Academic literature on the topic 'Vibration signature in defective bearings'
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Journal articles on the topic "Vibration signature in defective bearings"
1
Rabeyee, K., X. Tang, F. Gu, and A. D. Ball. "The Effect of Wear Evolution on Vibration-based Fault Detection in Tapered Roller Bearings." International Journal of Condition Monitoring 9, no. 1 (April 1, 2019): 18–23. http://dx.doi.org/10.1784/204764297237736057.
Full textAbstract:
Rolling element bearings (REBs) are typical tribological components used widely in rotating machines. Their failure could cause catastrophic damage. Therefore, condition monitoring of bearings has always had great appeal for researchers. Usually, the detection and diagnostics of incipient bearing faults are achieved by characterising the weak periodic impacts induced by the collision of defective bearing components. However, race wear evolution, which is inevitable in bearing applications, can affect the contact between bearing elements and races, thereby decreasing the impact magnitudes and impeding detection performance. In this paper, the effect of wear evolution on the condition monitoring of rolling bearings is firstly analysed based on internal clearance changes resulting from the wear effect. Then, an experimental study is ingeniously designed to simulate wear evolution and evaluate its influence on wellknown envelope signatures according to measured vibrations from widely used tapered roller bearings. The fault type is diagnosed in terms of two indices: the magnitude variation of characteristic frequencies and the deviation of such frequencies. The experimental results indicate a signature decrease with regard to wear evolution, suggesting that accurate severity diagnosis needs to take into account both the wear conditions of the bearing and the signature magnitudes.
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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.
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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.
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Gaaliche, Nesrine, Subramanian Chithambaram, and Raouf Fathallah. "Dynamic Analysis of Outer and Inner Race Defects on Thermoplastic Rolling Bearing System Using Implicit Finite Element Method." International Journal of Acoustics and Vibration 27, no. 2 (June 30, 2022): 172–82. http://dx.doi.org/10.20855/ijav.2022.27.21853.
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This paper proposes an improved finite element dynamic model to analyze the vibration response of a rolling bearing system. The vibration responses of defect free and defective polypropylene (PP) bearings are analyzed using the finite element analysis and compared with the experimental results using the in house developed bearing fatigue test rig. The boundary conditions of this model are imposed to ensure an adequate homogeneity with the experimental apparatus. This study considers the viscoelastic property of thermoplastic to investigate the effect of the flexibility and damping viscosity of material. The three-dimensional dynamic analysis detects the vibrations produced in a rolling bearing system. Modal frequencies and the vibration modes shapes which must be avoided had been determined. Monitoring the evolution of vibration signatures as a function of defect location is also carried out using Finite Element Analysis (FEM). Test results reveal that peak vibration amplitudes are more pronounced in an inner ring defect than in an outer ring defect. Vibration signals of stainless-steel bearings are investigated to compare the performance of the thermoplastic bearings with its metallic counterparts. Both test and simulation results reveal that a lower level of vibration is observed with PP bearings compared to that of metal. The PP bearings not only dampen vibrations, but also accommodate shaft misalignments unlike their steel counterparts. Once overall vibration spectrums results are validated, Von Mises stresses within the rings are evaluated under excessive loading conditions. The simulation results show that stress is high in raceways where the balls are compressed between the raceways. Computational results agree well with the experimental tests for the test scenarios.
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Hariharan, V., and P. S. S. Srinivasan. "Condition monitoring studies on ball bearings considering solid contaminants in the lubricant." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 8 (January 12, 2010): 1727–48. http://dx.doi.org/10.1243/09544062jmes1885.
Full textAbstract:
Rolling element bearings are common in any rotating machinery. They are subject to failure under continuous running. Therefore they have received a great deal of attention in the field of condition monitoring. In rolling element bearings, contamination of lubricant grease by solid particles is one of the several reasons for an early bearing failure. In this context, this article investigates the effect of contamination of lubricant by solid particles on the dynamic behaviour of rolling bearings. Silica powder at three concentration levels and different particle sizes was used to contaminate the lubricant. Experimental tests have been performed on the ball bearings lubricated with grease, and the trends in the amount of vibration affected by the contamination of the grease were determined. The contaminant concentration as well as the particle size is varied. Vibration signatures were analysed in terms of root mean square (RMS) values. From the results, some fruitful conclusions are made about the bearing performance. The effects of contaminant and the bearing vibration are studied for both good and defective bearings. The results show significant variation in the RMS velocity values on varying the contaminant concentration and particle size.
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Jamil, Mohd Atif, and Sidra Khanam. "Fault Classification of Rolling Element Bearing in Machine Learning Domain." International Journal of Acoustics and Vibration 27, no. 2 (June 30, 2022): 77–90. http://dx.doi.org/10.20855/ijav.2022.27.21829.
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Rolling element bearings are crucial components of rotating machinery used in various industries, including aerospace, navigation, machine tools, etc. Therefore, it is essential to establish suitable techniques for condition monitoring and fault diagnosis of bearings to avoid breakdowns and damages during operation for overall industrial sustainability. Vibration-based condition monitoring has been the most employed technique in this perspective. Many researchers have investigated the vibration response of rolling element bearings having inner race defects, outer race defects, or rolling element defects using conventional techniques in past decades. However, Machine Learning (ML) has emerged as another way of bearing fault diagnosis. In this work, fault signatures of ball bearings are classified using a total of 6 (with 24 subcategories) ML models, and a comparative performance of these models is presented. The ML classifiers are trained with extracted time-domain and frequency-domain features using open-source Case Western Reserve University (CWRU) bearing data. Two datasets of different sample size and number of samples of vibration data corresponding to a healthy ball bearing, a defective bearing with inner race defect, a ball defect, and an outer race defect, running at a particular set of working conditions, are considered. The accuracy of ML models is compared to identify the best model for classifying the faults under consideration.
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Patra, Pravajyoti, V. Huzur Saran, and Suraj P. Harsha. "Chaotic dynamics of cylindrical roller bearing supported by unbalanced rotor due to localized defects." Journal of Vibration and Control 26, no. 21-22 (March 3, 2020): 1898–908. http://dx.doi.org/10.1177/1077546320912109.
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This article presents a nonlinear vibration signature study of high-speed defective cylindrical roller bearings under unbalance rotor conditions. Qualitative analysis is conducted considering a spall defect of a specific size on major elements such as outer race, inner race, and rollers. A spring-mass model with nonlinear stiffness and damping is formulated to study the dynamic behavior of the rotor-bearing model. The set of nonlinear differential equations are solved using the fourth-order Runge–Kutta method to predict the characteristics of the discrete spectra and analyze the stability of the system. The results show that higher impulsive forces are generated because of outer race defects than defects in the inner race and roller. This can be explained as every time the roller passes through the defect in the outer race during rotation, the energy is released. However, in the case of both the roller and inner race defects, the impulsive force generated in the load zone is averaged because of the force generated in the unloading zone. The route to chaos from periodic to quasiperiodic response has been observed and analyzed that vibration signature is very much sensitive not only to the defects of bearing components but also to the rotor speed.
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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.
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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.
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Afia, Adel, Chemseddine Rahmoune, and Djamel Benazzouz. "Gear fault diagnosis using Autogram analysis." Advances in Mechanical Engineering 10, no. 12 (December 2018): 168781401881253. http://dx.doi.org/10.1177/1687814018812534.
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Rotary machines consist of various devices such as gears, bearings, and shafts that operate simultaneously. As a result, vibration signals have nonlinear and non-stationary behavior, and the fault signature is always buried in overwhelming and interfering contents, especially in the early stages. As one of the most powerful non-stationary signal processing techniques, Kurtogram has been widely used to detect gear failure. Usually, vibration signals contain a relatively strong non-Gaussian noise which makes the defective frequencies non-dominant in the spectrum compared to the discrete components, which reduce the performance of the above method. Autogram is a new sophisticated enhancement of the conventional Kurtogram. The modern approach decomposes the data signal by Maximal Overlap Discrete Wavelet Packet Transform into frequency bands and central frequencies called nodes. Subsequently, the unbiased autocorrelation of the squared envelope for each node is computed to select the node with the highest kurtosis value. Finally, Fourier transform is applied to that squared envelope to extract the fault signature. In this article, the proposed method is tested and compared to Fast Kurtogram for gearbox fault diagnosis using experimental vibration signals. The experimental results improve the detectability of the proposed method and affirm its effectiveness.
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Ou, Yangli, Shuilong He, Chaofan Hu, Jiading Bao, and Wenjie Li. "Research on Rolling Bearing Fault Diagnosis Using Improved Majorization-Minimization-Based Total Variation and Empirical Wavelet Transform." Shock and Vibration 2020 (May 15, 2020): 1–11. http://dx.doi.org/10.1155/2020/3218564.
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Bearings are among the most widely used core components in mechanical equipment. Their failure creates the potential for serious accidents and economic losses. Vibration signature analyses are the most common approach to assess the viability of bearings due to its ease of measurement and high correlation with structural dynamics. However, the collected vibration signals of rolling bearings are usually nonstationary and are inevitably accompanied by noise interference. This makes it difficult to extract the feature frequency for the failed bearing and affects the diagnosis accuracy. The majorization-minimization-based total variation (TV-MM) denoising algorithm effectively removes the noise interference from the signal and highlights the related feature information. The value of its main parameter λ determines the quality of the denoising effect. However, manually selecting parameters requires professional experience in a process that it is time-consuming and laborious, while the use of genetic algorithms is cumbersome. Therefore, an improved particle swarm algorithm (IPSO) is used to find the optimal solution of λ. The IPSO utilises the mutation concept in genetic algorithms to reinitialise the particles with a certain probability after each update. In addition, the empirical wavelet transform (EWT) is an adaptive signal processing method suitable for processing nonlinear and nonstationary signals. Therefore, this paper presents an ensemble analysis method that combines the IPSO, TV-MM, and EWT. First, IPSO is used to optimise the denoising parameter λ. The TV-MM under this parameter effectively removes the background noise interference and improves the accuracy of the subsequent modal decomposition. Then, the EWT is used for the adaptive division to produce a set of sequences. Finally, Hilbert envelope demodulation is performed on each component to realise fault diagnosis. The results from simulations and signals received from defective bearings with outer race fault, inner race fault, and rolling element fault demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings.
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Aijun, Hu, Lin Jianfeng, Sun Shangfei, and Xiang Ling. "A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing." Shock and Vibration 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/9375491.
Full textAbstract:
The fault signals of rolling element bearing are often characterized by the presence of periodic impulses, which are modulated high-frequency harmonic components. The features of early fault in rolling bearing are very weak, which are often masked by background noise. The impulsiveness of the vibration signal has affected the identification of characteristic frequency for the early fault detection of the bearing. In this paper, a novel approach based on morphological operators is presented for impulsive signal extraction of early fault in rolling element bearing. The combination Top-Hat (CTH) is proposed to extract the impulsive signal and enhance the impulsiveness of the bearing fault signal, and the envelope analysis is applied to reveal the fault-related signatures. The impulsive extraction performance of the proposed CTH is compared with that of finite impulse response filter (FIR) by analyzing the simulated bearing fault signals, and the result indicates that the CTH is more effective in extracting impulsive signals. The method is evaluated using real fault signals from defective bearings with early rolling element fault and early fault located on the outer race. The results show that the proposed method is able to enhance the impulsiveness of early bearing fault signals.
Dissertations / Theses on the topic "Vibration signature in defective bearings"
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Stack, Jason R. "Fault signature detection for rolling element bearings in electric machines." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/13276.
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Moazen-ahmadi, Alireza. "Vibration signatures of defective bearings and defect size estimation methods." Thesis, 2016. http://hdl.handle.net/2440/112971.
Full textAbstract:
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
Book chapters on the topic "Vibration signature in defective bearings"
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Pateriya, Ambuj, N. D. Mittal, and M. K. Pradhan. "Identification of Lubricant Contamination in Journal Bearings Using Vibration Signature Analysis." In Lecture Notes in Mechanical Engineering, 35–44. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8341-1_3.
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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.
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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.
Conference papers on the topic "Vibration signature in defective bearings"
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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.
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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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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.
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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.
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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.
Full textAbstract:
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.
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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.
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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.
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Jain, Sharad, and Hugh Hunt. "Vibration Response of a Wind-Turbine Planetary Gear Set in the Presence of a Localized Planet Bearing Defect." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-63452.
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In a wind-turbine gearbox, planet bearings exhibit a high failure rate and are considered as one of the most critical components. Development of efficient vibration based fault detection methods for these bearings requires a thorough understanding of their vibration signature. Much work has been done to study the vibration properties of healthy planetary gear sets and to identify fault frequencies in fixed-axis bearings. However, vibration characteristics of planetary gear sets containing localized planet bearing defects (spalls or pits) have not been studied so far. In this paper, we propose a novel analytical model of a planetary gear set with ring gear flexibility and localized bearing defects as two key features. The model is used to simulate the vibration response of a planetary system in the presence of a defective planet bearing with faults on inner or outer raceway. The characteristic fault signature of a planetary bearing defect is determined and sources of modulation sidebands are identified. The findings from this work will be useful to improve existing sensor placement strategies and to develop more sophisticated fault detection algorithms.
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Singh, Kamaljit, Sudhanshu Sharma, and J. P. Sharma. "Antifriction Bearing Sleeves for Diagnostics and Energy Harvesting." In STLE/ASME 2010 International Joint Tribology Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ijtc2010-41152.
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Roller ball bearings are the most common and one of the most important components in rotating machinery. Bearings, in general produce vibrations which can be harvested to produce energy and analysis of these vibrations can also be used to determine the condition of ball bearing. In this paper we discuss how to use the bearings for energy harvesting and conditioning monitoring in machines. A sleeve, padded with piezoelectric material, is designed to solve the dual purpose. Piezo electric materials have the ability to generate an electric field or electric potential in response to applied mechanical strain. Tests are conducted on the good and defective bearings to study the effectiveness of the sleeve. Phase fluctuation based processors are found to be effective in ball bearing condition monitoring. For condition monitoring the signature responses for a given time period are studied. At a constant speed of increase in load leads to an increase in voltage generated. For a single non-coated piezo film, voltage varies from 383 mV at 80 lbf to 683 mV at 320 lbf at 40Hz. With the increased stacking of non-coated piezo films at 320 lbf, voltage generated shows an increase of 23 %. Nano-coating mixture (Ferrofluid and Zinc oxide nanoparticles) causes an additional piezoelectric effect on the surface of piezo film as ZnO acts as an additional source of electrons, due to its ability to emit charges at room temperature. The single piezo film configuration at 320 lbf generates a voltage of 663 mV while the voltage increases 2.1 times for a single nano-coated piezo film. Introduction of defects causes increases in the contact stress at the asperities leading to an increase in the vibrations and forces. Also, an increase in vibration and force, leads to an increase in the voltage generated. For a single piezo film configuration, in a normal bearing, the voltage generated is 663 mV while a defective bearing gives a voltage of 698 mV.
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Qiu, Hai, Huageng Luo, and Neil Eklund. "On-Board Aircraft Engine Bearing Prognostics: Enveloping or FFT Analysis?" In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86141.
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Roller bearing prognosis requires the detection of a bearing defect signature in the earliest possible stage in order to avoid a minor or catastrophic mechanical failure. Defects can occur in any of the bearing parts, inner and outer race, cage and rolling elements. It is possible to identify the defective component of the bearing based on the specific vibration frequencies that are excited. However, the pattern of vibration spectrum changes as the bearing deteriorates through different stages. Depending on which failure stage the bearing is in, different techniques are required to find fault signatures in different frequency ranges. Techniques such as enveloping analysis that works in the high frequency region require higher data sampling rates and therefore more expensive data acquisition hardware than techniques conducted in low frequency region. This paper compares two popular rolling element bearing diagnostics techniques — spectrum analysis in the bearing characteristic frequency range and enveloping analysis in the high frequency range — using aircraft engine test rig data. The techniques are compared both in terms of the time of detection and data sampling requirement; this analysis provides guidance for technology adoption in future field deployment. Results demonstrate that enveloping analysis is able to detect bearing defects much earlier than the spectrum analysis, but it requires a higher data sampling rate. The bearing defect characteristic frequency is detectable in low frequency spectrum only in the late stage of the failure and it is contaminated by other harmonics such as shaft unbalance. From a practical perspective, the final choice of the technology adopted for deployment should be based on an analysis of hardware requirements and tolerance of detection latency.
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Campbell, Craig M., and Massoud S. Tavakoli. "Vibration Signature Analysis of Needle Bearings." In International Congress & Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1996. http://dx.doi.org/10.4271/961015.
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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.
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Ohta, Hiroyuki, Shinya Hayashi, Soichiro Kato, and Yutaka Igarashi. "Effects of Grease Types on Vibration and Acoustic Emission of Defective Linear-Guideway Type Recirculating Ball Bearings." In STLE/ASME 2010 International Joint Tribology Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ijtc2010-41053.
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In this paper, vibrations and acoustic emissions (AEs) of defective linear-guideway type recirculating ball bearings under grease lubrication were measured. The experimental results show that the vibration and AE amplitudes (the pulse amplitudes, the RMS values) of both the normal and defective bearings have a tendency to be reduced when a grease with higher base oil viscosity is used. Under the same type of grease, the RMS values of the vibrations and AE of the defective bearings increase as the defect angle increases. However, the increases of the RMS values due to increased defect angle are reduced when a grease with higher base oil viscosity is used.