Journal articles on the topic 'Inner race bearing fault'
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1
Fei, Cheng-Wei, Yat-Sze Choy, Guang-Chen Bai, and Wen-Zhong Tang. "Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults." Structural Health Monitoring 17, no. 2 (January 24, 2017): 156–68. http://dx.doi.org/10.1177/1475921716687167.
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To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time–frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner–outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.
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Karyatanti, Iradiratu, Firsyaldo Purnomo, Ananda Noersena, Rafli Zulkifli, Nuddin Harahab, Ratno Wibowo, Agus Budiarto, and Ardik Wijayanto. "Sound analysis to diagnosis inner race bearing damage on induction motors using fast fourier transform." Serbian Journal of Electrical Engineering 20, no. 1 (2023): 33–47. http://dx.doi.org/10.2298/sjee2301033k.
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The induction motor is a type of electric machine that is widely used for industrial operations in this modern era. It is an alternating current electric machine with several advantages, namely cheap, simple construction, and not requiring excessive maintenance, but has the biggest percentage of motor fault in the bearings. Therefore, this study aims to identify the inner race-bearing fault detection system based on sound signal frequency analysis. The sound signal processing was carried out using the Fast Fourier Transform (FFT) algorithm to analyze the condition of the inner race-bearing. The sound signal was used because it does not require direct contact with the bearing (non-invasive). The fault detection system was tested with two defects, namely scratched inner race and perforated inner race bearing. The results gave a successful detection of the condition of the inner race bearing with a percentage of 81.24%. This showed that the fault detection system using sound signals with FFT signal processing was carried out with high accuracy.
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Wang, Hongchao. "Fault diagnosis of rolling element bearing compound faults based on sparse no-negative matrix factorization-support vector data description." Journal of Vibration and Control 24, no. 2 (March 10, 2016): 272–82. http://dx.doi.org/10.1177/1077546316637979.
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The bispectrum of rolling element bearing compound faults contains abundant fault characteristic information, and how to extract the fault feature effectively is a key problem. The fault diagnosis method of rolling element bearing compound faults based on Sparse No-Negative Matrix Factorization (SNMF)-Support Vector Data Description (SVDD) is proposed in the paper. The figure handling method SNMF is used firstly in fault feature extraction of the bispectrums of rolling element bearing different kinds of compound faults and the sparse coefficient matrices of the bispectrums are obtained. The sparse coefficient matrices are used as training and test input vectors of SVDD. At last, the three kinds of rolling element bearing compound faults (inner race outer race compound faults, outer race rolling element compound faults and inner race outer race rolling element compound faults) are classified correctly. In order to verify the advantages of the proposed method, the diagnosis results of the same three kinds of rolling element bearing compound faults based on No-Negative Matrix Factorization (NMF)-SVDD is used as comparison. The proposed method provides a new idea for fault diagnosis of rolling element bearing compound faults.
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Manjunatha, G., and H. C. Chittappa. "Bearing Fault Classification using Empirical Mode Decomposition and Machine Learning Approach." Journal of Mines, Metals and Fuels 70, no. 4 (June 20, 2022): 214. http://dx.doi.org/10.18311/jmmf/2022/30060.
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Industrial machinery often breakdowns due to faults in rolling bearing. Bearing diagnosis plays a vital role in condition monitoring of machinery. Operating conditions and working environment of bearings make them prone to single or multiple faults. In this research, signals from both healthy and faulty bearings are extracted and decomposed into empirical modes. By analyzing different empirical modes from 8 derived empirical modes for healthy and faulty bearings under different fault sizes, the first mode has the most information to classify bearing condition. From the first empirical mode eight features in time domain were calculated for various bearing conditions like healthy, rolling element fault, outer and inner race fault. The feature extraction of vibration signal based on Empirical Mode Decomposition (EMD) is extensively explored and applied in diagnosis of fault in rolling bearings. This paper presents mathematical analysis for selection of valid Intrinsic Mode Functions (IMFs) of EMD. These chosen features are trained and classified using different classifiers. Among them K-star classifier is most reliable to categorize the bearing defects.
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Sun, J., Gang Yu, and Chang Ning Li. "Bearing Fault Diagnosis Using Gaussian Mixture Models (GMMs)." Applied Mechanics and Materials 10-12 (December 2007): 553–57. http://dx.doi.org/10.4028/www.scientific.net/amm.10-12.553.
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This paper presents a novel method for bearing fault diagnosis based on wavelet transform and Gaussian mixture models (GMMs). Vibration signals for normal bearings, bearings with inner race faults, outer race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the vibration signals and to generate feature vectors. GMMs were trained and used as a diagnostic classifier. Experimental results have shown that GMMs can reliably classify different fault conditions and have a better classification performance as compared to the multilayer perceptron neural networks.
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Chen, Xiaohui, Lei Xiao, Xinghui Zhang, and Zhenxiang Liu. "A heterogeneous fault diagnosis method for bearings in gearbox." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, no. 8 (July 27, 2014): 1491–99. http://dx.doi.org/10.1177/0954406214544727.
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Bearing failure is one of the most important causes of breakdown of rotating machinery. These failures can lead to catastrophic disasters or result in costly downtime. One of the key problems in bearing fault diagnosis is to detect the bearing fault as early as possible. This capability enables the operator to have enough time to do some preventive maintenance. Most papers investigate the bearing faults under rational assumption that bearings work individually. However, bearings are usually working as a part of complex systems like a gearbox. The fault signal of bearings can be easily masked by other vibration generated from gears and shafts. The proposed method separates bearing signals from other signals, and then the optimum frequency band which the bearing fault signal is prominent is determined by mean envelope Kurtosis. Subsequently, the envelope analysis is used to detect the bearing faults. Finally, two bearing fault experiments are used to validate the proposed method. Each experiment contains two bearing fault modes, inner race fault and outer race fault. The results demonstrate that the proposed method can detect the bearing fault easier than spectral Kurtosis and envelope Kurtosis.
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Shi, Yuan Cheng, Yong Ying Jiang, Hai Feng Gao, and Jia Wei Xiang. "A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults." Applied Mechanics and Materials 548-549 (April 2014): 369–73. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.369.
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The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.
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Tian, Jing, Yan-Ting Ai, Cheng-Wei Fei, Feng-Ling Zhang, and Yat-Sze Choy. "Dynamic modeling and simulation of inter-shaft bearings with localized defects excited by time-varying displacement." Journal of Vibration and Control 25, no. 8 (January 29, 2019): 1436–46. http://dx.doi.org/10.1177/1077546318824927.
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To accurately describe the dynamic features of inter-shaft bearings with localized defect under operation, the dynamic model of inter-shaft bearing with localized defects was established with respect to time-varying displacement excitation. Based on fault simulations on a birotor experimental rig, the developed dynamic model of inter-shaft bearing is validated to have high accuracy (over 99%) when localized defects happen on inner and outer race with co- and counter-rotation, which indicates that the model can be adopted to simulate the faults of inter-shaft bearing instead of experiment. Through investigation of the square-root (SR) amplitudes of bearing vibration with different defect sizes, radial loads, and rotational directions, we find that the SR amplitudes of bearing vibration increase with increasing defect size and radial load for both co- and counter-rotation. The amplitudes of counter-rotation are larger than those of co-rotation for inner race and outer race, and the amplitude of inner race defect are larger than that of outer race defect for the same defect size or same radial load. This work reveals the SR variation of bearing vibration with localized surface defects under different defect sizes and radial loads, and accurately describes the dynamic characteristics of inter-shaft bearing with localized defects. The efforts of this study open a door to adopt a dynamic model in the future to evaluate and monitor the health condition of inter-shaft bearings in an aeroengine or other rotating machinery.
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Du, Jianxi, Lingli Cui, Jianyu Zhang, Jin Li, and Jinfeng Huang. "The Method of Quantitative Trend Diagnosis of Rolling Bearing Fault Based on Protrugram and Lempel–Ziv." Shock and Vibration 2018 (November 1, 2018): 1–8. http://dx.doi.org/10.1155/2018/4303109.
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This paper proposes a new method to realize the quantitative trend diagnosis of bearings based on Protrugram and Lempel–Ziv. Firstly, the fault features of original fault signals of bearing inner and outer race with different severity are extracted using Protrugram algorithm, and the optimal analysis frequency band is selected which reflects the fault characteristic. Then, the Lempel–Ziv complexity of the frequency band is calculated. Finally, the relationship between Lempel–Ziv complexity and fault size is obtained. Analysis results show that the severity of fault is proportional to the Lempel–Ziv complexity index value under different fault types. The Lempel–Ziv complexity showed different trend rules, respectively, in the inner and outer race, which realized the quantitative trend diagnosis of bearing faults.
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Jamil, Mohd Atif, and Sidra Khanam. "Fault Classification of Rolling Element Bearing in Machine Learning Domain." International Journal of Acoustics and Vibration 27, no. 2 (June 30, 2022): 77–90. http://dx.doi.org/10.20855/ijav.2022.27.21829.
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Rolling element bearings are crucial components of rotating machinery used in various industries, including aerospace, navigation, machine tools, etc. Therefore, it is essential to establish suitable techniques for condition monitoring and fault diagnosis of bearings to avoid breakdowns and damages during operation for overall industrial sustainability. Vibration-based condition monitoring has been the most employed technique in this perspective. Many researchers have investigated the vibration response of rolling element bearings having inner race defects, outer race defects, or rolling element defects using conventional techniques in past decades. However, Machine Learning (ML) has emerged as another way of bearing fault diagnosis. In this work, fault signatures of ball bearings are classified using a total of 6 (with 24 subcategories) ML models, and a comparative performance of these models is presented. The ML classifiers are trained with extracted time-domain and frequency-domain features using open-source Case Western Reserve University (CWRU) bearing data. Two datasets of different sample size and number of samples of vibration data corresponding to a healthy ball bearing, a defective bearing with inner race defect, a ball defect, and an outer race defect, running at a particular set of working conditions, are considered. The accuracy of ML models is compared to identify the best model for classifying the faults under consideration.
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Liu, Jing, Yimin Shao, and Xiaomeng Qin. "Dynamic simulation for a tapered roller bearing considering a localized surface fault on the rib of the inner race." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 231, no. 4 (February 22, 2017): 670–83. http://dx.doi.org/10.1177/1464419317695171.
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Vibration characteristics of a tapered roller bearing system will be significantly affected by a localized surface fault in the rib of the inner race. The operating condition for the tapered roller bearing system is always monitored to prevent serious failures from happening based on changes in the vibration characteristics. However, most of the previous works are focused on dynamic simulations for a localized surface fault in the race surface. A new dynamic simulation method for a tapered roller bearing with a localized surface fault on the rib of the inner race is proposed. The non-Hertzian contact of taper roller to races and rib is considered. The time-varying deflection excitation caused by the fault is formulated in the proposed method, as well as both the axial and radial contact deformation between the races and rollers. Effects of the axial load, radial load, and fault sizes on the vibration characteristics for the tapered roller bearing are analyzed. An experimental investigation is also developed to validate the proposed method. The results show that the proposed dynamic simulation method can formulate the vibration characteristics for the tapered roller bearing caused by the localized surface fault on the rib of the inner race, which may give some guidance for the tapered roller bearing condition monitoring and fault diagnosis, especially for the incipient localized surface fault.
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Ge, Mingtao, Jie Wang, Yicun Xu, Fangfang Zhang, Ke Bai, and Xiangyang Ren. "Rolling Bearing Fault Diagnosis Based on EWT Sub-Modal Hypothesis Test and Ambiguity Correlation Classification." Symmetry 10, no. 12 (December 7, 2018): 730. http://dx.doi.org/10.3390/sym10120730.
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Because of the cyclic symmetric structure of rolling bearings, its vibration signals are regular when the rolling bearing is working in a normal state. But when the rolling bearing fails, whether the outer race fault or the inner race fault, the symmetry of the rolling bearing is broken and the fault destroys the rolling bearing’s stable working state. Whenever the bearing passes through the fault point, it will send out vibration signals representing the fault characteristics. These signals are often non-linear, non-stationary, and full of Gaussian noise which are quite different from normal signals. According to this, the sub-modal obtained by empirical wavelet transform (EWT), secondary decomposition is tested by the Gaussian distribution hypothesis test. It is regarded that sub-modal following Gaussian distribution is Gaussian noise which is filtered during signal reconstruction. Then by taking advantage of the ambiguity function superiority in non-stationary signal processing and combining correlation coefficient, an ambiguity correlation classifier is constructed. After training, the classifier can recognize vibration signals of rolling bearings under different working conditions, so that the purpose of identifying rolling bearing faults can be achieved. Finally, the method effect was verified by experiments.
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Shen, Changqing, Qingbo He, Fanrang Kong, and Peter W. Tse. "A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 6 (September 25, 2012): 1362–70. http://dx.doi.org/10.1177/0954406212460628.
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The research in fault diagnosis for rolling element bearings has been attracting great interest in recent years. This is because bearings are frequently failed and the consequence could cause unexpected breakdown of machines. When a fault is occurring in a bearing, periodic impulses can be revealed in its generated vibration frequency spectrum. Different types of bearing faults will lead to impulses appearing at different periodic intervals. In order to extract the periodic impulses effectively, numerous techniques have been developed to reveal bearing fault characteristic frequencies. In this study, an adaptive varying-scale morphological analysis in time domain is proposed. This analysis can be applied to one-dimensional signal by defining different lengths of the structure elements based on the local peaks of the impulses. The analysis has been first validated by simulated impulses, and then by real bearing vibration signals embedded with faulty impulses caused by an inner race defect and an outer race defect. The results indicate that by using the proposed adaptive varying-scale morphological analysis, the cause of bearing defect could be accurately identified even the faulty impulses were partially covered by noise. Moreover, compared to other existing methods, the analysis can be functioned as an efficient faulty features extractor and performed in a very fast manner.
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Tang, Guiji, Bin Pang, Tian Tian, and Chong Zhou. "Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest." Applied Sciences 8, no. 10 (October 10, 2018): 1859. http://dx.doi.org/10.3390/app8101859.
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Fault diagnosis of rolling bearings is important for ensuring the safe operation of industrial machinery. How to effectively extract the fault features and select a classifier with high precision is the key to realizing the fault recognition of bearings. Accordingly, a new fault diagnosis method of rolling bearings based on improved fast spectral correlation and optimized random forest (i.e., particle swarm optimization-random forest (PSO-RF)) is proposed in this paper. The main contributions of this study are made from two aspects. One is that an improved fast spectral correlation approach was developed to extract the fault features of bearings and form the feature vector more effectively. The other is that an optimized random forest classifier was developed to achieve highly accurate identification by exploiting particle swarm optimization to select the best parameters of random forest (RF). In the presented method, improved fast spectral correlation was first utilized to analyze the raw vibration signal caused by a faulty bearing to obtain the enhanced envelope spectrum. Then, the amplitudes of the four characteristic cyclic frequencies (i.e., the rotating frequency, the characteristic frequency of outer-race fault, the characteristic frequency of inner-race fault, and the characteristic frequency of rolling element fault) exhibited in the enhanced envelope spectrum were selected to form the feature vector. Finally, the PSO-RF method was introduced for identifying and classifying bearing faults. The experimental investigations demonstrate the proposed method can accurately identify bearing faults and outperform other state-of-art techniques considered.
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Zhang, Junhong, Xin Lu, Jiewei Lin, Liang Ma, and Jun Wang. "Dynamic Analysis of a Rotor-Bearing-SFD System with the Bearing Inner Race Defect." Shock and Vibration 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/2489376.
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In this paper, the dynamic behavior of a rotor-bearing-SFD system with the inner race defect of bearing is investigated. The contact force between the rolling element and the race is calculated in Hertzian contact and elastohydrodynamic lubrication condition. The supporting force of the SFD is simulated by integrating the pressure distribution derived from Reynolds’s equation. The equations of motion of the rotor-bearing-SFD system are derived and solved using the fourth-order Runge-Kutta method. The dynamic behavior and the fault characteristics are analyzed with two configurations of the SFD: (1) mounted on the unfaulted bearing and (2) mounted on the faulty bearing. According to the analysis of time-frequency diagram, waterfall plot, and spectral diagram, the results show that the characteristics of inner race defects on bearing frequencies are related to the characteristic multiple frequency of the inner race defect and the fundamental frequency. The speed and defect width have different influence on the distribution and amplitude of frequency. The SFD can enhance the system stability under the bearing fault but the enhancement decreases with the increasing speed. Meanwhile, the beneficial effect of the SFD varies according to the mounted position in the rotor system.
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Kumar, Prashant, Prince Kumar, Ananda Shankar Hati, and Heung Soo Kim. "Deep Transfer Learning Framework for Bearing Fault Detection in Motors." Mathematics 10, no. 24 (December 9, 2022): 4683. http://dx.doi.org/10.3390/math10244683.
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The domain of fault detection has seen tremendous growth in recent years. Because of the growing demand for uninterrupted operations in different sectors, prognostics and health management (PHM) is a key enabling technology to achieve this target. Bearings are an essential component of a motor. The PHM of bearing is crucial for uninterrupted operation. Conventional artificial intelligence techniques require feature extraction and selection for fault detection. This process often restricts the performance of such approaches. Deep learning enables autonomous feature extraction and selection. Given the advantages of deep learning, this article presents a transfer learning–based method for bearing fault detection. The pretrained ResNetV2 model is used as a base model to develop an effective fault detection strategy for bearing faults. The different bearing faults, including the outer race fault, inner race fault, and ball defect, are included in developing an effective fault detection model. The necessity for manual feature extraction and selection has been reduced by the proposed method. Additionally, a straightforward 1D to 2D data conversion has been suggested, altogether eliminating the requirement for manual feature extraction and selection. Different performance metrics are estimated to confirm the efficacy of the proposed strategy, and the results show that the proposed technique effectively detected bearing faults.
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AlShalalfeh, Ashraf, and Laith Shalalfeh. "Bearing Fault Diagnosis Approach under Data Quality Issues." Applied Sciences 11, no. 7 (April 6, 2021): 3289. http://dx.doi.org/10.3390/app11073289.
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In rotary machinery, bearings are susceptible to different types of mechanical faults, including ball, inner race, and outer race faults. In condition-based monitoring (CBM), several techniques have been proposed in fault diagnostics based on the vibration measurements. For this paper, we studied the fractal characteristics of non-stationary vibration signals collected from bearings under different health conditions. Using the detrended fluctuation analysis (DFA), we proposed a novel method to diagnose the bearing faults based on the scaling exponent (α1) of vibration signal at the short-time scale. In vibration data with high sampling rate, our results showed that the proposed measure, scaling exponent, provides an accurate identification of the health state of the bearing. At the end, we evaluated the performance of the proposed method under different data quality issues, data loss and induced noise.
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Stack, J. R., T. G. Habetler, and R. G. Harley. "Fault-signature modeling and detection of inner-race bearing faults." IEEE Transactions on Industry Applications 42, no. 1 (January 2006): 61–68. http://dx.doi.org/10.1109/tia.2005.861365.
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Anbu, Thangavelu, and Ashok. "Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform." Computation 7, no. 4 (September 23, 2019): 54. http://dx.doi.org/10.3390/computation7040054.
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The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.
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Gu, Xiaohui, Shaopu Yang, Yongqiang Liu, Feiyue Deng, and Bin Ren. "Compound faults detection of the rolling element bearing based on the optimal complex Morlet wavelet filter." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 10 (May 22, 2017): 1786–801. http://dx.doi.org/10.1177/0954406217710673.
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Wavelet filter is widely used in extracting fault features embedded in the noisy vibration signal, especially the complex Morlet wavelet. In most occasions, the filter parameters are optimized adaptively with a suitable objective function. And then, with the Hilbert transform demodulation analysis, the single localized fault in rolling element bearings can be detected. To extend it for compound faults detection, a novel index deduced from the different intervals of the prominent bearing fault frequencies and subsequent harmonics in the envelope spectrum is proposed. By maximizing the ratio of correlated kurtosis to kurtosis of the envelope spectrum amplitudes of the filtered signal, the optimal complex Morlet wavelet filters corresponding to the different faults are designed by the particle filtering method, respectively. Two cases of real signals are analyzed to evaluate the performance of the proposed method, which include one case of experiment signal with artificial outer race fault coupled with roller fault, as well as one case of engineering data with outer race fault coupled with inner race fault. Furthermore, some comparisons with a previous method are also conducted. The results demonstrate the effectiveness and robustness of the method in compound faults diagnosis of the rolling element bearings.
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Bi, Guo, Jin Chen, Jun He, Fuchang Zhou, and Gui Cai Zhang. "Application of Degree of Cyclostationarity in Rolling Element Bearing Diagnosis." Key Engineering Materials 293-294 (September 2005): 347–54. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.347.
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Minor and random slip between rolling elements and races in rolling element bearings makes vibration signals have periodically time-varying ensemble statistics, which is known as cyclostationarity. Two second-order cyclostationary methods, the spectral correlation density (SCD) and the degree of cyclostationarity (DCS), are talked about in this paper based on a statistical model of rolling element bearings. The SCD provides redundant information in bi-frequency plane and cyclic frequency domain embodies the majority of it, which is a series of non-zero discrete cyclic frequencies completely reflecting the fault characters of rolling element bearings. The DCS has virtues of less computation and clearer representation, at the same time keeps the same characters with SCD in cyclic frequency domain. And the DCS is also proved to be resistant to the additive and multiplicative stationary noise. Simulation and experiential results from three rolling element bearing faults: outer race defect, inner race defect and rolling element defect, indicate practicability of the DCS analysis in rolling element bearing condition monitoring and fault diagnosis.
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Tian, Jing, Yanting Ai, Chengwei Fei, Ming Zhao, Fengling Zhang, and Zhi Wang. "Fault Diagnosis of Intershaft Bearings Using Fusion Information Exergy Distance Method." Shock and Vibration 2018 (August 29, 2018): 1–8. http://dx.doi.org/10.1155/2018/7546128.
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For the fault diagnosis of intershaft bearings, the fusion information exergy distance method (FIEDM) is proposed by fusing four information exergies, such as singular spectrum exergy, power spectrum exergy, wavelet energy spectrum exergy, and wavelet space spectrum exergy, which are extracted from acoustic emission (AE) signals under multiple rotational speeds and multimeasuring points. The theory of FIEDM is investigated based on four information exergy distances under multirotational speeds. As for rolling bearings, four faults and one normal condition are simulated on a birotor test rig to collect the AE signals, in which the four faults are inner ring fault, outer ring fault, rolling element fault, and inner race-rolling element coupling fault. The faults of the intershaft bearings are analyzed and diagnosed by using the FIEDM. From the investigation, it is demonstrated that the faults of the intershaft bearings are accurately diagnosed and identified, and the FIEDM is effective for the analysis and diagnosis of intershaft bearing faults. Furthermore, the fault diagnosis precision of intershaft bearings becomes higher with increasing rotational speed.
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Gong, Tingkai, Xiaohui Yuan, Xu Wang, Yanbin Yuan, and Binqiao Zhang. "Fault diagnosis for rolling element bearing using variational mode decomposition and l1 trend filtering." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 1 (September 11, 2019): 116–28. http://dx.doi.org/10.1177/1748006x19869114.
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In order to extract the faint fault features of bearings in strong noise background, a method based on variational mode decomposition and l1 trend filtering is proposed in this study. In the variational model, the mode number κ is determined difficulty, thus l1 trend filtering is applied to simplify the frequency spectrum of the original signals. In this case, this parameter can be defined easily. At the same time, a criterion based on kurtosis is used to adaptively select the regularization parameter of l1 trend filtering. The combined approach is evaluated by simulation analysis and the vibration signals of damaged bearings with a rolling element fault, an outer race fault and an inner race fault. The results demonstrate that the hybrid method is effective in detecting the three bearing faults. Moreover, compared with another approach based on multiscale morphology and empirical mode decomposition, the proposed method can extract more bearing fault features.
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Chen, Zhaowen, Ning Gao, Wei Sun, Qiong Chen, Fengying Yan, Xinyu Zhang, Maria Iftikhar, Shiwei Liu, and Zhongqi Ren. "A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis." Shock and Vibration 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/590875.
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Mathematical morphology (MM) is an efficient nonlinear signal processing tool. It can be adopted to extract fault information from bearing signal according to a structuring element (SE). Since the bearing signal features differ for every unique cause of failure, the SEs should be well tailored to extract the fault feature from a particular signal. In the following, a signal based triangular SE according to the statistics of the magnitude of a vibration signal is proposed, together with associated methodology, which processes the bearing signal by MM analysis based on proposed SE to get the morphology spectrum of a signal. A correlation analysis on morphology spectrum is then employed to obtain the final classification of bearing faults. The classification performance of the proposed method is evaluated by a set of bearing vibration signals with inner race, ball, and outer race faults, respectively. Results show that all faults can be detected clearly and correctly. Compared with a commonly used flat SE, the correlation analysis on morphology spectrum with proposed SE gives better performance at fault diagnosis of bearing, especially the identification of the location of outer race fault and the level of fault severity.
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Luo, Wei, Changfeng Yan, Junbao Yang, Yaofeng Liu, and Lixiao Wu. "Vibration response of defect-ball-defect of rolling bearing with compound defects on both inner and outer races." IOP Conference Series: Materials Science and Engineering 1207, no. 1 (November 1, 2021): 012006. http://dx.doi.org/10.1088/1757-899x/1207/1/012006.
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Abstract Aiming at the problem that the existing compound defects model of rolling bearings under radial load is difficult to reflect the actual contact between rolling elements and defects. A new model is proposed to accurately reflect the simultaneous or sequential contact between inner and outer race defects and rolling elements. Considering the coupled excitation between shaft and bearing and pedestal, time-varying displacement excitation, and radial clearance, a four degree-of-freedom vibration model of rolling bearing with compound faults on both inner and outer races is built. The vibration equations are calculated by the method of numerical way, and the model is verified by experiment. The vibration response characteristics of the Defect-Ball-Defect model are studied, which renders a theoretical criterion for bearing fault diagnosis.
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Ao, HungLinh, Junsheng Cheng, Kenli Li, and Tung Khac Truong. "A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM." Shock and Vibration 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/825825.
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This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.
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Wang, Dong Yun, Wen Zhi Zhang, Wei Ping Lu, and J. W. Du. "Application of Wavelet Packet Transform for Detection of Ball Bearing Race Fault." Materials Science Forum 626-627 (August 2009): 511–16. http://dx.doi.org/10.4028/www.scientific.net/msf.626-627.511.
Full textAbstract:
In this study, a fault diagnosis system is proposed for rolling ball bearing race using wavelet packet transform(WPT) and artificial neural network(ANN)technique. Vibration signal from ball bearings having defects on inner race and outer race is considered and the extraction method of feature vector based on wavelet packet transform with frequency band energy is used. The vibration signal is decomposed into the individual frequency bands. The variations of the signal energy in these bands reflect the different fault locations. Further, the artificial neural network is proposed to develop the diagnostic rules of the data base in the present fault identification system. The experimental work is performed to evaluate the effect of fault diagnosis in a rolling ball bearing platform under different fault conditions. The experimental results indicate the effectiveness of the proposed method in fault bearing identification.
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Mohammed, Jawad, and Jaber Abdulhady. "Rolling bearing fault detection based on vibration signal analysis and cumulative sum control chart." FME Transactions 49, no. 3 (2021): 684–95. http://dx.doi.org/10.5937/fme2103684m.
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Monitoring the condition of rotating machines is essential for the systems' safety, reducing maintenance costs, and increasing reliability. In this research, a fault detection system for bearings was developed using the vibration analysis technique with the statistical control chart approach. A test rig was first designed and constructed; then, various bearing faults, such as inner race and outer race faults, were simulated and examined in the test rig. After capturing the vibration signals at different bearing health conditions, the time-domain signal analysis technique was employed for extracting different indicative features. The obtained time domain features were then analyzed to find out the most fault-significant feature. Then, only one feature was selected to design the control chart for bearing health condition monitoring. The cumulative sum control chart (CUSUM was utilized since it can detect the small changes in bearing health states. The results showed the effectiveness of utilizing this method, and it was found that the percentage of the out-of-control points in the event of the combined cage and ball fault to the number of tested samples is greater than the other fault types.
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Usgame Sandoval, Hector M., Camilo A. Pedraza Ramírez, and Jabid Quiroga. "Acoustic emission-based early fault detection in tapered roller bearings." Ingeniería e Investigación 33, no. 3 (September 1, 2013): 5–10. http://dx.doi.org/10.15446/ing.investig.v33n3.41032.
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This paper proposes an acoustic emissions-based method for monitoring tapered roller bearings. This method monitors tracking time-based fault indicators (i.e. RMS, peak value, ring-down count and kurtosis) obtained using the AE signal. Experiments were carried out on a dedicated test bench for different levels of tapered roller bearing outer and inner race defect severity. Although the fault indicators studied could not discriminate between outer and inner raceway defects, the experimental results highlighted the proposed indicators' tapered roller bearing fault detection and fault severity assessment ability (i.e. peak value, RMS and ring-down count).
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Tyagi, Sunil, and S. K. Panigrahi. "An improved envelope detection method using particle swarm optimisation for rolling element bearing fault diagnosis." Journal of Computational Design and Engineering 4, no. 4 (May 22, 2017): 305–17. http://dx.doi.org/10.1016/j.jcde.2017.05.002.
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Abstract Traditionally Envelope Detection (ED) is implemented for detection of rolling element bearing faults by extracting the envelope of band-passed vibration signal and thereafter taking its Fourier transform. The performance of ED is highly sensitive to the envelope window (i.e. central frequency and bandwidth of the passband). This paper employs Particle Swarm Optimisation (PSO) to select the most optimum envelope window to band pass the vibration signals emanating from rotating driveline that was run in normal and with faults induced rolling element bearings. The envelopes of band-passed signals were extracted with the help of Hilbert Transform. The performance of ED whose envelope window was optimised by PSO to identify various commonly occurring bearing faults such as bearing with Outer Race Fault (ORF), Inner Race Fault (IRF) and Rolling Element Fault (REF) were checked under varying load conditions. The performance of ‘ED enhanced by PSO’ was also checked with increase in the severity of defect. It was shown that the improved ED method is successfully able to identify all types of bearing faults under different load conditions. It was shown that the by selecting envelope window by PSO makes ED especially useful to identify bearing faults at the incipient stage of defect. It was also shown by presenting comparative performance that by optimising the envelope window by PSO the performance of ED gets significantly enhanced in comparison to the traditional ED method for bearing fault diagnosis.
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Guo, Yu Jie, Jing Yu Liu, Jie Li, Zhan Hui Liu, and Wen Tao Zhang. "A Method for Improving Envelope Spectrum Symptom of Fault Rolling Bearing Based on the Auto-Correlation Acceleration Signal." Applied Mechanics and Materials 275-277 (January 2013): 856–64. http://dx.doi.org/10.4028/www.scientific.net/amm.275-277.856.
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Envelope analysis is the popular fault diagnosis method of rolling bearing. It can diagnosis rolling bearing fault, such as inner race fault and outer race fault. The traditional envelope analysis is set up on the measured acceleration signal. It is influenced by noise seriously, especially in bearing early fault stage. Research shows that the amplitude modulation phenomenon can be enlarged if the envelope analysis is done for the auto-correlation signal. Vibration test was done for a driving motor with fault rolling bearing. Factors influencing the effect of envelope analysis were analyzed.
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Du, Wenliao, Zhiyang Wang, Xiaoyun Gong, Liangwen Wang, and Guofu Luo. "Optimum IMFs Selection Based Envelope Analysis of Bearing Fault Diagnosis in Plunger Pump." Shock and Vibration 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1248626.
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As the plunger pump always works in a complicated environment and the hydraulic cycle has an intrinsic fluid-structure interaction character, the fault information is submerged in the noise and the disturbance impact signals. For the fault diagnosis of the bearings in plunger pump, an optimum intrinsic mode functions (IMFs) selection based envelope analysis was proposed. Firstly, the Wigner-Ville distribution was calculated for the acquired vibration signals, and the resonance frequency brought on by fault was obtained. Secondly, the empirical mode decomposition (EMD) was employed for the vibration signal, and the optimum IMFs and the filter bandwidth were selected according to the Wigner-Ville distribution. Finally, the envelope analysis was utilized for the selected IMFs filtered by the band pass filter, and the fault type was recognized by compared with the bearing character frequencies. For the two modes, inner race fault and compound fault in the inner race and roller of rolling element bearing in plunger pump, the experiments show that a promising result is achieved.
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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.
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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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An, Xueli, and Luoping Pan. "Wind turbine bearing fault diagnosis based on adaptive local iterative filtering and approximate entropy." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 17 (April 4, 2016): 3228–37. http://dx.doi.org/10.1177/0954406216642478.
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For the unsteady characteristics of a fault vibration signal from a wind turbine rolling bearing, a bearing fault diagnosis method based on adaptive local iterative filtering and approximate entropy is proposed. The adaptive local iterative filtering method is used to decompose original vibration signals into a finite number of stationary components. The components which comprise major fault information are selected for further analysis. The approximate entropy of the selected components is calculated as a fault feature value and input to a fault classifier. The classifier is based on the nearest neighbor algorithm. The vibration signals from a spherical roller bearing on a wind turbine in its normal state, with an outer race fault, an inner race fault and a roller fault are analyzed. The results show that the proposed method can accurately and efficiently identify the fault modes present in the rolling bearings of a wind turbine.
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Jiang, Fan, Zhencai Zhu, Wei Li, Bo Wu, Zhe Tong, and Mingquan Qiu. "Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings." Shock and Vibration 2018 (September 4, 2018): 1–12. http://dx.doi.org/10.1155/2018/1063645.
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Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.
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Irfan, Muhammad, Faisal Althobiani, Abdullah Saeed Alwadie, Maryam Zaffar, Ali Abbass, Adam Glowacz, Saleh Mohammed Ghonaim, et al. "Condition monitoring of water pump bearings using ensemble classifier." Advances in Mechanical Engineering 14, no. 3 (March 2022): 168781322210891. http://dx.doi.org/10.1177/16878132221089170.
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The bearings faults are reported to be the major reason for centrifugal pump (CPs) failures. Limited literature is available to diagnose the minor scratches in the bearing surface through non-intrusive condition monitoring techniques. Recent research on the analysis of bearing scratches through non-intrusive motor current analysis (MCA) has shown encouraging results where the comparison of machine learning and convolutional neural networks (CNNs) was performed in the classification of healthy bearings and faulty bearings (holes and scratches). The fault classification accuracy of 89.26% through MCA combination with machine learning and CNN algorithm was reported which is very low. The key factors of low accuracies were identified as low amplitudes of the harmonics in the MCA spectrum, the magnitude of environmental noise, and utilization of conventional feature extraction techniques. This problem has been tackled in this paper by developing a novel feature extractor (NFE) that extracts powerful features from the integrated current and voltage sensors data. The NFE has been derived using the threshold-based decision mechanism which has the capability to identify the location of the feature harmonic, feature extraction, measure the amplitude of the fault component, and compare it with the derived threshold. The experimental data has been collected for the bearing balls (BB), bearing cage (BC), inner race (IR) and the outer race (OR) faults, and the performance of the NFE has been tested on an ensemble classifier (CatBoost) and the better classification accuracy (99.2% for an individual feature and 100% with the combination of two or more features) of NFE has been achieved as compared to previously reported methods.
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Li, Meijiao, Huaqing Wang, Gang Tang, Hongfang Yuan, and Yang Yang. "An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing." Advances in Mechanical Engineering 6 (January 1, 2014): 676205. http://dx.doi.org/10.1155/2014/676205.
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In order to improve the effectiveness for identifying rolling bearing faults at an early stage, the present paper proposed a method that combined the so-called complementary ensemble empirical mode decomposition (CEEMD) method with a correlation theory for fault diagnosis of rolling element bearing. The cross-correlation coefficient between the original signal and each intrinsic mode function (IMF) was calculated in order to reduce noise and select an effective IMF. Using the present method, a rolling bearing fault experiment with vibration signals measured by acceleration sensors was carried out, and bearing inner race and outer race defect at a varying rotating speed with different degrees of defect were analyzed. And the proposed method was compared with several algorithms of empirical mode decomposition (EMD) to verify its effectiveness. Experimental results showed that the proposed method was available for detecting the bearing faults and able to detect the fault at an early stage. It has higher computational efficiency and is capable of overcoming modal mixing and aliasing. Therefore, the proposed method is more suitable for rolling bearing diagnosis.
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An, Xueli, and Yongjun Tang. "Application of variational mode decomposition energy distribution to bearing fault diagnosis in a wind turbine." Transactions of the Institute of Measurement and Control 39, no. 7 (February 2, 2016): 1000–1006. http://dx.doi.org/10.1177/0142331215626247.
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For the unsteady characteristics of a fault vibration signal of a wind turbine’s rolling bearing, a bearing fault diagnosis method based on variational mode decomposition of the energy distribution is proposed. Firstly, variational mode decomposition is used to decompose the original vibration signal into a finite number of stationary components. Then, some components which comprise the major fault information are selected for further analysis. When a rolling bearing fault occurs, the energy in different frequency bands of the vibration acceleration signals will change. Energy characteristic parameters can then be extracted from each component as the input parameters of the classifier, based on the K nearest neighbour algorithm. This can identify the type of fault in the rolling bearing. The vibration signals from a spherical roller bearing in its normal state, with an outer race fault, with an inner race fault and with a roller fault were analyzed. The results showed that the proposed method (variational mode decomposition is used as a pre-processor to extract the energy of each frequency band as the characteristic parameter) can identify the working state and fault type of rolling bearings in a wind turbine.
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Li, Hui. "Local Mean Decomposition Based Bearing Fault Detection." Advanced Materials Research 490-495 (March 2012): 360–64. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.360.
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A novel method of bearing fault diagnosis based on local mean decomposition (LMD) is proposed. LMD method is self-adaptive to non-stationary and non-linear signal. LMD can adaptively decompose the vibration signal into a series of product functions (PFs), which is the product of an envelope signal and a frequency modulated signal. Then the envelope spectrum is applied to the selected product function that stands for the bearing faults. Therefore, the character of the bearing fault can be recognized according to the envelope spectrum of product function. The experimental results show that local mean decomposition based envelope spectrum can effectively detect and diagnose bearing inner and outer race fault under strong background noise condition.
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Saha, Dip Kumar, Md Emdadul Hoque, and Hamed Badihi. "Development of Intelligent Fault Diagnosis Technique of Rotary Machine Element Bearing: A Machine Learning Approach." Sensors 22, no. 3 (January 29, 2022): 1073. http://dx.doi.org/10.3390/s22031073.
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The bearing is an essential component of a rotating machine. Sudden failure of the bearing may cause an unwanted breakdown of the manufacturing plant. In this paper, an intelligent fault diagnosis technique was developed to diagnose various faults that occur in a deep groove ball bearing. An experimental setup was designed and developed to generate faulty data in various conditions, such as inner race fault, outer race fault, and cage fault, along with the healthy condition. The time waveform of raw vibration data generated from the system was transformed into a frequency spectrum using the fast Fourier transform (FFT) method. These FFT signals were analyzed to detect the defective bearing. Another significant contribution of this paper is the application of a machine learning (ML) algorithm to diagnose bearing faults. The support vector machine (SVM) was used as the primary algorithm. As the efficiency of SVM heavily depends on hyperparameter tuning and optimum feature selection, the particle swarm optimization (PSO) technique was used to improve the model performance. The classification accuracy obtained using SVM with a traditional grid search cross-validation (CV) optimizer was 92%, whereas the improved accuracy using the PSO-based SVM was found to be 93.9%. The developed model was also compared with other traditional ML techniques such as k-nearest neighbor (KNN), decision tree (DT), and linear discriminant analysis (LDA). In every case, the proposed model outperformed the existing algorithms.
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Ke, Wei, Yong Xiang Zhang, and Lin Li. "Cyclic Spectrum Analysis on Rolling-Element Bearing with Inner-Race Point Defect." Advanced Materials Research 291-294 (July 2011): 1469–73. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.1469.
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Vibration signal of rolling-element bearing is random cyclostationarity when a fault develops, the proper analysis of which can be used for condition monitor. Cyclic spectrum is a common cyclostationary analysis method and has a great many algorithms which have distinct efficiency in different application circumstance, two common algorithms (SSCA and FAM) are compared in the paper. The FAM is recommended to be used in diagnosing rolling-element bearing fault via calculation of simulation signal in different signal to noise ratio. The cyclic spectrum of practice signal of rolling-element bearing with inner-race point defect is analyzed and a new characteristic extraction method is put forward. The preferable result is acquired verify the correctness of the analysis and indicate that the cyclic spectrum is a robust method in diagnosing rolling-element bearing fault.
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Hamadache, Moussa, Dongik Lee, Emiliano Mucchi, and Giorgio Dalpiaz. "Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions." Applied Sciences 8, no. 8 (August 17, 2018): 1392. http://dx.doi.org/10.3390/app8081392.
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This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra of vibration signals to detect and diagnose bearing faults. In this paper, a novel vibration-based BFDD via a probability plot (ProbPlot) image recognition technique under constant and variable speed conditions is proposed. The proposed technique is based on the absolute value principal component analysis (AVPCA), namely, ProbPlot via image recognition using the AVPCA (ProbPlot via IR-AVPCA) technique. A comparison of the features (images) obtained: (1) directly in the time domain from the original raw data of the vibration signals; (2) by capturing the Fast Fourier Transformation (FFT) of the vibration signals; or (3) by generating the probability plot (ProbPlot) of the vibration signals as proposed in this paper, is considered. A set of realistic bearing faults (i.e., outer-race fault, inner-race fault, and balls fault) are experimentally considered to evaluate the performance and effectiveness of the proposed ProbPlot via the IR-AVPCA method.
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Cui, Du, Yang, Xu, and Song. "Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution." Applied Sciences 9, no. 8 (April 23, 2019): 1681. http://dx.doi.org/10.3390/app9081681.
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Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and achallenge is how to accurately separate the inner and outer race fault features from noisy compoundfaults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Qfactorsand improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, thecompound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonancecomponents of the signal (compound fault impact component and small amount of noise) are obtained,but it can only highlight the impact of compound faults, and failed to separate the inner and outerrace compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection ofparameters (the shift order M and the filter length L) based on the iterative calculation method withthe Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filteredand de-noised by the proposed method, the inner and outer race fault signals are obtained respectively.The fault characteristic frequency is consistent with the theoretical calculation value. The results showthat the proposed method can efficiently separate the mixed fault information and avoid the mutualinterference between the components of the compound fault.
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El Morsy, Mohamed, and Gabriela Achtenova. "DETERMINATION OF ROLLER BEARING INNER RACE DEFECT BASED ON VIBRATION SIGNAL." Volume 24, No 3, September 2019 24, no. 3 (September 2019): 467–75. http://dx.doi.org/10.20855/ijav.2019.24.31291.
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The present article’s intent is to measure and identify the roller bearing inner race defect width and its corresponding characteristic frequency based on filtered time-domain vibration signal. In case localized fault occurs in a bearing, the rolling elements encounter some slippage as the rolling elements enter and leave the bearing load zone. As a consequence, the incidence of the impacts never reproduce exactly at the same position from one cycle to another. Moreover, when the position of the defect is moving with respect to the load distribution zone of the bearing, the series of impulses are modulated in amplitude in time-domain and the conforming Bearing Characteristic Frequencies (BCFs) arise in frequency domain. In order to verify the ability of time-domain in measuring the fault of rolling bearing, an artificial fault is introduced in the vehicle gearbox bearing: an orthogonal placed groove on the inner race with the initial width of 0.6mm approximately. The faulted bearing is a roller bearing quantification of the characteristic features relevant to the inner race bearing defect. It is located on the gearbox input shaft—on the clutch side. To jettison the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter based on an optimal daughter Morlet wavelet function whose parameters are optimized based on maximum Kurtosis (Kurt.). The residual signal is performed for the measurement of defect width. The proposed technique is used to analyse the experimental signal of vehicle gearbox rolling bearing. The experimental test stand is equipped with two dynamometer machines; the input dynamometer serves as an internal combustion engine, the output dynamometer introduces the load on the flange of the output joint shaft. The Kurtosis and Pulse Indicator (PI) are selected as the evaluation parameters of the de-noising effect. The results show the reliability of the proposed approach for identification and quantification of the characteristic features relevant to the inner race bearing defect.
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Chen, Lihai, Ao Tan, Lixiu Yang, Xiaoxu Pang, and Ming Qiu. "Defect Size Evaluation of Cylindrical Roller Bearings with Compound Faults on the Inner and Outer Races." Mathematical Problems in Engineering 2022 (September 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/6070822.
Full textAbstract:
Faults in cylindrical roller bearings are one of the main contributors to major faults in rotating machinery. The development of bearing fault diagnosis technologies is key to measuring the performance, status, and risk of failure of rolling-element bearings and has attracted extensive attention from industry and academia. When faults arise, they are often not a single fault but a compound fault, such as the simultaneous failure of the inner and outer races. In this paper, a method for evaluating the size of compound faults on the inner and outer races of cylindrical roller bearings is proposed. The dynamic modeling method developed by Gupta is employed to create a dynamic model for compound faults on the inner and outer rings of rolling bearings that allows the time domain signal of the vibration responses of compound faults on the inner and outer races to be obtained. Adopting an improved continuous harmonic wavelet packet decomposition method for the decomposition and reconstruction of the compound fault signal, we arrive at the corresponding single-point fault signal. The relationship between defect size and key metrics of the vibration, such as root mean square acceleration (RMS), peak, crest factor (CF), kurtosis, and level crossing rate (LCR), is investigated. The results show that there is a strong linear correlation between LCR and defect size, which can be used to evaluate the size of the defect. Experimental data for cylindrical roller bearings with compound faults on the inner and outer races are examined to verify the results.
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Yuan, Xiaoqing, Naqash Azeem, Azka Khalid, and Jahanzeb Jabbar. "Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings." Applied Sciences 12, no. 17 (August 26, 2022): 8541. http://dx.doi.org/10.3390/app12178541.
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This study proposes a statistical approach based on vibration energy at damage to detect multiple damages occurring in roller bearings. The analysis was performed at four different rotating speeds—1002, 1500, 2400, and 3000 RPM—following four different damages—inner race, outer race, ball, and combination damage—and under two types of loading conditions. These experiments were performed on a SpectraQuest Machinery Fault Simulator™ by acquiring the vibration data through accelerometers under two operating conditions: with the bearing loader on the rotor shaft and without the bearing loader on the rotor shaft. The histograms showed diversity in the defected bearing as compared to the intact bearing. There was a marked increase in the kurtosis values of each damaged roller bearing. This research article proposes that histograms, along with kurtosis values, represent changes in vibration energy at damage that can easily detect a damaged bearing. This study concluded that the vibration energy at damage-based statistical technique is an outstanding approach to detect damages in roller bearings, assisting Industry 4.0 to diagnose faults automatically.
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Gelman, Len, and Gabrijel Persin. "Novel Fault Diagnosis of Bearings and Gearboxes Based on Simultaneous Processing of Spectral Kurtoses." Applied Sciences 12, no. 19 (October 4, 2022): 9970. http://dx.doi.org/10.3390/app12199970.
Full textAbstract:
Diagnosis of bearings and gears, traditionally uses the envelope (i.e., demodulation) approach. The spectral kurtosis (SK) is a technique used to identify frequency bands for demodulation. These frequency bands are related to the structural resonances, excited by a series of fault-induced impulses. The novel approach for bearing/gear local fault diagnosis is proposed, based on division of bearing/gear vibration signals into specially defined short duration segments and simultaneous processing of SKs of all these segments for damage diagnosis. The SK-filtered vibrations are used for diagnostic feature extraction further subjected to the decision-making process, based on k-means and k-nearest neighbors. The important feature of the proposed approach is robustness to random slippage in bearings. The experimental validation of a bearing inner race local defects (1.2% relative damage size), and simulated gear vibration (15% relative pitting size), shows a very good diagnostic performance on bearing vibrations and gear vibrations to diagnose local faults. Novel diagnostic effectiveness comparison between the proposed technology and wavelet-based technology is performed for diagnosis of local bearing damage.
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Feng, Zhi Peng, Jin Zhang, Ru Jiang Hao, Fu Lei Chu, and Xue Jun Li. "Time-Wavelet Energy Spectral Analysis for Fault Diagnosis of Rolling Element Bearings." Applied Mechanics and Materials 34-35 (October 2010): 655–60. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.655.
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Periodic impulses in vibration signals and its repeating frequency are the key indicators for diagnosing the localized damage of rolling element bearings. A new method, so called time-wavelet energy spectrum, is proposed to extract the characteristic frequency of faulty elements. It is applied to analyzing the vibration signals of bearings under normal and faulty (with damage on outer race, inner race and ball respectively) statuses. The analysis results show that the time-wavelet energy spectrum is effective in extracting the repeating frequency of periodic impulses. It can not only extract the relatively significant fault feature of outer race damage, but also can extract the weaker fault features of inner race and ball damage.
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Suthar, Venish, Vinay Vakharia, Vivek K. Patel, and Milind Shah. "Detection of Compound Faults in Ball Bearings Using Multiscale-SinGAN, Heat Transfer Search Optimization, and Extreme Learning Machine." Machines 11, no. 1 (December 26, 2022): 29. http://dx.doi.org/10.3390/machines11010029.
Full textAbstract:
Intelligent fault diagnosis gives timely information about the condition of mechanical components. Since rolling element bearings are often used as rotating equipment parts, it is crucial to identify and detect bearing faults. When there are several defects in components or machines, early fault detection becomes necessary to avoid catastrophic failure. This work suggests a novel approach to reliably identifying compound faults in bearings when the availability of experimental data is limited. Vibration signals are recorded from single ball bearings consisting of compound faults, i.e., faults in the inner race, outer race, and rolling elements with a variation in rotational speed. The measured vibration signals are pre-processed using the Hilbert–Huang transform, and, afterward, a Kurtogram is generated. The multiscale-SinGAN model is adapted to generate additional Kurtogram images to effectively train machine-learning models. To identify the relevant features, metaheuristic optimization algorithms such as teaching–learning-based optimization, and Heat Transfer Search are applied to feature vectors. Finally, selected features are fed into three machine-learning models for compound fault identifications. The results demonstrate that extreme learning machines can detect compound faults with 100% Ten-fold cross-validation accuracy. In contrast, the minimum ten-fold cross-validation accuracy of 98.96% is observed with support vector machines.
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Jie, Bian, Chang Qing Huo, and Jing Jing Yu. "Fault Feature Detection of Rolling Bearing Based on LMD and Third-Order Cumulant Diagonal Slice Spectrum." Applied Mechanics and Materials 851 (August 2016): 333–39. http://dx.doi.org/10.4028/www.scientific.net/amm.851.333.
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Bearing fault signal presents obvious nonlinear and non-stationary characteristics, and local mean decomposition (LMD) method can adaptively process nonlinear and non-stationary signal. Quadratic phase coupling is one of the commonest nonlinear phenomena, and three-order cumulant diagonal slice spectrum is suitable for the extraction of the frequency components involved in quadratic phase coupling. In order to effectively detect the fault features of rolling bearing, a fault feature detection method of rolling bearing based on LMD and diagonal slice spectrum was proposed in the paper. Diagonal slice spectrum was applied in the extraction of the frequency components involved in quadratic phase coupling from the simulation signal, and it successfully extracted the quadratic phase coupling frequency components of the , and . The method was finally used in the detection of the bearing inner race fault features, and the results demonstrated that compared with diagonal slice spectrum of the PF component signals, the diagonal slice spectrum of the PF component envelope signals can identify fault characteristics much clearer and brighter. Meanwhile, the effective extraction of the inner race fault features verified the effectiveness and practicability of the method proposed in this paper.