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Journal articles on the topic 'Defective bearings'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

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.

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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.
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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.

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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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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.

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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.
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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.

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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.
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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.

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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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6

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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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.

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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.
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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.

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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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9

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.

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10

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.

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11

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.

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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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12

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.

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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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13

Tarawneh, Constantine, Joseph Montalvo, and Brent Wilson. "Defect detection in freight railcar tapered-roller bearings using vibration techniques." Railway Engineering Science 29, no. 1 (February 3, 2021): 42–58. http://dx.doi.org/10.1007/s40534-020-00230-x.

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AbstractCurrently, there are two types of defect detection systems used to monitor the health of freight railcar bearings in service: wayside hot-box detection systems and trackside acoustic detection systems. These systems have proven to be inefficient in accurately determining bearing health, especially in the early stages of defect development. To that end, a prototype onboard bearing condition monitoring system has been developed and validated through extensive laboratory testing and a designated field test in 2015 at the Transportation Technology Center, Inc. in Pueblo, CO. The devised system can accurately and reliably characterize the health of bearings based on developed vibration thresholds and can identify defective tapered-roller bearing components with defect areas smaller than 12.9 cm2 while in service.
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14

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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15

Zhang, Si Jie, Bin Chen, Bao Cheng Gao, and Yuan Zhou. "Moving Defect Localization of Wheel-Bearings with Particle Filter and MUSIC." Advanced Materials Research 945-949 (June 2014): 2021–25. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2021.

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To solve the problem of moving defect localization for wheel-bearings, a novel algorithm based on particle filter and multiple signal classification (MUSIC) is proposed in this paper. It introduces two-dimensional circular sensor array to measure acoustic signals of defective bearings. By through of MUSIC, the direction-of-arrivals (DOAs) of defective signal are firstly estimated. After the motion trajectory was calculated by particle filter and DOAs, the defect was located by reference sound source. The experimental results show that the radius and phase errors of proposed method are less than 2mm and 5 degrees.
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16

Jamadar, Imran Moulaalli, and Dipakkumar Vakharia. "Correlation of base oil viscosity in grease with vibration severity of damaged rolling bearings." Industrial Lubrication and Tribology 70, no. 2 (March 12, 2018): 264–72. http://dx.doi.org/10.1108/ilt-04-2016-0078.

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Purpose The main objective of the paper is to explore the theoretical correlation of base oil viscosity in grease and to study the effect of grease grade on mechanical vibrations associated with the damaged rolling bearings. Design/methodology/approach For theoretical purposes, formulation theory of dimensional analysis was implemented. Experiments were then performed on the test bearings lubricated with three different types of greases, namely, SKF LGHP2, SKF LGMT3 and SKF LGWA2. Findings The numerical results obtained from the theoretical model along with the results of experiments show that the vibration amplitudes of the defective bearings come down to a lower level when it is lubricated with the grease of a higher base oil viscosity. Research limitations/implications The promising results from the theoretical model make it usable for the practical rotating machineries applying a variety of the rolling bearings. Consequently, if the bearing is not severely damaged, its performance can be increased by lubricating it with thicker grease. Originality/value Despite many significant contributions in the field to detect the presence of defects, not many studies have been performed that relate the lubrication condition of the rolling bearings with the vibration response, because around 50-75% of the bearing failures are attributed to be lubrication related. Hence, there is need to develop a mathematical model that can correlate the vibration severity of the bearings with viscosity of the lubricant oil in the greases along with other design and operating parameters.
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17

Dong, Ya Bin, Ming Fu Liao, Xiao Long Zhang, and Yu Min He. "A New Morphological Method for Faults Diagnosis of Rolling Element Bearings." Applied Mechanics and Materials 152-154 (January 2012): 1539–44. http://dx.doi.org/10.4028/www.scientific.net/amm.152-154.1539.

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A new morphology analysis method had been proposed to effectively extract the impulse components in the vibration signals of defective rolling element bearings. In the method, the morphology operator had been constructed by average of the closing and opening operator. For the construction of structure element (SE), the flat and zero was adopted as the shape and the height of SE, respectively, and the element numbers of the SE was optimized by a new proposed criterion (called SNR criterion). Vibration signals of two defective rolling bearings with an outer and an inner fault respectively are employed to validate the proposed method and the results are compared with ones calculated by envelopment analysis method. It shows that the proposed method is effective and robust to extract morphological features, and can be used to the on-line diagnostics of rolling element bearings in rotating machines conveniently.
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18

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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19

Zhang, Zhinan, Weimin Ding, and Huifang Ma. "Local stress analysis of a defective rolling bearing using an explicit dynamic method." Advances in Mechanical Engineering 8, no. 12 (December 2016): 168781401667990. http://dx.doi.org/10.1177/1687814016679909.

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This article provides insights into the localized stress in a defect zone of the rolling bearings when the rolling elements pass through the defect. A dynamic finite element model of a rolling bearing with an artificial round defect in its outer raceway is solved numerically using the explicit dynamic finite element software package, ABAQUS. The maximum Mises stress and maximum contact pressure in the defect zone are obtained in the simulation. The effects of radial load, rotation speed, and initial defect size on the stress level are investigated. The results show that higher stresses are generated during the rolling balls passing through the defect. The radial load and defect size have significant effects on the stress level of defect, while low-level rotation speed has neglected effects.
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20

IGARASHI, Teruo, and Kazuto TOKURA. "Studies on vibration and sound of defective rolling bearings (4th report, Sound of ball bearing with defects)." Transactions of the Japan Society of Mechanical Engineers Series C 51, no. 467 (1985): 1515–22. http://dx.doi.org/10.1299/kikaic.51.1515.

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21

Liu, Fang, Chanqing Shen, Qingbo He, Ao Zhang, Fanrang Kong, and Yongbin Liu. "Doppler effect reduction scheme via acceleration-based Dopplerlet transform and resampling method for the wayside acoustic defective bearing detector system." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 18 (April 11, 2014): 3356–73. http://dx.doi.org/10.1177/0954406214530880.

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In a wayside acoustic defective bearing detector system, bearing faults are detected through the analysis of the acoustic signal generated by the bearings of a passing vehicle. As vehicles pass by with high speeds, the acoustic signal recorded by the stationary microphone is disturbed by the Doppler effect. The reduction of the frequential structure disturbance of signals facilitates the efficient diagnosis of bearing faults. This study proposes a Doppler effect reduction scheme for the removal of the frequential structure disturbances of Doppler-shifted signals in the acoustic defective bearing detector system. First, the parameters, including the initial speed and the initial acceleration of the vehicle, are estimated by the acceleration-based Dopplerlet transform via the matching pursuit algorithm. Second, the time vector for resampling is calculated according to the estimated initial speed, the initial acceleration in the first step, the sound speed, and the measured geometric parameters of the ADBD system. Finally, the distorted signal is resampled through spline interpolation. Simulation and experimental cases are used to validate the effectiveness of the proposed scheme. Compared with the steady-motion-based method, the proposed scheme can better capture the true time-varying nature of Doppler-shifted signals. Moreover, this scheme is also robust to noise.
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22

Saucedo-Espinosa, Mario A., Hugo Jair Escalante, and Arturo Berrones. "Detection of defective embedded bearings by sound analysis: a machine learning approach." Journal of Intelligent Manufacturing 28, no. 2 (November 21, 2014): 489–500. http://dx.doi.org/10.1007/s10845-014-1000-x.

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23

Ono, K., and Y. Okada. "Analysis of Ball Bearing Vibrations Caused by Outer Race Waviness." Journal of Vibration and Acoustics 120, no. 4 (October 1, 1998): 901–8. http://dx.doi.org/10.1115/1.2893918.

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An analytical investigation of the shaft vibration caused by a ball bearing is presented in this paper. Bearing vibration could be caused by a number of factors, such as defects occurring on the race track or the rolling elements. A common problem with defective bearings is the generation of waviness on the outer race track during the manufacturing process. The vibration of an automobile drive shaft caused by rolling elements rolling over the waviness surface is transmitted to the passenger cabin, and produces undesirable noise. In this paper an analytical study is undertaken to evaluate the effect of waviness number, radial gap and shaft imbalance on the bearing vibration. An experimental investigation was carried out to confirm the analytical study. The results show that the analytical study and experimental investigation agree very well.
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24

IGARASHI, Teruo, and Junichi KATO. "Studies on Vibration and Sound of Defective Rolling Bearings : Third Report, Vibration of Ball Bearing with Multiple Defects." Bulletin of JSME 28, no. 237 (1985): 492–99. http://dx.doi.org/10.1299/jsme1958.28.492.

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25

Su, Zhou, Juanjuan Shi, Weiguo Huang, Jun Wang, Changqing Shen, Xingxing Jiang, and Zhongkui Zhu. "Bearing fault severity assessment using variable-step multiscale fusion Lempel-Ziv complexity." Journal of Physics: Conference Series 2184, no. 1 (March 1, 2022): 012002. http://dx.doi.org/10.1088/1742-6596/2184/1/012002.

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Abstract Most research on bearing health condition monitoring is devoted to distinguish the defective condition from the healthy condition. In practice, the fault severity assessment is also critical for performing the prognostics and maintenance of bearings. Lempel-Ziv complexity (LZC) has been widely used for the bearing quantitative fault diagnosis. However, the original LZC extracts fault information only at the single scale and often fails to portray the fault features. Then, the multiscale LZC is proposed to more comprehensively extract the fault information. However, multiscale analysis would shorten the length of time series and lead to inaccurate calculation results as the scale factor increases. As such, this paper proposes a novel bearing fault severity assessment method using variable-step multiscale fusion Lempel-Ziv complexity (VSMFLZC) to facilitate the quantitative fault diagnosis of bearings. The variable step length strategy is developed in the proposed method to optimize the coarse-grained procedure. Then, Laplace score is applied to evaluate the features and weights at each scale to obtain the proposed VSMFLZC. By such a fusion algorithm, the sequence can be converted into a single but comprehensive evaluation indicator for the fault severity assessment. The experimental results indicate that the proposed method outperforms the original LZC and multiscale LZC, where the fault features can be more comprehensively extracted and the fault severity assessment of rolling bearing can be successfully realized.
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26

Jamadar, I. M., and D. P. Vakharia. "An in-situ synthesized model for detection of defective roller in rolling bearings." Engineering Science and Technology, an International Journal 19, no. 3 (September 2016): 1488–96. http://dx.doi.org/10.1016/j.jestch.2016.05.003.

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27

Tandon, N., and K. S. Kumar. "Detection of Defects at Different Locations in Ball Bearings by Vibration and Shock Pulse Monitoring." Noise & Vibration Worldwide 34, no. 3 (March 2003): 9–16. http://dx.doi.org/10.1260/095745603321537983.

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In the present study, the vibrations and shock pulse values of good and defective bearings have been measured. Bearings with one defect and two defects (with different degrees of separation) on the outer race have been used. The position of the defects with respect to the maximum load zone axis was also varied. The study shows that it is easier to detect defects on the outer race of bearings when the defects are in the maximum load zone. The vibration levels and shock pulse values decrease with increase in angle between two defects. The levels decrease as the defects are moved away from the maximum load position. Shock pulse method showed better defect detectability as compared to overall vibration levels. However, at low speed, the shock pulse method has not proved to be as effective as at higher speeds.
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28

Tomar, Arvind Singh, and Pratesh Jayaswal. "A Hybrid Fault Diagnosis Method Using Translation Invariant Wavelet Denoising, Hierarchical Entropy, and Support Vector Machine with PSO Algorithm." Traitement du Signal 39, no. 6 (December 31, 2022): 2041–53. http://dx.doi.org/10.18280/ts.390616.

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The rolling element bearing is used in various machinery and produces vibration due to imperfections, surface irregularities during manufacture, damaged bearings, and inaccuracies in the allied element. Also, the rolling element bearing vibration generally shows non-linear dynamic characteristics and is masked with heavy background noise. This noble investigation advances a hybrid technique for removing background noise from the vibration signal and detecting bearing defects. Translation invariant wavelet denoising is the initial stage in this hybrid method for noise removal from the signal. The second phase uses Hierarchical Entropy (HE) for defect feature frequency extraction. Hierarchical entropy at scale four and SampEns of eight hierarchical decomposition nodes was utilized to determine the defect feature vector. In particular, low-frequency components are investigated through multi-scale entropy (MSE), but hierarchical entropy (HE) incorporates low-frequency and high-frequency components and can extract more defective information. Implemented a multi-class support vector machine (SVM) for extracting Hierarchical entropy as feature vectors. These feature vectors are trained by utilizing particle swarm optimization (PSO). To accomplish a prediction model, examine the optimal SVM parameters and then various bearing conditions with the variation of type, size, speed, and load severity identified by SVM. The investigation results show that hierarchical entropy can adequately and more precisely express the features of bearing vibration signals. It is beyond MSE, and the proposed Nobel hybrid Translation invariant wavelet denoising and Hierarchical entropy-based method will effectively remove the noisy background signal. Also, it distinguishes different bearings successfully, indicates the bearing conditions correctly, and is more prominent than those found on MSE.
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29

Wang, Ran, Chenyu Zhang, Liang Yu, Haitao Fang, and Xiong Hu. "Rolling Bearing Weak Fault Feature Extraction under Variable Speed Conditions via Joint Sparsity and Low-Rankness in the Cyclic Order-Frequency Domain." Applied Sciences 12, no. 5 (February 26, 2022): 2449. http://dx.doi.org/10.3390/app12052449.

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Rolling bearings are critical to the normal operation of mechanical systems, which often undergo time-varying working conditions. When the local defects appear on a rolling bearing, the transient impulses will generate and be covered by the strong background noise. Therefore, extracting the rolling bearing weak fault feature with time-varying speed is critical to mechanical system diagnosis. A weak fault feature extraction strategy of rolling bearing under time-varying working conditions is proposed. Firstly, the order-frequency spectral correlation (OFSC) is computed for transferring the measured signal into a higher dimensional space. Then, the joint sparsity and low-rankness constraint is imposed on OFSC to detect the time-varying faulty characteristics. An algorithm in the alternating direction method of multipliers (ADMM) framework is derived. Finally, the enhanced envelope order spectrum (EEOS) is applied to further detect the defective features, which can make the fault features more obvious. The feasibility of the proposed method is confirmed by simulations and an experimental case.
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He, Qingbo, Jun Wang, Fei Hu, and Fanrang Kong. "Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement." Journal of Sound and Vibration 332, no. 21 (October 2013): 5635–49. http://dx.doi.org/10.1016/j.jsv.2013.05.026.

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31

Liu, Jing, and Yimin Shao. "A numerical investigation of effects of defect edge discontinuities on contact forces and vibrations for a defective roller bearing." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 230, no. 4 (August 3, 2016): 387–400. http://dx.doi.org/10.1177/1464419315615451.

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Vibration characteristics of a roller bearing caused by a roller passing over the defect on the races are determined by the contact forces between the roller and races of the bearing. The vibration characteristics and contact forces are determined by the sizes and edge discontinuities of the defect. Therefore, it is very useful to investigate the relationships between defect edge discontinuities and the contact forces, and those between defect edge discontinuities and the vibrations for diagnosing the defects with different edge discontinuities in the roller bearings. A dynamic nonlinear finite element model for a roller bearing with a localized surface defect considering different edge discontinuities on its outer race is developed using an explicit dynamics finite element software package in this work. The effects of the defect edge discontinuities, radial load, and shaft speed on the contact force between the roller and outer race of the roller bearing are investigated, as well as the vibrations of the bearing. In-depth analyses of the contact forces between the roller and localized surface defect with different edge discontinuities are presented, which did not study in the previous literatures. The numerical results show that the number of the impacts between the roller and end edge of the defect caused by the re-stressing is more than that between the roller and beginning edge of the defect caused by the de-stressing, which is also affected by the defect edge discontinuities.
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32

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.

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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.
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33

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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34

Rohani Bastami, Abbas, and Amir Bashari. "Rolling element bearing diagnosis using spectral kurtosis based on optimized impulse response wavelet." Journal of Vibration and Control 26, no. 3-4 (September 24, 2019): 175–85. http://dx.doi.org/10.1177/1077546319877702.

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Envelope analysis is widely used in fault diagnosis of rolling element bearings (REBs). In envelope analysis, it is necessary to select a frequency band which is related to the resonance of the bearing. Spectral kurtosis (SK) is known as a powerful method to find the resonance band in vibration of a defective REB. SK, calculated by short time Fourier transform, suffers from its dependency on the window length. In this article, a special wavelet transform is used to obtain a SK diagram. It is shown that choice of mother wavelet function has great influence on the resulting SK diagram. The proposed wavelet is based on the impulse response of a damped single degree of freedom system. An optimization algorithm is used to optimize the SK diagram for fault detection. The method is tested for both simulated and experimental vibration data.
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35

Rubio, Eduardo, and Juan C. Jáuregui. "Experimental characterization of mechanical vibrations and acoustical noise generated by defective automotive wheel hub bearings." Procedia Engineering 35 (2012): 176–81. http://dx.doi.org/10.1016/j.proeng.2012.04.178.

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36

Tu, Wenbing, Jinwen Yang, Wennian Yu, and Ya Luo. "Contact characteristic and vibration mechanism of rolling element bearing in the process of fault evolution." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 235, no. 1 (January 5, 2021): 19–36. http://dx.doi.org/10.1177/1464419320985707.

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The vibration response of rolling element bearing has a close relation with its fault. An accurate evaluation of the bearing vibration response is essential to the bearing fault diagnosis. At present, most bearing dynamics models are built based on rigid assumptions, which may not faithfully reveal the dynamic characteristics of bearing in the presence of fault. Moreover, previous similar works mainly focus on the fault with a specified size without considering the varying contact characteristics as the fault evolves. This paper developed an explicit dynamics finite element model for the bearing with three types of raceway faults considering the flexibility of each bearing component in order to accurately study the contact characteristic and vibration mechanism of defective bearings in the process of fault evolution. The developed model is validated by comparing its simulation results with both analytical and experimental results. The dynamic contact patterns between the rolling elements and the fault, the additional displacement due to the fault and the faulty characteristics within the bearing vibration signal during the fault evolution process are investigated. The analysis results from this work can provide practitioners an in-depth understanding towards the internal contact characteristics with the existence of raceway fault and theoretical basis for rolling bearing fault diagnosis.
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Jiang, Kuosheng, Lianghe Li, Liubang Han, and Shuai Gou. "In-Process Quality Inspection of Rolling Element Bearings Based on the Measurement of Microelastic Deformation of Outer Ring." Shock and Vibration 2019 (May 7, 2019): 1–12. http://dx.doi.org/10.1155/2019/5656143.

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Quality inspection is the necessary procedure before bearings leaving manufacturing factories. A testing machine with low shaft speed and light radial load condition is generally used to test the dynamic quality of bearings, which avoids creating any potential damages to testing bearings. However, the signal of defective bearings is easily polluted by very weak noise using the traditional vibration-based measurement method due to the low shaft speed and light radial load condition specified for nondestructive inspection, which needs complicated and time-consuming calculation and is not suitable for online inspection. Thus, there are problems about special operating conditions and weak fault severity in quality inspection of bearings, which is quite different from the fault diagnosis of bearings. In this paper, a novel dynamic quality evaluation technique is proposed based on the measurement of Hertz deformations. The measurement system is mainly composed of an eddy current sensor, sensor fixture, and data acquisition platform with less transfer path than the vibration-based measurement system. The sensor fixture is optimized through numerical simulations to obtain signals with a high signal-to-noise ratio. Accurate evaluation of dynamic quality can be implemented reliably with simple signal processing. The proposed method can be used with a rotating speed of 100 rev/min and test load of 100 N, which is remarkably lower than the traditional quality inspection machineries with a rotating speed of around 1000 rev/min and the test load of 400 N. Both simulation and experiment studies have verified the proposed method.
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38

Jiang, Li, and Shunsheng Guo. "Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis." Shock and Vibration 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/1205868.

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The high-dimensional features of defective bearings usually include redundant and irrelevant information, which will degrade the diagnosis performance. Thus, it is critical to extract the sensitive low-dimensional characteristics for improving diagnosis performance. This paper proposes modified kernel marginal Fisher analysis (MKMFA) for feature extraction with dimensionality reduction. Due to its outstanding performance in enhancing the intraclass compactness and interclass dispersibility, MKMFA is capable of effectively extracting the sensitive low-dimensional manifold characteristics beneficial to subsequent pattern classification even for few training samples. A MKMFA- based fault diagnosis model is presented and applied to identify different bearing faults. It firstly utilizes MKMFA to directly extract the low-dimensional manifold characteristics from the raw time-series signal samples in high-dimensional ambient space. Subsequently, the sensitive low-dimensional characteristics in feature space are inputted into K-nearest neighbor classifier so as to distinguish various fault patterns. The four-fault-type and ten-fault-severity bearing fault diagnosis experiment results show the feasibility and superiority of the proposed scheme in comparison with the other five methods.
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39

Deilamsalehy, Hanieh, Timothy C. Havens, Pasi Lautala, Ezequiel Medici, and James Davis. "An automatic method for detecting sliding railway wheels and hot bearings using thermal imagery." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 231, no. 6 (March 22, 2016): 690–700. http://dx.doi.org/10.1177/0954409716638703.

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One of the most important safety-related tasks in the rail industry is an early detection of defective rolling stock components. Railway wheels and wheel bearings are the two components prone to damages due to their interactions with brakes and railway track, which makes them a high priority when the rail industry investigates improvements in the current detection processes. One of the specific wheel defects is a flat wheel, which is often caused by a sliding wheel during a heavy braking application. The main contribution of this paper is the development of a computer vision method for automatically detecting the sliding wheels from images taken by wayside thermal cameras. As a byproduct, the process will also include a method for detecting hot bearings from the same images. We first discuss our automatic detection and segmentation method, which identifies the wheel and bearing portion of the image. Then, we develop a method, using histogram of oriented gradients to extract the features of these regions. These feature descriptors are later employed by support vector machine to build a fast classifier with a good detection rate, which can detect abnormalities in the wheel. At the end, we train our algorithm using simulated images of sliding wheels and test it on several thermal images collected in a revenue service by the Union Pacific Railroad in North America.
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40

Sun, Yali, Hua Li, Xing Zhao, Jiyou Fei, Xiaodong Liu, and Yijie Niu. "A Novel Denoise Method of Acoustic Signal from Train Bearings Based on Resampling Technique and Improved Crazy Climber Algorithm." Shock and Vibration 2022 (March 24, 2022): 1–11. http://dx.doi.org/10.1155/2022/8303722.

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The wayside acoustic defective bearing detector system (TADS) is located on both sides of the railway, so that the acoustic signals recorded by the microphone not only include the sound from the train bearings but also include it from the other disturbance sources. The heavy noise and multisource acoustic signals would badly reduce the reliability and accuracy of the detection result of the TADS. In order to extract the useful information from the recorded signal exactly and efficiently, a novel denoising method based on the Short-time Fourier transform (STFT) and improved Crazy Climber algorithm was improved in this paper. Firstly, the STFT was performed on the recorded acoustic signals in order to obtain the time-frequency distribution matrix. Based on the original algorithm, the novel movement rule and the fitting process of the ridge lines were presented which could extract the time-frequency ridge lines of the acoustic signal accurately and rapidly. In this way, the important information from the train bearings could be divided from the heavy noise and other signals. Finally, the simulation and experimental verifications were carried out, and the denoising method based on the STFT and improved Crazy Climber algorithm has proved to be effective in extracting ridge lines of the time-frequency distribution matrix and dividing the useful information form the recorded acoustic signals.
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41

IGARASHI, Teruo, and Kiyokazu TAJIMA. "Studies on vibration and sound of defective rolling bearings (5th report, Diagnosing method of defects i a ball bearing equipped with machine)." Transactions of the Japan Society of Mechanical Engineers Series C 52, no. 473 (1986): 32–39. http://dx.doi.org/10.1299/kikaic.52.32.

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42

Gabrielli, Alberto, Mattia Battarra, Emiliano Mucchi, and Giorgio Dalpiaz. "A procedure for the assessment of unknown parameters in modeling defective bearings through multi-objective optimization." Mechanical Systems and Signal Processing 185 (February 2023): 109783. http://dx.doi.org/10.1016/j.ymssp.2022.109783.

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43

Patargias, T. I., C. T. Yiakopoulos, and I. A. Antoniadis. "Performance assessment of a morphological index in fault prediction and trending of defective rolling element bearings." Nondestructive Testing and Evaluation 21, no. 1 (March 2006): 39–60. http://dx.doi.org/10.1080/10589750600673568.

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44

Moazen Ahmadi, Alireza, Carl Q. Howard, and Dick Petersen. "The path of rolling elements in defective bearings: Observations, analysis and methods to estimate spall size." Journal of Sound and Vibration 366 (March 2016): 277–92. http://dx.doi.org/10.1016/j.jsv.2015.12.011.

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45

Vashishtha, Govind, and Rajesh Kumar. "Autocorrelation energy and aquila optimizer for MED filtering of sound signal to detect bearing defect in Francis turbine." Measurement Science and Technology 33, no. 1 (October 20, 2021): 015006. http://dx.doi.org/10.1088/1361-6501/ac2cf2.

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Abstract This paper presents a method to detect the bearing defects in Francis turbine by minimal entropy deconvolution (MED) filter making use of a sound signal. As the outputs of MED are mainly influenced by the filter length hence its appropriate selection is very necessary to recover a single random pulse in case of a weak faulty signal. The optimal filter length selection is done by Aquila optimizer adaptively which uses the autocorrelation energy as its fitness function. Experimentation done on defective bearings of Francis turbine suggested that the proposed method exposes periodic impulses effectively in case of a weak faulty signal or when the fault signal is embedded within the noise or interferences from other parts of Francis turbine. The proposed fault identification method has been compared with other models of MED such as particle swarm optimization -MED and maximum correlated kurtosis deconvolution. Results obtained reveals that the proposed method is superior in identifying the faulty signal embedded with heavy noise.
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46

Liu, Jing, and Shangkun Du. "Dynamic Analysis of a High-Speed Railway Train With the Defective Axle Bearing." International Journal of Acoustics and Vibration 25, no. 4 (December 30, 2020): 525–31. http://dx.doi.org/10.20855/ijav.2020.25.41701.

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Axle bearings (AXBs) are critical parts for high-speed railway trains (HSTs). Local faults in the AXBs have great influences on the operational dynamics of HSTs. Although some previous works formulated the local faults in single AXB, the vibrations of the whole train system with the defective AXB cannot be described. To overcome this problem, this study conducts a dynamic model for a HST considering a local fault in one AXB. The previous single AXB model cannot formulate the studied case. The impacts caused by the fault in the AXB is defined as a time-dependent force model considering a half-sine type. The road spectrum excitations from the roadbed and rail are formulated by a track irregularities model. The effects of the train speeds and fault sizes on the HST dynamics are introduced. The simulation results from the proposed and previous works are contrasted to show the model validation. The results show that the faults in the AXB will greatly affect the HST dynamics. It depicts that this study can afford a more reasonable approach for understanding the dynamics of HSTs considering the defective AXBs compared to the reported single AXB model.
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47

Petersen, Dick, Carl Howard, and Zebb Prime. "Varying stiffness and load distributions in defective ball bearings: Analytical formulation and application to defect size estimation." Journal of Sound and Vibration 337 (February 2015): 284–300. http://dx.doi.org/10.1016/j.jsv.2014.10.004.

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48

Moazen-ahmadi, Alireza, and Carl Q. Howard. "A defect size estimation method based on operational speed and path of rolling elements in defective bearings." Journal of Sound and Vibration 385 (December 2016): 138–48. http://dx.doi.org/10.1016/j.jsv.2016.09.014.

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49

Ruiz-Cárcel, C., E. Hernani-Ros, Y. Cao, and D. Mba. "Use of Spectral Kurtosis for Improving Signal to Noise Ratio of Acoustic Emission Signal from Defective Bearings." Journal of Failure Analysis and Prevention 14, no. 3 (March 20, 2014): 363–71. http://dx.doi.org/10.1007/s11668-014-9805-7.

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50

Hernández, Angela, Cristina Castejón, Juan Carlos García-Prada, Isidro Padrón, and Graciliano Nicolas Marichal. "Wavelet Packets Transform processing and Genetic Neuro-Fuzzy classification to detect faulty bearings." Advances in Mechanical Engineering 11, no. 8 (August 2019): 168781401983118. http://dx.doi.org/10.1177/1687814019831185.

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A great investment is made in maintenance of machinery in any industry. A big percentage of this is spent both in workers and in materials in order to prevent potential issues with said devices. In order to avoid unnecessary expenses, this article presents an intelligent method to detect incipient faults. Particularly, this study focuses on bearings due to the fact that they are the mechanical elements that are most likely to break down. In this article, the proposed method is tested with data collected from a quasi-real industrial machine, which allows for the measurement of the behaviour of faulty bearings with incipient defects. In a second phase, the vibrations obtained from healthy and defective pieces are processed with a multiresolution analysis with the purpose of extracting the most interesting characteristics. Particularly, a Wavelet Packets Transform processing is carried out. Finally, these parameters are used as Genetic Neuro-Fuzzy inputs; this way, once it has been trained, it will indicate whether the analyzed mechanical element is faulty or not.
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