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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.
Повний текст джерелаАнотація:
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.
2
Larizza, Francesco, Alireza Moazen-Ahmadi, Carl Q. Howard, and Steven Grainger. "The importance of bearing stiffness and load when estimating the size of a defect in a rolling element bearing." Structural Health Monitoring 18, no. 5-6 (October 25, 2018): 1527–42. http://dx.doi.org/10.1177/1475921718808805.
Повний текст джерелаАнотація:
The change in the static stiffness of a bearing assembly is an important discriminator when determining the size of a defect in a rolling element bearing. In this article, the force–displacement relationships for defective bearings under various static radial loadings at various cage angular positions are analytically estimated and experimentally measured and analyzed. The study shows that the applied load has a significant effect on the static stiffness variations in defective rolling element bearings. The experimental measurements of the effect of the defect size on the varying stiffness of the bearing assembly, which has not been shown previously, provides valuable knowledge for developing methods to distinguish between defective bearings with defects that are smaller or larger than one angular ball spacing. The methods and results presented here contribute to the wider experimental investigation of the effects of loadings on the varying static stiffness of defective bearings and its effects on the measured vibration signatures. A large data set was obtained and has been made publicly available.
3
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.
Повний текст джерелаАнотація:
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.
4
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.
Повний текст джерелаАнотація:
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.
5
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.
Повний текст джерелаАнотація:
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.
6
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.
Повний текст джерелаАнотація:
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.
7
Hu, Lei, Yuandong Xu, Fengshou Gu, Jing He, Niaoqing Hu, and Andrew Ball. "Autocorrelation Ensemble Average of Larger Amplitude Impact Transients for the Fault Diagnosis of Rolling Element Bearings." Energies 12, no. 24 (December 12, 2019): 4740. http://dx.doi.org/10.3390/en12244740.
Повний текст джерелаАнотація:
Rolling element bearings are one of the critical elements in rotating machinery of energy engineering systems. A defective roller of bearing moves in and out of the load zone during each revolution of the cage. Larger amplitude impact transients (LAITs) are produced when the defective roller passes the load zone centre and the defective area strikes the inner or outer races. A series of LAIT segments with higher signal to noise ratio are separated from a continuous vibration signal according to the bearing geometry and kinematics. In order to eliminate the phase errors between different LAIT segments that can arise from rotational speed fluctuations and roller slippages, unbiased autocorrelation is introduced to align the phases of LAIT segments. The unbiased autocorrelation signals make the ensemble averaging more accurate, and hence, archive enhanced diagnostic signatures, which are denoted as LAIT-AEAs for brevity. The diagnostic method based on LAIT separation and autocorrelation ensemble average (AEA) is evaluated with the datasets captured from real bearings of two different experiment benches. The validation results of the LAIT-AEAs are compared with the squared envelope spectrums (SESs) yielded based on two state-of-the-art techniques of Fast Kurtogram and Autogram.
8
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.
Повний текст джерелаАнотація:
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.
9
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.
Повний текст джерелаАнотація:
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.
10
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.
Повний текст джерелаАнотація:
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.
11
Shi, Juanjuan, and Ming Liang. "A fractal-dimension-based envelope demodulation for rolling element bearing fault feature extraction from vibration signals." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 18 (August 8, 2016): 3194–211. http://dx.doi.org/10.1177/0954406215608894.
Повний текст джерелаАнотація:
Vibration analysis has been extensively used as an effective tool for bearing condition monitoring. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault feature (i.e. fault-induced impulses), periodic interferences from other mechanical/electrical components, and background noise. The incipient impulses which excite as well as modulate the resonance frequency of the system are easily masked by compounded effects of periodic interferences and noise, making it challenging to do a reliable fault diagnosis. As such, this paper proposes an envelope demodulation method termed short time fractal dimension (STFD) transform for fault feature extraction from such vibration signal mixture. STFD transform calculation related issues are first addressed. Then, by STFD, the original signal can be quickly transformed into a STFD representation, where the envelope of fault-induced impulses becomes more pronounced whereas interferences are partly weakened due to their morphological appearance differences. It has been found that the lower the interference frequency, the less effect the interference has on STFD representations. When interference frequency keeps increasing, more effects on STFD representations will be resulted. Such effects can be reduced by the proposed kurtosis-based peak search algorithm (KPSA). Therefore, bearing fault signature is kept and interferences are further weakened in the STFD-KPSA representation. The proposed method has been favourably compared with two widely used enveloping methods, i.e. multi-morphological analysis and energy operator, in terms of extracting impulse envelopes from vibration signals obscured by multiple interferences. Its performance has also been examined using both simulated and experimental data.
12
Patel, V. N., N. Tandon, and R. K. Pandey. "Experimental Study for Vibration Behaviors of Locally Defective Deep Groove Ball Bearings under Dynamic Radial Load." Advances in Acoustics and Vibration 2014 (May 18, 2014): 1–7. http://dx.doi.org/10.1155/2014/271346.
Повний текст джерелаАнотація:
Rolling element bearings are used in many mechanical systems at the revolute joints for sustaining the dynamic loads. Thus, the reliable and efficient functioning of such systems critically depends on the good health of the employed rolling bearings. Hence, health monitoring of rolling bearings through their vibration responses is a vital issue. In this paper, an experimental investigation has been reported related to the vibration behaviours of healthy and locally defective deep groove ball bearings operating under dynamic radial load. The dynamic load on the test bearings has been applied using an electromechanical shaker. The vibration spectra of the healthy and defective deep groove ball bearings in time and frequency domains have been compared and discussed. Overall vibration increases in presence of local defects and dynamic radial load.
13
Kong, Fanzhao, Wentao Huang, Yunchuan Jiang, Weijie Wang, and Xuezeng Zhao. "Research on effect of damping variation on vibration response of defective bearings." Advances in Mechanical Engineering 11, no. 3 (March 2019): 168781401982773. http://dx.doi.org/10.1177/1687814019827733.
Повний текст джерелаАнотація:
This article presents a dynamic model of ball bearings with a localised defect on the outer raceway to analyse the effect of damping variation on the vibration response of defective bearings. First, a dynamic model is built based on the Hertzian contact theory, the interaction between the bearing inner ring, outer ring, and rolling element; the effect of damping and stiffness is considered; and the vibration equation of the bearing system is solved by the fourth-order Runge–Kutta algorithm. Then, the damping ratios of the experimental bearings using different types of viscosity lubricating grease are measured and compared with the damping ratios of the dynamic model; in addition, the viscous damping coefficient of the experimental bearings are calculated. Finally, the numerical analysis and experimental results show that the grease with a different level of viscosity affects the vibration signal of the defective bearing.
14
Yadav, H. K., D. H. Pandya, and S. P. Harsha. "Nonlinear vibration signature analysis of a rotor supported ball bearings." International Journal of Nonlinear Dynamics and Control 1, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijndc.2017.083626.
Повний текст джерела
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Pandya, D. H., H. K. Yadav, and S. P. Harsha. "Nonlinear vibration signature analysis of a rotor supported ball bearings." International Journal of Nonlinear Dynamics and Control 1, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijndc.2017.10004226.
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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.
Повний текст джерелаАнотація:
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.
17
Veselovska, Nataliya, Serhiy Shargorodskiy, Bohdan Bratslavets, and Olha Yalina. "RESEARCH OF FEATURES OF DEVELOPMENT OF BEARING DEFECTS ON THE BASIS OF WAVELET ANALYSIS." ENGINEERING, ENERGY, TRANSPORT AIC, no. 4(111) (December 18, 2020): 5–13. http://dx.doi.org/10.37128/2520-6168-2020-4-1.
Повний текст джерелаАнотація:
Today the vibrodiagnostic method achieves the highest efficiency and manufacturability for the operation of the technical condition of the technological equipment of the agro-industrial complex. At the same time, this method is one of the most modern methods of technical diagnostics, indicating the kinematic warehouses of diagnostic objects. Vibration analysis is a fundamental tool for diagnostic monitoring of bearings. The vibration signal of defective rolling bearings and its spectrum contain characteristic features by which it is possible to fairly correctly identify the type and location of the defect. At the moment the defective element passes through the loaded zone of the rolling bearing, a pronounced peak, an energy impulse, appears in the vibration. Thus, when a bearing with internal defects is operating, characteristic components appear in vibration - harmonics with natural frequencies, the numerical values of which can be calculated using theoretical formulas using the geometric dimensions of the bearing elements and the rotor speed of the mechanism. In a loaded bearing, four characteristic frequencies can be distinguished that are used for diagnostics - the frequency of the outer bearing cage, the frequency of the inner cage, the cage frequency and the rolling element frequencies. The complexity of the analysis of vibration signals of rolling bearings for the purpose of their diagnostics lies in the fact that the signs of a defective bearing are distributed over a wide range of frequencies, have low vibrational energy and are somewhat random in nature. In addition, the vibration signal is, of course, removed from the body of the equipment containing the bearing, and therefore contains not only information useful from the point of view of bearing diagnostics, but also noise - vibrations produced by other parts of the mechanism. The analysis of methods for diagnosing bearing defects based on wavelet analysis of their vibration signals allows us to single out the most promising direction, which consists in the fact that the bearing vibration signal is decomposed into coefficients using wavelet analysis, after which the most significant coefficients are selected from these coefficients.
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Zuber, Ninoslav, and Dragan Cvetkovic. "Rolling Element Bearings Fault Identification in Rotating Machines, Existing Methods of Vibration Signal Processing Techniques and Practical Considerations." Applied Mechanics and Materials 430 (September 2013): 70–77. http://dx.doi.org/10.4028/www.scientific.net/amm.430.70.
Повний текст джерелаАнотація:
This paper addresses the suitability of vibration monitoring and analysis techniques to detect different types of defects in roller element bearings. Processing techniques are demonstrated on signals acquired from the test rig with defective bearings. As a result it is shown that there is no reliable universal method for bearing failure monitoring from its early occurence up to bearings failure. Two real life case studies with different types of bearing failures are presented with practical considerations on methods used for failure identification.
19
Su, Y.-T., Y.-T. Sheen, and M.-H. Lin. "Signature Analysis of Roller Bearing Vibrations: Lubrication Effects." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 206, no. 3 (May 1992): 193–202. http://dx.doi.org/10.1243/pime_proc_1992_206_115_02.
Повний текст джерелаАнотація:
This study investigates the vibration signature of roller bearings, induced by the surface irregularities of components, under various lubricating conditions. The bearing vibration is modelled as the output of the bearing assembly which is subjected to the excitations of surface irregularities through the oil-film. The oil-film acts as a spring between the roller and race. The stiffness of oil-film under different lubricating conditions is studied from the empirical equation of minimum oil-film thickness. It is shown that the vibration spectra of a normal roller bearing may have a pattern of equal frequency spacing distribution (EFSD) whose frequency information is similar to that of a damaged bearing. Under large loading and low running speed, the vibration energy is low if the lubricant viscosity is high. On the other hand, at high running speed, the vibration energy is high with high lubricant viscosity.
20
Escaler, Xavier, and Toufik Mebarki. "Full-Scale Wind Turbine Vibration Signature Analysis." Machines 6, no. 4 (December 7, 2018): 63. http://dx.doi.org/10.3390/machines6040063.
Повний текст джерелаАнотація:
A sample of healthy wind turbines from the same wind farm with identical sizes and designs was investigated to determine the average vibrational signatures of the drive train components during normal operation. The units were variable-speed machines with three blades. The rotor was supported by two bearings, and the drive train connected to an intermediate three-stage planetary/helical gearbox. The nominal 2 MW output power was regulated using blade pitch adjustment. Vibrations were measured in exactly the same positions using the same type of sensors over a six-month period covering the entire range of operating conditions. The data set was preliminary validated to remove outliers based on the theoretical power curves. The most relevant frequency peaks in the rotor, gearbox, and generator vibrations were detected and identified based on averaged power spectra. The amplitudes of the peaks induced by a common source of excitation were compared in different measurement positions. A wind speed dependency of broadband vibration amplitudes was also observed. Finally, a fault detection case is presented showing the change of vibration signature induced by a damage in the gearbox.
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Choy, F. K., J. Zhou, M. J. Braun, and L. Wang. "Vibration Monitoring and Damage Quantification of Faulty Ball Bearings." Journal of Tribology 127, no. 4 (July 5, 2005): 776–83. http://dx.doi.org/10.1115/1.2033899.
Повний текст джерелаАнотація:
More often than not, the rolling element bearings in rotating machinery are the mechanical components that are first prone to premature failure. Early warning of an impending bearing failure is vital to the safety and reliability of high-speed turbomachinery. Presently, vibration monitoring is one of the most applied procedures in on-line damage and failure monitoring of rolling element bearings. This paper presents results from an experimental rotor-bearing test rig with quantified damage induced in the supporting rolling element bearings. Both good and damaged radial and tapered ball bearings are used in this study. The vibration signatures due to damage at the ball elements and the inner race of the bearing are also examined. Vibration signature analyzing schemes such as frequency domain analysis, and chaotic vibration analysis (modified Poincare diagrams) are applied and their effectiveness in pinpoint damage are compared in this study. The size/level of the damage is corroborated with the vibration amplitudes to provide quantification criteria for bearing progressive failure prediction. Based on the results from this study, it is shown that the use of the modified Poincare map, based on the relative carrier speed, can provide an effective way for identification and quantification of bearing damage in rolling element bearings.
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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.
Повний текст джерелаАнотація:
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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Jamaludin, N., D. Mba, and R. H. Bannister. "Condition monitoring of slow-speed rolling element bearings using stress waves." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 215, no. 4 (November 1, 2001): 245–71. http://dx.doi.org/10.1177/095440890121500401.
Повний текст джерелаАнотація:
Condition monitoring of rolling element bearings through the use of vibration analysis is an established technique for detecting early stages of component degradation. However, this success is not mirrored at rotational speeds below 16r/min. At such speeds the energy generated from bearing defects might not show as an obvious change in signature and thus becomes undetectable using conventional vibration measuring equipment. This paper presents an investigation into the applicability of stress wave analysis for detecting early stages of bearing damage at a rotational speed of 1.12r/min (0.0187 Hz). Furthermore, it reviews work undertaken in monitoring bearings rotating at speeds below 16r/min.
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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.
Повний текст джерелаАнотація:
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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Upadhyay, S. H., S. P. Harsha, and S. C. Jain. "Vibration signature analysis of high speed unbalanced rotating shaft supported on ball bearings." International Journal of Design Engineering 2, no. 2 (2009): 191. http://dx.doi.org/10.1504/ijde.2009.028651.
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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.
Повний текст джерелаАнотація:
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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Elia, GD, M. Cocconcelli, E. Mucchi, and G. Dalpiaz. "Combining blind separation and cyclostationary techniques for monitoring distributed wear in gearbox rolling bearings." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 6 (August 9, 2016): 1113–28. http://dx.doi.org/10.1177/0954406216636165.
Повний текст джерелаАнотація:
This work seeks to study the potential effectiveness of the Blind Signal Extraction (BSE) as a pre-processing tool for the detection of distributed faults in rolling bearings. In the literature, most of the authors focus their attention on the detection of incipient localized defects. In that case, classical techniques (i.e. envelope analysis) are robust in recognizing the presence of the fault and its characteristic frequency. However, when the fault grows, the classical approach fails, due to the change of the fault signature. De facto, in this case the signal does not contain impulses at the fault characteristic frequency, but more complex components with strong non-stationary contents. Moreover, signals acquired from complex machines often contain contributions from several different components as well as noise; thus the fault signature can be hidden in the complex system vibration. Therefore, pre-processing tools are needed in order to extract the bearing signature, from the raw system vibration. In this paper the authors focalize their attention on the application of the BSE in order to extract the bearing signature from the raw vibration of mechanical systems. The effectiveness and sensitivity of BSE is here exploited on the basis of both simulated and real signals. Among different procedures for the BSE computation, the Reduced-Rank Cyclic Regression algorithm (RRCR) is used. Firstly a simulated signal including the effect of gear meshing as well as a localized fault in bearings is introduced in order to tune the parameters of the RRCR. Next, two different real cases are considered, a bearing test-rig as an example of simple machine and a gearbox test-rig as an example of complex machine. In both examples, the bearings were degreased in order to accelerate the wear process. The BSE is compared with the usual pre-processing technique for the analysis of cyclostationary signals, i.e. the extraction of the residual signal. The fault detection is carried out by the computation of the Integrated Cyclic Modulation Spectrum on the extracted signals. The results indicate that the extracted signals via BSE clearly highlight the distributed fault signature, in particular both the appearance of the faults as well as their development are detected, whilst noise still hides fault grow in the residual signals.
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Deák, Krisztián, and Imre Kocsis. "Support Vector Machine with Wavelet Decomposition Method for Fault Diagnosis of Tapered Roller Bearings by Modelling Manufacturing Defects." Periodica Polytechnica Mechanical Engineering 61, no. 4 (September 29, 2017): 276. http://dx.doi.org/10.3311/ppme.10802.
Повний текст джерелаАнотація:
Tapered roller element bearings are generally applied in machines and transmission gearboxes. In manufacturing outer ring, inner ring and the rollers usually suffer damages. It is a challenging task to reveal and classify the defects. This paper presents an efficient method for fault classification by support vector machines. The faults on the bearing parts created by laser beam machine have similar shape and surface topography as the grinding faults from the manufacturing process. Vibration signature is collected by sensitive transducer and high resolution data acquisition unit. A test-rig is constructed to model the circumstances of the operation of the built-in tapered roller bearings. Moreover, test-rig is planned with the aim to mitigate the harmful vibration components from the environment that influence the precision of the vibration measurement. Feature extraction is executed by wavelet decomposition. Decomposition level is determined by FFT considering the structural frequencies of the bearing elements. The proper wavelet is selected by the Energy-to-Shannon Entropy criteria from Daubechies and Symlet wavelet families. The fault classification is done by R Cran software using support vector machine classifiers. Time domain parameters of the vibration signature such as kurtosis, skewness, crest factor and range are provided to the classifier. Classification rates are high enough to ensure the efficiency of the method in all cases in the study.
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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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Al-Najjar, B. "Improved effectiveness of vibration monitoring of rolling bearings in paper mills." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 212, no. 2 (February 1, 1998): 111–20. http://dx.doi.org/10.1243/1350650981541930.
Повний текст джерелаАнотація:
Rolling element bearing failures in paper mill machines are considered in relation to their critical role in the machine function. The paper discusses these failures according to what becomes damaged and how, and relates them to the vibration spectra and their development over the lives of the bearings. Interpretations of some variations in the vibration signature, i.e. relating vibration amplitude changes and frequency shifts to the deterioration processes involved, are proposed and discussed. The literature was found mainly to confirm this analysis. A new approach to envelope alarming is presented and shown theoretically (logically) to offer later renewal with fewer failures, and therefore lower cost and higher productivity. Deficiencies in data coverage and quality, and the feedback of case study results, are discussed. A model to improve maintenance experience is proposed and discussed. Using vibration to monitor component conditions, the accurate prediction of remaining life requires (a) enough vibration measurements, (b) numerate records of operating conditions, (c) better discrimination between frequencies in the spectrum and (d) correlation of (b) and (c). This is because life prediction depends on the amplitudes of (and) the frequencies generated by the component damage. Much money could be saved because some of the present policies utilize as little as half of the useful life of a bearing.
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Shen, Chia-Hsuan, Jie Wen, Pirapat Arunyanart, and Fred K. Choy. "Vibration Signature Analysis and Parameter Extractions on Damages in Gears and Rolling Element Bearings." ISRN Mechanical Engineering 2011 (September 6, 2011): 1–10. http://dx.doi.org/10.5402/2011/402928.
Повний текст джерелаАнотація:
This paper is to analyze and identify damage in gear teeth and rolling element bearings by establishing pattern feature parameters from vibration signatures. In the present work, different damage scenarios involving different combinations of gear tooth damage, bearing damage are considered. Each of the damage scenarios are studied and compared in the time domain, the frequency domain, and the joint time-frequency domain using the FM0 technique, the Fourier Transform, the Wigner-Ville Transform, and the Continuous Wavelet Transform, respectively. Results obtained from the three different signal domains are analyzed to develop indicative parameters and visual presentations that measure the integrity and wellness of the bearing and gear components. The joint time-frequency domain obtained from the continuous wavelet transform has shown to be a superior technique for providing clear visual examination solution for different types of component damages as well as for feature extractions used for computer-based machine health monitoring solution.
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Zhen, Dong, Junchao Guo, Yuandong Xu, Hao Zhang, and Fengshou Gu. "A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis." Sensors 19, no. 18 (September 16, 2019): 3994. http://dx.doi.org/10.3390/s19183994.
Повний текст джерелаАнотація:
To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.
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Antoni, J., and R. B. Randall. "A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults." Journal of Vibration and Acoustics 125, no. 3 (June 18, 2003): 282–89. http://dx.doi.org/10.1115/1.1569940.
Повний текст джерелаАнотація:
This paper addresses the stochastic modeling of the vibration signal produced by localized faults in rolling element bearings and its use for diagnostic purposes. The aim is essentially to provide a better understanding of the recognized “envelope analysis” technique as classically used in the diagnostics of rolling element bearings, and incidentally give theoretical proofs for the specific features of envelope spectra as obtained from experimental data. The proposed model may also prove useful for simulation purposes. First, the excitation force generated by a defect is modeled as a random point process and its spectral signature is derived analytically. Then its transmission through the bearing is investigated in detail in order to find the spectral characteristics of the resulting vibration signal. The analysis finally gives sound justification for “squared” envelope analysis and the type of spectral indicators that should be used with it.
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Kulkarni, SS, AK Bewoor, and RB Ingle. "Vibration signature analysis of distributed defects in ball bearing using wavelet decomposition technique." Noise & Vibration Worldwide 48, no. 1-2 (January 2017): 7–18. http://dx.doi.org/10.1177/0957456517698318.
Повний текст джерелаАнотація:
The analysis of vibration signals acquired from a ball bearing with an extended type of distributed defects is carried out using wavelet decomposition technique. The influence of artificially generated defect and its location on outer and inner race of the ball bearing is observed using vibration data acquired from bearing housing. The comparison of diagnostic information from fast Fourier transform and time frequency decomposition method is made for inner and outer race of ball bearing with single as well as multiple extended defects. To decompose vibration signal acquired from bearing, db04 wavelet technique was implemented. It is observed that impulses appear with a time period corresponding to characteristic defect frequencies. The results observed from wavelet decomposition technique and fast Fourier transform reveal that the characteristic defect frequency is quite consistent even with change in location of defect. The extended type of distributed defects in the ball bearings can also be effectively diagnosed with the help of wavelet decomposition technique and fast Fourier transform.
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J. Jayakanth, J., and M. Chandrasekaran. "Virtual Instrumentation Programming and Psoc Embedded Design for Bearing Fault Detection: Uses Impulse Excitation Technique." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 170. http://dx.doi.org/10.14419/ijet.v7i3.27.17753.
Повний текст джерелаАнотація:
This paper describes, a novel innovative design for generating vibration signals in a bearing, which is in static mode not in dynamic mode like other usual measurements reported in literatures, for various types of bearing faults analysis supports users with a technique based on monitoring vibration signals. This paper reports the computed power spectrum from vibration signal clearly identifies the fault signature with its raise in amplitude. Hence, in an impulse excitation technique vibration analysis with computed power spectrum provides an efficient monitoring of fault in axle bearings in a short timing. Programmable System on Chip (PSoC) creator design and Virtual Instrument program written in LabVIEW, a graphical language, provides efficient implementation of impulse excitation technique possible in static test method in a minimal time.
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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.
Повний текст джерелаАнотація:
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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Yang, Jiang Tian, Wen Yu Zhao, and Jay Lee. "Stator Current-Based Locomotive Traction Motor Bearing Fault Detection." Advanced Materials Research 819 (September 2013): 186–91. http://dx.doi.org/10.4028/www.scientific.net/amr.819.186.
Повний текст джерелаАнотація:
Rolling-element bearings are critical components in locomotive traction motors. A reliable online bearing fault-diagnostic technique is critically needed to prevent motor systems performance degradation and malfunction. Motor bearing failure induces vibration, resulting in the modulation of the stator current. Compared with conventional monitoring techniques such as vibration monitoring or temperature monitoring, stator current-based monitoring offers significant economic benefits and implementation advantages. In this paper, a novel approach to locomotive traction motor current signature analysis based on wavelet packet decomposition (WPD) of stator current is presented. The effectiveness and practicability of the proposed method is verified by locomotive running tests.
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Tong, Zhe, Wei Li, Fan Jiang, Zhencai Zhu, and Gongbo Zhou. "Bearing fault diagnosis based on spectrum image sparse representation of vibration signal." Advances in Mechanical Engineering 10, no. 9 (September 2018): 168781401879778. http://dx.doi.org/10.1177/1687814018797788.
Повний текст джерелаАнотація:
Bearings are crucial for industrial production and susceptible to malfunction in rotating machines. Image analysis can give a comprehensive description of vibration signal, thus, it has achieved much more attention recently in fault diagnosis field. However, it brings lots of redundant information from a single spectrum image matrix behind rich fault information, and massive spectrum image samples lead to exacerbation of this situation, which readily results in the accuracy-dropping problem of multiple local defective bearings diagnosis. To solve this issue, a novel feature extraction method based on image sparse representation is proposed. Original spectrum images are acquired through fast Fourier transformation. Sparse coefficient that reveals the underlying structure of spectrum image based on raw signals is extracted as the feature by implementing the orthogonal matching pursuit and K-singular value decomposition algorithm strategically, and then two-dimensional principal component analysis is applied for further processing of these features. Finally, fault types are identified based on a minimum distance strategy. The experimental results are given to demonstrate the effectiveness of the proposed method.
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Viana, Carlos Alberto Alves, Diogo Stuani Alves, and Tiago Henrique Machado. "Linear and Nonlinear Performance Analysis of Hydrodynamic Journal Bearings with Different Geometries." Applied Sciences 12, no. 7 (March 22, 2022): 3215. http://dx.doi.org/10.3390/app12073215.
Повний текст джерелаАнотація:
In rotor dynamics, a traditional way of representing the dynamics of hydrodynamic bearings is using stiffness/damping coefficients. It is thus necessary to carry out a linearization of hydrodynamic forces around the shaft’s equilibrium position. However, hydrodynamic bearings have highly nonlinear nature, depending on operating conditions. Therefore, this paper discusses the applicability of these linear/nonlinear approaches using a computational model of the rotating system, where the finite element method is used for rotor modelling and the finite volume method for bearing calculation. The main goal is to investigate the boundaries for linear approximation of the hydrodynamic forces present in lobed hydrodynamic bearings, with the system operating under high loading conditions. Several numerical simulations were performed varying preload parameter and rotating speed. A comparison of the system’s responses, in time domain (shaft orbits) and frequency domain (full spectrum), is made for linear and nonlinear models. Results showed that trilobed bearings are more susceptible to nonlinearities, even in situations of smaller vibration amplitudes, while elliptical bearings are sensitive only under larger vibration amplitudes. These analyses are of great importance for mapping the influence of nonlinearities in different types of lobed hydrodynamic bearings with fixed geometry, varying the preload parameter to verify the influence on the system’s dynamic response. This study is important and serves as the basis for cases of monitoring and fault diagnosis (in the field of structural health monitoring) since it is crucial to distinguish what would be a fault signature or a standard nonlinear effect created by the use of hydrodynamic bearings.
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Fang, Bo, Jianzhong Hu, Cheng Yang, Yudong Cao, and Minping Jia. "A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis." Measurement Science and Technology 33, no. 2 (December 17, 2021): 025009. http://dx.doi.org/10.1088/1361-6501/ac3fc7.
Повний текст джерелаАнотація:
Abstract Blind deconvolution (BD) is an effective algorithm for enhancing the impulsive signature of rolling bearings. As a convex optimization problem, the existing BDs have poor optimization performance and cannot effectively enhance the impulsive signature excited by weak faults. Moreover, the existing BDs require manual derivation of the calculation process, which brings great inconvenience to the researcher’s personalized design of the maximization criterion. A new BD algorithm based on backward automatic differentiation is proposed, which is named backward automatic differentiation blind deconvolution (BADBD). The calculation process does not require manual derivation so a general solution of BDs based on different maximization criteria is realized. BADBD constructs multiple cascaded filters to filter the raw vibration signal, which makes up for the deficiency of single filter performance. The filter coefficients are determined by Adam algorithm, which improves the optimization performance of the proposed BADBD. BADBD is compared with classic BDs by synthesized and real vibration signals. The results reveal superior capability of BADBD to enhance the impulsive signature and the fault diagnosis performance is significantly better than the classic BDs.
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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.
Повний текст джерелаАнотація:
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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Forest, Florent, Quentin Cochard, Cecile Noyer, Marc Joncour, Jérôme Lacaille, Mustapha Lebbah, and Hanene Azzag. "Large-scale Vibration Monitoring of Aircraft Engines from Operational Data using Self-organized Models." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 11. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1131.
Повний текст джерелаАнотація:
Vibration analysis is an important component of industrial equipment health monitoring. Aircraft engines in particular are complex rotating machines where vibrations, mainly caused by unbalance, misalignment, or damaged bearings, put engine parts under dynamic structural stress. Thus, monitoring the vibratory behavior of engines is essential to detect anomalies and trends, avoid faults and improve availability. Intrinsic properties of parts can be described by the evolution of vibration as function of rotation speed, called a vibration signature. This work presents a methodology for large-scale vibration monitoring on operating civil aircraft engines, based on unsupervised learning algorithms and a flight recorder database. Firstly, we present a pipeline for massive extraction of vibration signatures from raw flight data, consisting in time-domain medium-frequency sensor measurements. Then, signatures are classified and visualized using interpretable self-organized clustering algorithms, yielding a visual cartography of vibration profiles. Domain experts can then extract various insights from resulting models. An abnormal temporal evolution of a signature gives early warning before failure of an engine. In a post-finding situation after an event has occurred, similar at-risk engines are detectable. The approach is global, end-to-end and scalable, which is yet uncommon in our industry, and has been tested on real flight data.
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Corrêa, Márcio Pereira, Ayslan Cuzzuol Machado, João Inácio da Silva Filho, Dorotéa Vilanova Garcia, Mauricio Conceição Mario, and Carlos Teofilo Salinas Sedano. "Paraconsistent annotated logic applied to industry assets condition monitoring and failure prevention based on vibration signatures." Research, Society and Development 11, no. 1 (January 3, 2022): e14211125104. http://dx.doi.org/10.33448/rsd-v11i1.25104.
Повний текст джерелаАнотація:
In this study, we introduced an expert system (ESvbrPAL2v), responsible for monitoring assets based on vibration signature analysis through a set of algorithms based on the Paraconsistent Annotated Logic – PAL. Being a non-classical logic, the main feature of the PAL is to support contradictory inputs in its foundation. It is therefore suitable for building algorithmic models capable of performing out appropriate treatment for complex signals, such as those coming from vibration. The ESvbrPAL2v was built on an ATMega2560 microcontroller, where vibration signals were captured from the mechanical structures of the machines by sensors and, after receiving special treatment through the Discrete Fourier Transform (DFT), then properly modeled to paraconsistent logic signals and vibration patterns. Using the PAL fundamentals, vibration signature patterns were built for possible and known vibration issues stored in ESvbrPAL2v and continuously compared through configurations composed by a network of paraconsistent algorithms that detects anomalies and generate signals that will report on the current risk status of the machine in real time. The tests to confirm the efficiency of ESvbrPAL2v were performed in analyses initially carried out on small prototypes and, after the initial adjustments, tests were carried out on bearings of a group of medium-power motor generators built specifically for this study. The results are shown at the end of this study and have a high index of signature identification and risk of failure detection. These results justifies the method used and future applications considering that ESvbrPAL2v is still in its first version.
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Kramti, Sharaf Eddine, Jaouher Ben Ali, Eric Bechhoefer, Karim Takrouni, Abdelghani Darghouthi, and Mounir Sayadi. "Toward an online strategy for mechanical failures diagnostics inside the wind turbine generators based on spectral analysis." Wind Engineering 45, no. 4 (July 12, 2021): 782–92. http://dx.doi.org/10.1177/0309524x211028759.
Повний текст джерелаАнотація:
Mechanical faults in wind turbine generators lead to a huge problems of breaking electricity production, increase the maintenance cost and can damage the wind turbine generator, which caught fire in the nacelle. Gearbox installed in the nacelle is the important component in wind turbine, it composed by shafts bearings and gearings. Always bearings and gearings failures are related together. Therefore, to control these rotating components we need to analysis their vibration signature. This work was done with Tunisian Electricity and Gas Company (STEG) it’s about mechanical failures diagnostics which are based on three temporal vibration signals which are made by three sensors installed also in three positions axial, vertical, and horizontal. In this work we present a Fast Fourier Transform which is applied on raw mechanical vibration signals by the vibration department of STEG, which are compared with a new strategy of envelope analysis is commonly used to obtain the mechanical faults harmonics from the envelope signal spectrum analysis and has shown a more suitable results of diagnostics which is applied on the same data sets from Fast Fourier Transform. In this work, we illustrate a squared envelope based on spectral kurtosis approach to determine optimum envelope analysis parameters including the filtering band and center frequency through a short time Fourier transform. This method proves a better diagnosis results using real vibration data set from vibration department of STEG.
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Sahoo, Sudarsan, and Jitendra K. Das. "Application of Adaptive Wavelet Transform for Gear Fault Diagnosis Using Modified-LLMS Based Filtered Vibration Signal." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 12, no. 3 (June 10, 2019): 257–62. http://dx.doi.org/10.2174/2352096511666180525123616.
Повний текст джерелаАнотація:
Background: Vibration signature acquired from a gear mesh can be used to identify the defect present in a gear mesh hence can be used to diagnose the condition of a gear mesh. But the signal acquired from the subject may not be noise free and may be non stationary. Methods: Before going for the analysis of the acquired signal a preprocessing on the acquired signal is required to make it noise free. In the present work in first phase, the acquired vibration signal is filtered to reduce the noise and to improve the SNR (signal to noise ratio). The filtering is done by an Adaptive Noise Cancellation (ANC) technique. A modified Leaky Least Mean Square (LLMS) based adaptive algorithm along with a digital filter is used to achieve the ANC. The signal acquired from a healthy gear is used as the reference signal for the adaptive filter based de-noising process. In the second phase of the present work Adaptive Wavelet Transform (AWT) is used to detect the fault by extracting the features from the filtered vibration signal. From the signal pattern the adaptive wavelet is designed. The adaptive wavelet scalogram is compared with the standard wavelet scalogram. Results: The result shows that the adaptive wavelet scalogram is better in analyzing the vibration signal. In this work a gear drive experimental set-up is made. Two different types of defective gears are used for the experiment. In type-1 defective gear one tooth is broken and in type-2 defective gear two teeth are broken. Initially, the vibration signal is acquired from a healthy gear which is used as the reference signal. Then the vibration signal from type-1 defective gear and type-2 defective gear is acquired and processed for the analysis and to identify the defects. Conclusion: The present work shows that with the application of modified-LLMS algorithm and AWT the proposed technique of signal processing is more suitable for the fault identification and hence for the condition monitoring of the gear.
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Kankar, PK, Satish C. Sharma, and SP Harsha. "Vibration signature analysis of a high speed rotor supported on ball bearings due to localized defects." Journal of Vibration and Control 19, no. 12 (June 27, 2012): 1833–53. http://dx.doi.org/10.1177/1077546312448506.
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Buzzoni, Marco, Elia Soave, Gianluca D’Elia, Emiliano Mucchi, and Giorgio Dalpiaz. "Development of an Indicator for the Assessment of Damage Level in Rolling Element Bearings Based on Blind Deconvolution Methods." Shock and Vibration 2018 (December 16, 2018): 1–13. http://dx.doi.org/10.1155/2018/5384358.
Повний текст джерелаАнотація:
The monitoring of rolling element bearings through vibration-based condition indicators plays a crucial role in the modern machinery. The kurtosis is a very efficient indicator being sensitive to impulsive components within the vibration signature that often are symptomatic of localized faults. In order to improve the diagnostic performance of the kurtosis, blind deconvolution algorithms can be exploited in order to detect bearing faults and, most importantly, their position. In this scenario, this paper focuses on the development of a novel condition indicator specifically designed for the damage assessment in rolling element bearings. The proposed indicator allows to track the bearing degradation process taking into account three different possible positions: outer race, inner race, and rolling element. This indicator fits real-time monitoring procedures allowing for the automatic detection and identification of the bearing fault. The validation of the proposed indicator has been carried out by means of both simulated signal and a run-to-failure experiment. The results highlight that the proposed indicator is able to detect more efficiently the fault occurrence and, most importantly, quicker than other established techniques.
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Patra, Pravajyoti, V. Huzur Saran, and SP Harsha. "Vibration response analysis of high-speed cylindrical roller bearings using response surface method." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 234, no. 2 (March 9, 2020): 379–92. http://dx.doi.org/10.1177/1464419320910864.
Повний текст джерелаАнотація:
The operating clearance in a bearing influences friction, load zone size and fatigue life of a bearing. Hence, an effort is made to investigate the effect of radial internal clearance on the dynamical behavior of a cylindrical roller bearing system with an unbalance present in the system. The differential equations representing the dynamics of the cylindrical roller bearings have been derived using Lagrange’s equations and solved numerically using the fourth-order Runge-Kutta iterative method. The nonlinear vibration signature has been analyzed due to the clearance and the same is represented by various tools like Acceleration-time plots, Poincaré plots and FFT plots. The approximation method is used to calculate the load distribution and deformation of the individual rollers located at a different position in the load zone, for a preloading/interference fit and positive internal clearance. A response surface method is used to analyze the severity involved in the system due to the combined effect of independent variables like rotor speed, radial load, and radial internal clearance. The observations presented here are not only useful to diagnose the bearing health condition with respect to parametric effects but also exhibit their interactive effects on bearing performance.
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Zhao, H., F. K. Choy, and M. J. Braun. "Transient and Steady State Vibration Analysis of a Wavy Thrust Bearing." Journal of Tribology 128, no. 1 (June 22, 2005): 139–45. http://dx.doi.org/10.1115/1.2033900.
Повний текст джерелаАнотація:
This paper describes a numerical procedure for analyzing the dynamics of transient and steady state vibrations in a wavy thrust bearing. The major effects of the wavy geometry and the operating parameters on the dynamic characteristics of the bearing had been discussed in a previous paper; the present paper thus concentrates on examining the relationships between the development of the transient and steady state vibrations when operating conditions are parametrically varied. Special attention is given to the development of steady state vibrations from initial transients with comparisons and consequences to the overall system stability. Numerical based vibration signature analysis procedures are then used to identify and quantify the transient vibrations. The conclusions provide general indicators for designing wavy thrust bearings that are less susceptible to transients induced by external perturbations.
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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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