A Composite Fault Feature Enhancement Approach for Rolling Bearings Grounded on ITD and Entropy-based Weight Method

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Published Jan 24, 2023
mingyue yu jingwen su liqiu liu yi zhang

Abstract

Aiming to precisely identify a compound fault of rolling bearing, the paper has contributed a fault characteristic enhancement method by combing entropy weight method (EWM) and intrinsic time scale decomposition (ITD). Firstly, to effectively segregate frequency components in vibration signals, proper rotation components (PRCs) were obtained by decomposing vibration signals based on ITD. Secondly, in view of the fact that amplitude, variance and correlation coefficient vary greatly in a bearing fault accompanied by impact components, parameter evaluation indexes were brought in to depict the fault characteristics of PRCs, including average, variance, correlation coefficient, margin factor, kurtosis, impulse factor, peak factor and so on. Thirdly, weight coefficient of each parameter index was calculated by entropy weight method and the characteristics of each PRC highlighted based on that. Finally, the signals were reconstructed according to the PRCs whose characteristics had been enhanced. Meanwhile reconstructed signals were denoised with singular differential spectrum (SDS) to reduce the influence of noise components, and then the type of compound fault was distinguished grounded on the frequency spectrum. To further prove the efficiency of proposed method, it is compared with other methods (SDS, ITD + entropy method). The result indicates that the proposed method can further highlight the characteristic information of compound faults of bearing and embody more exact identification and judgment on the type of faults.

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Keywords

rolling bearing; intrinsic time-scale decomposition; entropy weighting method;

References
Berlin, Lu Chao et al. Extraction method of rolling bearing fault characteristics based on ITD and ICA[J]. Vibration and shock,2015,34(14):153-156. DOI: 10.13465/j.cnki.jvs.2015.14.026.
Debiao Meng et al. Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction[J]. Computer Modeling in Engineering & Sciences, 2022, 130(1): 543-558. DOI: 10.32604/CMES.2022.018123.
Deng Linfeng Zhang Aihua et al. Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM[J]. Advances in Mechanical Engineering, 2022, 14(1) DOI: 10.1177/16878140211072990.
Ding Jiakai et al. A Fault Feature Extraction Method for Rolling Bearing Based on Intrinsic Time-Scale Decomposition and AR Minimum Entropy Deconvolution[J]. Shock and Vibration, 2021, 2021 DOI: 10.1155/2021/6673965.
Dong Shaojiang et al. Rolling bearing performance degradation assessment based on singular value decomposition-sliding window linear regression and improved deep learning network in noisy environment[J]. Measurement Science and Technology, 2022, 33(4). DOI: 10.1088/1361-6501/AC39D1.
Fei Wang, Zhang Wenjin et al. Bearing fault diagnosis based on intrinsic time-scale decomposition and extreme learning machine[J]. Vibroengineering PROCEDIA, 2017, 14: 97-101. DOI: 10.21595/vp.2017.19198.
Gao Yajuan, Chen Lei et al. Fault diagnosis of rolling bearings combined with all-purpose ITD and KPCA[J]. Mechanical design and manufacturing,2019(04):154-157. DOI: 10.19356/j.cnki.1001-3997.2019.04.039.
Guo Dazhi et al. Research on bearing diagnosis technology based on wavelet transform and one-dimensional convolutional neural network[J]. MATEC Web of Conferences, 2021, 336: 01010-01010. DOI: 10.1051/MATECCONF/202133601010.
Gougam Fawzi et al. Bearing faults classification under various operation modes using time domain features, singular value decomposition, and fuzzy logic system[J].
Jun Ma et al. A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing[J]. Journal of Low Frequency Noise, Vibration and Active Control, 2018, 37(4) : 928-954.Advances in Mechanical Engineering, 2020, 12(10).
Li hua et al. A bearing fault diagnosis method based on enhanced singular value decomposition[J]. IEEE Transactions on Industrial Informatics, 2020, PP (99): 1-1. DOI: 10.1109/tii.2020.3001376.
Li Hua et al. Correlated SVD and Its Application in Bearing Fault Diagnosis. [J]. IEEE transactions on neural networks and learning systems, 2021, PP DOI: 10.1109/TNNLS.2021.3094799
Li Hongxian, Tang Baoping et al. Fault diagnosis of optimal frequency band demodulation of rolling bearings based on enhanced entropy weight steepness diagram[J]. Vibration and shock,2019,38(17):24-31+50. DOI: 10.13465/j.cnki.jvs.2019.17.004.
Liu Feng, Li Xinxin et al. Fault diagnosis method for rolling bearing based on ITD and improved MCKD[J]. Journal of Guangxi University (Natural Science Edition),2021,46(01):107-115. DOI: 10.13624/j.cnki.issn.1001-7445.2021.0107.
Mingyue Yu and Xiang Pan. A novel ITD-GSP-based characteristic extraction method for compound faults of rolling bearing[J]. Measurement, 2020, 159 (prepublish): 107736-107736. DOI: 10.1016/j.measurement.2020.107736.
Pan Xiang Feng Zhigang et al. A method to diagnose compound fault of rolling bearing with ITD-AF[J]. Journal of Vibroengineering, 2021, 23(3): 559-571. DOI:10.21595/JVE.2020.21476.
Te Han et al. The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value[J]. Shock and Vibration, 2016, 2016 : 1-14.
Wang Hao, Tan Jiwen et al. Feature-level fusion and decision-level fusion of rolling bearing fault information[J]. Mechanical research and application,2016,29(01):212-214+218. DOI: 10.16576/j.cnki.1007-4414.2016.01.070.
Wu Yu, Jiang Yuliang et al. Abnormal temperature rise detection model of rail vehicle bearing based on AHP-entropy method optimization decision[J]. Transactions of the Chinese Journal of Railway Science and Engineering,2020,17(11):2909-2919. DOI: 10.19713/j.cnki.43-1423/u. T20200070.
Xueli An et al. Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing[J]. Journal of Vibration and Control, 2012, 18(2): 240-245. DOI: 10.1177/1077546311403185.
Yang Zhifei, Sui Wentao et al. Bearing fault diagnosis by variable mode decomposition and entropy method[J]. Combined machine tool and automated machining technology,2017(11):78-80. DOI: 10.13462/j.cnki.mmtamt.2017.11.020.
Yahui Cui et al. Incipient fault diagnosis of rolling bearing using accumulative component kurtosis in SVD process[J]. Journal of Vibroengineering, 2018, 20(3): 1443-1458. DOI: 10.21595/jve.2018.19181.
Yuan Zhe et al. Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method[J]. Journal of Low Frequency Noise, Vibration and Active Control, 2019: 146134841986701-146134841986701. DOI: 10.1177/1461348419867012.
Zhang Yan. Application of entropy method in the optimization decision of rolling bearing selection grey[J]. Bearings,2009(12):5-7. DOI:10.19533/j. issn1000-3762.2009.12.002.
Zhao Lei, Xia Junzhong et al. Extraction of fault characteristics of rolling bearings based on MED and ITD[J]. Journal of Military Transportation College,2018,20(03):45-49. DOI: 10.16807/j.cnki.12-1372/e.2018.03.011.
Zhou Yiwen, Chen Jinhai et al. Early fault diagnosis of rolling bearings based on noise auxiliary signal enhancement[J]. Journal of Vibration and shock,2020,39(15):66-73. DOI: 10.13465/j.cnki.jvs. 2020.15.009.
Zou Tiangang et al. Early fault diagnosis of rolling bearings based on signal reconstruction through SDD-SVD and FWEO[J]. Journal of Physics: Conference Series, 2021, 1820(1): 012069-.DOI: 10.1088/1742-6596/1820/1/012069.DOI: 10.1177/1687814020967874
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Technical Papers