanical systems at the revolute joints for sustaining the dynamic loads. Thus, the reliable and efficient functioning of such systems critically depends on the good health of the employed rolling bearings. Hence, health monitoring of rolling bearings through their vibration responses is a vital issue. In this paper, an experimental investigation has been reported related to the vibration behaviours of healthy and locally defective deep groove ball bearings operating under dynamic radial load. The dynamic load on the test bearings has been applied using an electromechanical shaker. The vibration spectra of the healthy and defective deep groove ball bearings in time and frequency domains have been compared and discussed. Overall vibration increases in presence of local defects and dynamic radial load.
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Evdokimova, O. V., V. A. Babeshko, O. M. Babeshko, V. S. Evdokimov, M. M. Akinina, Y. B. Eletskiy, and S. B. Uafa. "About the features of resources of defective bearings." Ecological Bulletin of Research Centers of the Black Sea Economic Cooperation 16, no. 2 (June 28, 2019): 15–20. http://dx.doi.org/10.31429/vestnik-16-2-15-20.
Повний текст джерела
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Babeshko, V. A., O. M. Babeshko, O. V. Evdokimova, Yu B. Eletskii, and S. B. Uafa. "Strength Properties of Lubricated Bearings with Defective Coatings." Mechanics of Solids 54, no. 8 (December 2019): 1165–70. http://dx.doi.org/10.3103/s0025654419080065.
Повний текст джерелаДисертації з теми "Defective bearings"
1
Moazen-ahmadi, Alireza. "Vibration signatures of defective bearings and defect size estimation methods." Thesis, 2016. http://hdl.handle.net/2440/112971.
Повний текст джерелаАнотація:
Rolling element bearings are widely used in rotary machinery, often with extremely
demanding performance criteria. The failure of bearings is the most common reason for
machine breakdowns. Machine failures can be catastrophic, resulting in costly downtime
and sometimes in human casualties. The implementation of condition monitoring systems,
which use data from various sources to determine the state of bearings, is commonly used
to predict bearing failure. Hence, a considerable amount of attention has been devoted to
bearing failure modes, fault detection, fault development and life expectations of bearings.
The focus of this research is on the fault detection and defect size estimations of ball and
cylindrical rolling element bearings with outer race defects.
In classic bearing vibration condition monitoring methods, the trend of vibration
amplitudes is often used to determine when a bearing should be replaced. As a defect on
the surface of a bearing raceway enlarges, the changes in the size and shape of the defect
due to successive passes of the rolling elements can result in a fluctuation of the averaged
measured values of the vibration amplitude. As an alternative to studying measures of
vibration severity in order to determine the size of the defect indirectly, the actual
geometric arc length of a bearing defect can be determined from the vibration signal and
used to decide when to replace the bearing. The research in this project provides an insight
into both the stiffness behaviour of a defective bearing assembly, with ball and cylindrical
rolling elements, and the characteristics of the vibration signature in defective bearings in
order to identify the vibration features associated with the entry and exit events of bearing
defect. The ultimate aim of this research is to develop methods to accurately estimate the
size of a defect on the outer raceway of a bearing, which are not dependent on the magnitude of the vibration response, but instead use these features for tracking defect size
in bearings.
In the research conducted here, the vibration excitation of a bearing associated with linespall
defects is studied both experimentally and analytically. An improved nonlinear
dynamic model of the contact forces and vibration responses generated in defective rolling
element bearings is proposed to study the vibration characteristics in defective bearings. It
is demonstrated that previous models are not able to predict these events accurately
without making significant assumptions about the path of rolling elements in the defect
zone. Similar to the results of the analytical modelling, the experimental results show that
there are discrepancies in previous theories describing the path of the rolling elements in
the defect zone that have led to poor results in simulating the vibration response and the
existing defect size estimation methods. The parametric study presented here shows that
the relative angular extents between the entry and exit events on the vibration results
decrease with increasing load. Significant speed dependency of these angular extents is
shown by simulation and experimental measurements of defective bearings as the
operational speed increases. The sources of inaccuracy in the previously proposed defect
size estimation algorithms are identified and explained. A complete defect size estimation
algorithm is proposed that is more accurate and less biased by shaft speed when compared
with existing methods.
A method is presented for calculating and analysing the quasi-static load distribution and
varying stiffness of a bearing assembly with a raceway defect of varying load, depth,
length, and surface roughness. It has been found that as the shaft and rollers in a defective
bearing rotate, it causes the stiffness of the bearing assembly to vary, which cause
parametric vibration excitations of the bearing assembly. It is shown that when the defect size is greater than one angular roller spacing, signal aliasing occurs and the vibration
signature is similar to when the defect size is less than one angular roller spacing. Using
the results from simulations and experimental testing, signal processing techniques are
developed to distinguish defect sizes that are less than or greater than one angular roller
spacing.
The results of this study provide an improved hypothesis for the path of a rolling element
as it travels through a defect and its relationship to the vibration signature in a bearing.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Mechanical Engineering, 2016
Книги з теми "Defective bearings"
1
Sheehy-Skeffington, Jennifer. Decision-Making Up Against the Wall. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190492908.003.0005.
Повний текст джерелаАнотація:
This chapter provides an overview of research on the behavioral dimension of low socioeconomic status and a set of theoretical and empirical principles for better understanding it. In particular, the chapter focuses on those behaviors that are claimed to exacerbate a situation of poverty or deprivation, such as poor academic performance, myopic financial decisions, early child-bearing, consumption of unhealthy foods, and engaging in unhealthy lifestyle habits. Though such behavioral patterns have been used to make claims as to the defective values or motives of the poor, the chapter argues that studying them rigorously, aided by the experimental method, leads to a more nuanced and accurate picture, in which psychology is systematically shaped by socioeconomic position. After reviewing evidence from education, public health, and behavioral economics concerning the behavioral dimension of low socioeconomic status, the chapter suggests an organizing set of mechanisms that might structure a comprehensive explanatory account of it.
Частини книг з теми "Defective bearings"
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Satwara, Neel, and V. N. Patel. "Vibration Monitoring of Defective Shaft Bearing System." In Lecture Notes in Mechanical Engineering, 311–25. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3716-3_24.
Повний текст джерела
2
Fourati, Aroua, Adeline Bourdon, Didier Rémond, Nabih Feki, Fakher Chaari, and Mohamed Haddar. "Current Signal Analysis of an Induction Machine with a Defective Rolling Bearing." In Applied Condition Monitoring, 45–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61927-9_5.
Повний текст джерела
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Liu, Fang, Changqing Shen, Ao Zhang, Fanrang Kong, and Yongbin Liu. "An Adaptive Doppler Effect Reduction Algorithm for Wayside Acoustic Defective Bearing Detector System." In Lecture Notes in Mechanical Engineering, 125–35. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09507-3_13.
Повний текст джерела
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Elasha, Faris, Matthew Greaves, and David Mba. "Diagnostics of a Defective Bearing Within a Planetary Gearbox with Vibration and Acoustic Emission." In Applied Condition Monitoring, 399–412. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20463-5_30.
Повний текст джерела
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Sawalhi, Nader, and Suri Ganeriwala. "Analysis and Signal Processing of a Gearbox Vibration Signal with a Defective Rolling Element Bearing." In Applied Condition Monitoring, 71–85. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20463-5_6.
Повний текст джерела
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Gupta, Pankaj, and M. K. Pradhan. "Fault Detection Through Vibration Signal Analysis of Rolling Element Bearing in Time Domain." In Handbook of Research on Manufacturing Process Modeling and Optimization Strategies, 208–34. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2440-3.ch010.
Повний текст джерелаАнотація:
Mechanical failure prevention and condition monitoring have been one of the concerns of mechanical engineers in recent years due to the personal safety, cost of failure, reliability and downtime issues of equipment. Rotating machines are one of the most important actuators in the industrial applications as well as in every day applications. Rolling element bearings are very critical components of rotating machines and the presence of defects in the bearing may lead to failure of machines. Hence, early identification of such defects along with the severity of damage under operating condition of the bearing may avoid malfunctioning and breakdown of machines. Defective bearings are source of vibration and these vibration signals can be used to assess the faulty bearings. This chapter presents the brief review of recent trends in research on bearing defects, sources of vibration and vibration measurement techniques in time domain, frequency domain and time-frequency domain. Detailed explanation of defect detection through scalar indicators in time domain.
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Endelman, Todd M. "The Social and Political Context of Conversion in Germany and England 1870–1914." In Broadening Jewish History, 95–114. Liverpool University Press, 2010. http://dx.doi.org/10.3828/liverpool/9781904113010.003.0006.
Повний текст джерелаАнотація:
This chapter focuses on Jews who left Judaism in the decades before the First World War and who were not attracted by the spiritual truths or ethical values of Christianity. It discusses disaffiliation as a hallmark of modern Jewish history in the West in which the flow out of Judaism was not equally strong in all countries and among all strata of Jewish society. It also analyses the characteristic patterns of drift and defection that emerged in every country or region bearing the impress of larger social and political conditions. The chapter talks about the temptation to abandon Judaism, which increased from 1870 to 1914, when rising antisemitism called into question Jewish integration into state and society with unprecedented intensity. It refers to England and Germany as states with dissimilar political cultures and social systems, which illuminates the history of the Jewish communities there.
Тези доповідей конференцій з теми "Defective bearings"
1
Montalvo, Joseph, Constantine Tarawneh, Jennifer Lima, Jonas Cuanang, and Nancy De Los Santos. "Estimating the Outer Ring Defect Size and Remaining Service Life of Freight Railcar Bearings Using Vibration Signatures." In 2019 Joint Rail Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/jrc2019-1284.
Повний текст джерелаАнотація:
The railroad industry currently utilizes two wayside detection systems to monitor the health of freight railcar bearings in service: The Trackside Acoustic Detection System (TADS™) and the wayside Hot-Box Detector (HBD). TADS™ uses wayside microphones to detect and alert the conductor of high-risk defects. Many defective bearings may never be detected by TADS™ since a high-risk defect is a spall which spans more than 90% of a bearing’s raceway, and there are less than 20 systems in operation throughout the United States and Canada. Much like the TADS™, the HBD is a device that sits on the side of the rail-tracks and uses a non-contact infrared sensor to determine the temperature of the train bearings as they roll over the detector. These wayside detectors are reactive in the detection of a defective bearing and require emergency stops in order to replace the wheelset containing the defective bearing. These costly and inefficient train stoppages can be prevented if a proper maintenance schedule can be developed at the onset of a defect initiating within the bearing. This proactive approach would allow for railcars with defective bearings to remain in service operation safely until reaching scheduled maintenance. Driven by the need for a proactive bearing condition monitoring system in the rail industry, the University Transportation Center for Railway Safety (UTCRS) research group at the University of Texas Rio Grande Valley (UTRGV) has been developing an advanced onboard condition monitoring system that can accurately and reliably detect the onset of bearing failure using temperature and vibration signatures of a bearing. This system has been validated through rigorous laboratory testing at UTRGV and field testing at the Transportation Technology Center, Inc. (TTCI) in Pueblo, CO. The work presented here builds on previously published work that demonstrates the use of the advanced onboard condition monitoring system to identify defective bearings as well as the correlations developed for spall growth rates of defective bearing outer rings (cups). Hence, the system uses the root-mean-square (RMS) value of the bearing’s acceleration to assess its health. Once the bearing is determined to have a defective outer ring, the RMS value is then used to estimate the defect size. This estimated size is then used to predict the remaining service life of the bearing. The methodology proposed in this paper can prove to be a useful tool in the development of a proactive and cost-efficient maintenance cycle for railcar owners.
2
Donelson, John, and Ronald L. Dicus. "Bearing Defect Detection Using On-Board Accelerometer Measurements." In ASME/IEEE 2002 Joint Rail Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/rtd2002-1645.
Повний текст джерелаАнотація:
Vibration signatures of defective roller bearings on railroad freight cars were analyzed in an effort to develop an algorithm for detecting bearing defects. The effort is part of a project to develop an on-board condition monitoring system for freight trains. The Office of Research and Development of the Federal Railroad Administration (FRA) is sponsoring the project. The measurements were made at the Transportation Technology Center (TTC) in Pueblo, CO on July 26 – 29, 1999 during the Phase III Field Test of the Improved Wayside Freight Car Roller Bearing Inspection Research Program sponsored by FRA and the Association of American Railroads (AAR). Wheel sets with specific roller bearing defects were installed on a test train consisting of 8 freight cars designed to simulate revenue service. The consist also contained non-defective roller bearings. Accelerometers were installed on the inboard side of the bearing adapters to measure the vibration signatures during the test. Signatures of both defective and non-defective bearings were recorded. The data were recorded on Sony Digital Audio Tape (DAT) Recorders sampling at a rate of 48 K samples per second. We used both ordinary and envelope spectral analysis to analyze the data in an effort to detect features that could be related to known defects. The spectra of non-defective bearings show no remarkable features at bearing defect frequencies. In general, the ordinary spectra of defective bearings do not exhibit remarkable features at the bearing defect frequencies. In contrast, the envelope spectra of defective bearings contain a number of highly resolved spectral lines at these frequencies. In several cases the spectral lines could be related to specific bearing defects. Based on the analysis performed to date, the envelope spectrum technique provides a promising method for detecting defects in freight car roller bearings using an on-board condition monitoring system.
3
Lima, Jennifer, Constantine Tarawneh, Jesse Aguilera, and Jonas Cuanang. "Estimating the Inner Ring Defect Size and Residual Service Life of Freight Railcar Bearings Using Vibration Signatures." In 2020 Joint Rail Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/jrc2020-8059.
Повний текст джерелаАнотація:
Abstract There are currently two primary wayside detection systems for monitoring the health of freight railcar bearings in the railroad industry: The Trackside Acoustic Detection System (TADS™) and the wayside Hot-Box Detector (HBD). TADS™ uses wayside microphones to detect and alert the train operator of high-risk defects. However, many defective bearings may never be detected by TADS™ since a high-risk defect is a spall which spans about 90% of a bearing’s raceway, and there are less than 30 systems in operation throughout the United States and Canada. HBDs sit on the side of the rail-tracks and use non-contact infrared sensors to acquire temperatures of bearings as they roll over the detector. These wayside bearing detection systems are reactive in nature and often require emergency stops in order to replace the wheelset containing the identified defective bearing. Train stoppages are inefficient and can be very costly. Unnecessary train stoppages can be avoided if a proper maintenance schedule can be developed at the onset of a defect initiating within the bearing. Using a proactive approach, railcars with defective bearings could be allowed to remain in service operation safely until reaching scheduled maintenance. The University Transportation Center for Railway Safety (UTCRS) research group at the University of Texas Rio Grande Valley (UTRGV) has been working on developing a proactive bearing condition monitoring system which can reliably detect the onset of bearing failure. Unlike wayside detection systems, the onboard condition monitoring system can continuously assess the railcar bearing health and can provide accurate temperature and vibration profiles to alert of defect initiation. This system has been validated through rigorous laboratory testing at UTRGV and field testing at the Transportation Technology Center, Inc. (TTCI) in Pueblo, CO. The work presented here builds on previously published work that demonstrates the use of the onboard condition monitoring system to identify defective bearings as well as the correlations developed for spall growth rates of defective bearing outer rings (cups). The system first uses the root-mean-square (RMS) value of the bearing’s acceleration to assess its health. Then, an analysis of the frequency domain of the acquired vibration signature determines if the bearing has a defective inner ring (cone) and the RMS value is used to estimate the defect size. This estimated size is then used to predict the residual life of the bearing. The methodology proposed in this paper can assist railroads and railcar owners in the development of a proactive and cost-efficient maintenance cycle for their rolling stock.
4
Oliver, Corey, and Lin Lin. "Acoustic Emission Analysis of Defective Radial Ball Bearings for Quality Control." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71498.
Повний текст джерелаАнотація:
The objective of this study is to capture and characterize the acoustic emissions of radial ball bearings in operation. A comparison is made between healthy bearings and defective ones. A precise 0.012” wide groove was cut into either a ball, the inner or outer race of test bearings. To reduce vibrational interference, the test shaft is isolated using a magnetic coupling and is supported by a thrust air bearing. The test bearing is acoustically insulated from its surrounding with mass loaded vinyl and acoustic reflections are dampened by wedge foam to improve the signal quality. The acoustic emissions are captured and digitized using a smartphone. Various signal processing techniques are used to characterize the signals and analyze defect modes. The results provide a comparison between the acoustic emissions of healthy and defective bearings operated at various speeds and provide methods for characterization.
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Tarawneh, Constantine M., Luz Sotelo, Anthony A. Villarreal, Nancy de los Santos, Ryan L. Lechtenberg, and Robert Jones. "Temperature Profiles of Railroad Tapered Roller Bearings With Defective Inner and Outer Rings." In 2016 Joint Rail Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/jrc2016-5816.
Повний текст джерелаАнотація:
In the railroad industry, monitoring the condition of key components such as bearings and wheels is vital to ensure the safe transport of goods and commodities. Bearing seizures are amongst the most dangerous types of failures experienced by trains because they occur unexpectedly and may lead to costly derailments. Current bearing health monitoring techniques include tracking the temperature and acoustic emissions given by the bearings. Although temperature histories of railroad tapered roller bearings are readily available, the literature does not provide information relating the temperature profiles to the severity of the bearing defect. The study presented here investigates the correlation between temperature profiles and bearing defect severity measured by the size of spalls present on bearing outer (cup) and inner (cone) rings. The temperature data used for this study was acquired from defective and healthy bearings that were run at various operating load and speed conditions. The data presented here provides the railroad industry with a greater understanding of the thermal behavior of defective bearings, which can be used to assess the future needs of bearing condition monitoring systems.
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Afsharfard, Aref, and Seyed Hamid Reza Sanei. "An Experimental Approach in Defect Detection of a Single Row Ball Bearing Using Noise Generation Signal." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-12146.
Повний текст джерелаАнотація:
Abstract Bearings are critical mechanical components that are used in rotary machinery. Timely detection of defects in such components can prevent catastrophic failure. Noise is generated during the rotation of bearings even without the presence of defects due to finite number of rotating elements to carry the load. Such noise is associated with the change in effective stiffness during rotation, however, a sharp spike is observed in the noise level with presence of local defects. This study uses the noise generation aspect of roller bearings to identify local defect in a single row ball bearing with outer race stationary under radial load. Experimental testing is conducted on two identical bearings. The defective bearing is selected from a diesel engine subjected to 20 years of service. Dissecting the defective bearing revealed pitting and spalling of the inner race and balls, the most two common bearing defects. Both time and frequency analysis of sound pressure generated by the bearings were performed. The results show that there is a clear distinction in the time and frequency spectra between healthy and defective bearings. Findings of this study revealed that using a simple cost efficient in-house experimental setup, local defects can be readily detected.
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Montalvo, Joseph, Constantine Tarawneh, and Arturo A. Fuentes. "Vibration-Based Defect Detection for Freight Railcar Tapered-Roller Bearings." In 2018 Joint Rail Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/jrc2018-6210.
Повний текст джерелаАнотація:
The railroad industry currently utilizes two wayside detection systems to monitor the health of freight railcar bearings in service: The Trackside Acoustic Detection System (TADS™) and the wayside Hot-Box Detector (HBD). TADS™ uses wayside microphones to detect and alert the conductor of high risk defects. Many defective bearings may never be detected by TADS™ due to the fact that a high risk defect is considered a spall which spans more than 90% of a bearing’s raceway, and there are less than 20 systems in operation throughout the United States and Canada. Much like the TADS™, the HBD is a device that sits on the side of the rail tracks and uses a non-contact infrared sensor to determine the temperature of the train bearings as they roll over the detector. The accuracy and reliability of the temperature readings from this wayside detection system have been concluded to be inconsistent when comparing several laboratory and field studies. The measured temperatures can be significantly different from the actual operating temperature of the bearings due to several factors such as the class of railroad bearing and its position on the axle relative to the position of the wayside detector. Over the last two decades, a number of severely defective bearings were not identified by several wayside detectors, some of which led to costly catastrophic derailments. In response, certain railroads have attempted to optimize the use of the temperature data acquired by the HBDs. However, this latter action has led to a significant increase in the number of non-verified bearings removed from service. In fact, about 40% of the bearings removed from service in the period from 2001 to 2007 were found to have no discernible defects. The removal of non-verified (defect-free) bearings has resulted in costly delays and inefficiencies. Driven by the need for more dependable and efficient condition monitoring systems, the University Transportation Center for Railway Safety (UTCRS) research team at the University of Texas Rio Grande Valley (UTRGV) has been developing an advanced onboard condition monitoring system that can accurately and reliably detect the onset of bearing failure. The developed system currently utilizes temperature and vibration signatures to monitor the true condition of a bearing. This system has been validated through rigorous laboratory testing at UTRGV and field testing at the Transportation Technology Center, Inc. (TTCI) in Pueblo, CO. The work presented here provides concrete evidence that the use of vibration signatures of a bearing is a more effective method to assess the bearing condition than monitoring temperature alone. The prototype bearing condition monitoring system is capable of identifying a defective bearing with a defect size of less than 6.45 cm2 (1 in2) using the vibration signature, whereas, the temperature profile of that same bearing will indicate a healthy bearing that is operating normally.
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Cherrad, M. L., H. Bendjama, and T. Fortaki. "Vibration analysis for defective bearings by blind source separation." In 2021 International Congress of Advanced Technology and Engineering (ICOTEN). IEEE, 2021. http://dx.doi.org/10.1109/icoten52080.2021.9493532.
Повний текст джерела
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Liu, T. I., and F. Ordukhani. "Detection of Roller Bearing Conditions Using Mechatronics Approach." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0732.
Повний текст джерелаАнотація:
Abstract An on-line monitoring and diagnostic system is needed to detect faulty bearings. In this work, by applying the feature selection technique to the data obtained from vibration signals, six indices were selected. Artificial neural networks were used for nonlinear pattern recognition. An attempt was made to distinguish between normal and defective bearings. Counterpropagation neural networks with various network sizes were trained for these tasks. The counterpropagation neural networks were able to recognize a normal from a defective bearing with the success rate between 88.3% to 100%. The best results were obtained when all the six indices were used for the on-line classification of roller bearings.
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Cuanang, Jonas, Constantine Tarawneh, Martin Amaro, Jennifer Lima, and Heinrich Foltz. "Optimization of Railroad Bearing Health Monitoring System for Wireless Utilization." In 2020 Joint Rail Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/jrc2020-8060.
Повний текст джерелаАнотація:
Abstract In the railroad industry, systematic health inspections of freight railcar bearings are required. Bearings are subjected to high loads and run at high speeds, so over time the bearings may develop a defect that can potentially cause a derailment if left in service operation. Current bearing condition monitoring systems include Hot-Box Detectors (HBDs) and Trackside Acoustic Detection Systems (TADS™). The commonly used HBDs use non-contact infrared sensors to detect abnormal temperatures of bearings as they pass over the detector. Bearing temperatures that are about 94°C above ambient conditions will trigger an alarm indicating that the bearing must be removed from field service and inspected for defects. However, HBDs can be inconsistent, where 138 severely defective bearings from 2010 to 2019 were not detected. And from 2001 to 2007, Amsted Rail concluded that about 40% of presumably defective bearings detected by HBDs did not have any significant defects upon teardown and inspection. TADS™ use microphones to detect high-risk bearings by listening to their acoustic sound vibrations. Still, TADS™ are not very reliable since there are less than 30 active systems in the U.S. and Canada, and derailments may occur before bearings encounter any of these systems. Researchers from the University Transportation Center for Railway Safety (UTCRS) have developed an advanced algorithm that can accurately and reliably monitor the condition of the bearings via temperature and vibration measurements. This algorithm uses the vibration measurements collected from accelerometers on the bearing adapters to determine if there is a defect, where the defect is within the bearing, and the approximate size of the defect. Laboratory testing is performed on the single bearing and four bearing test rigs housed at the University of Texas Rio Grande Valley (UTRGV). The algorithm uses a four second sample window of the recorded vibration data and can reliably identify the defective component inside the bearing with up to a 100% confidence level. However, about 20,000 data points are used for this analysis, which requires substantial computational power. This can limit the battery life of the wireless onboard condition monitoring system. So, reducing the vibration sample window to conduct an accurate analysis should result in a more optimal power-efficient algorithm. A wireless onboard condition monitoring module that collects one second of vibration data (5,200 samples) was manufactured and tested to compare its efficacy against a wired setup that uses a four second sample window. This study investigates the root-mean-square values of the bearing vibration and its power spectral density plots to create an optimized and accurate algorithm for wireless utilization.