Fault detection of rolling bearing based on principal component analysis and empirical mode decomposition

Yu Yuan; Chen Chen; Yu Yuan; Chen Chen

doi:10.3934/math.2020379

AIMS Mathematics

2020, Volume 5, Issue 6: 5916-5938. doi: 10.3934/math.2020379

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Research article

Fault detection of rolling bearing based on principal component analysis and empirical mode decomposition

Yu Yuan ^{1,3
,
,},
Chen Chen ²

1.
School of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China
2.
School of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, China
3.
Traction Power State Key Laboratory of Southwest Jiaotong University, Chendu 610031, China

Received: 02 June 2020 Accepted: 03 July 2020 Published: 16 July 2020
MSC : 03C98, 62H25

For the problem of inconsistent quantitative standards for running status analysis of rolling bearings, this paper uses principal component analysis (PCA) to extract a new index F, which is the joint parameters of time domain and frequency domain, and by establishing the value of F to analyze the running states of the rolling bearings. Firstly, the acceleration sensors are used to collect the vibration signal of the whole life cycle of the rolling bearings. Secondly, empirical mode decomposition (EMD) method is used to denoise the acquired vibration signal. Then, the main components of the denoised vibration signal are used to propose the characteristic parameters and synthesized into new parameter indicators. Finally, envelope analysis spectrum is used to analyze the fault classification under the new parameter index. The exepriment results show that the whole life cycle of the rolling bearings can be classified into five different operating periods by using the new parameter index, and each period represents a different bearing operating state.
- rolling bearing,
- PAC,
- life cycle,
- EMD,
- envelope analysis
Citation: Yu Yuan, Chen Chen. Fault detection of rolling bearing based on principal component analysis and empirical mode decomposition[J]. AIMS Mathematics, 2020, 5(6): 5916-5938. doi: 10.3934/math.2020379

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Abstract

For the problem of inconsistent quantitative standards for running status analysis of rolling bearings, this paper uses principal component analysis (PCA) to extract a new index F, which is the joint parameters of time domain and frequency domain, and by establishing the value of F to analyze the running states of the rolling bearings. Firstly, the acceleration sensors are used to collect the vibration signal of the whole life cycle of the rolling bearings. Secondly, empirical mode decomposition (EMD) method is used to denoise the acquired vibration signal. Then, the main components of the denoised vibration signal are used to propose the characteristic parameters and synthesized into new parameter indicators. Finally, envelope analysis spectrum is used to analyze the fault classification under the new parameter index. The exepriment results show that the whole life cycle of the rolling bearings can be classified into five different operating periods by using the new parameter index, and each period represents a different bearing operating state.

References

[1]	H. Zhao, H. Liu, J. Xu, et al. Performance prediction using high-order differential mathematical morphology gradient spectrum entropy and extreme learning machine, IEEE T. Instrum. Meas., 69 (2020), 4165-4172. doi: 10.1109/TIM.2019.2948414
[2]	S. Lu, P. Zheng, Y. Liu, et al. Sound-aided vibration weak signal enhancement for bearing fault detection by using adaptive stochastic resonance, J. Sound Vib., 449 (2019), 18-29. doi: 10.1016/j.jsv.2019.02.028
[3]	T. Li, J. Shi, X. Li, et al. Image encryption based on pixel-level diffusion with dynamic filtering and DNA-level permutation with 3D Latin cubes, Entropy, 21 (2019), 1-21.
[4]	Y. Xu, H. Chen, J. Luo, et al. Enhanced Moth-flame optimizer with mutation strategy for global optimization, Inform. Sciences, 492 (2019), 181-203. doi: 10.1016/j.ins.2019.04.022
[5]	H. Zhao, J. Zheng, J. Xu, et al. Fault diagnosis method based on principal component analysis and broad learning system, IEEE Access, 7 (2019), 99263-99272. doi: 10.1109/ACCESS.2019.2929094
[6]	H. Pan, Y. Yang, J. Zheng, et al. A noise reduction method of symplectic singular mode decomposition based on Lagrange multiplier, Mech. Syst. Signal Pr., 133 (2019), 1-21.
[7]	Q. Shu, S. Lu, M. Xia, et al. Enhanced feature extraction method for motor fault diagnosis using low-quality vibration data from wireless sensor networks, Meas. Sci. Technol., 31 (2020), 045016
[8]	R. Chen, S. K. Guo, X. Z. Wang, et al. Fusion of multi-RSMOTE with fuzzy integral to classify bug reports with an imbalanced distribution, IEEE T. Fuzzy Syst., 27 (2019), 2406-2420. doi: 10.1109/TFUZZ.2019.2899809
[9]	H. Zhao, S. Zuo, M. Hou, et al. A novel adaptive signal processing method based on enhanced empirical wavelet transform technology, Sensors, 18 (2018), 1-17. doi: 10.1109/JSEN.2018.2870228
[10]	Y. Liu, X. Wang, Z. Zhai, et al. Timely daily activity recognition from headmost sensor events, ISA T., 94 (2019), 379-390. doi: 10.1016/j.isatra.2019.04.026
[11]	H. Zhao, J. Zheng, W. Deng, et al. Semi-supervised broad learning system based on manifold regularization and broad network, IEEE T. Circuits-I., 67 (2020), 983-994.
[12]	T. Li, Z. Qian, T. He, Short-term load forecasting with improved CEEMDAN and GWO-based multiple kernel ELM, Complexity, 2020 (2020), 1-20.
[13]	J. Zheng, Z. Dong, H. Pan, et al. Composite multi-scale weighted permutation entropy and extreme learning machine based intelligent fault diagnosis for rolling bearing, Measurement, 143 (2019), 69-80. doi: 10.1016/j.measurement.2019.05.002
[14]	G. Xu, D. M. Hou, H. Qi, et al. High-speed train wheel set bearing fault diagnosis and prognostics: A new prognostic model based on extendable useful life, Mech. Syst. Signal Pr., 146 (2020), 1-23.
[15]	F. Zhang, J. Yan, P. Fu, et al. Ensemble sparse supervised model for bearing fault diagnosis in smart manufacturing, Mech. Syst. Signal Pr., 65 (2020),1-11.
[16]	R. Liu, B. Yang, E. Zio, et al. Artificial intelligence for fault diagnosis of rotating machinery: A review, Mech. Syst. Signal Pr., 108 (2018), 33-47. doi: 10.1016/j.ymssp.2018.02.016
[17]	R. A. Carmona, W. L. Hwang, B. Torresani, Characterization of signals by the ridges of their wavelet transforms, IEEE T. Signal Proces., 45 (1997), 2586-2590. doi: 10.1109/78.640725
[18]	W. Deng, H. Liu, J. Xu, et al. An improved quantum-inspired differential evolution algorithm for deep belief network, IEEE T. Instrum. Meas., 2020 (2020), 1-8.
[19]	H. Li, Y. Zhang, H. Zheng, Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearing, J. Mech. Sci. Technol., 23 (2009), 291-301. doi: 10.1007/s12206-008-1110-5
[20]	Y. Lei, J. Lin, Z. He, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery, Mech. Syst. Signal Pr., 35 (2013), 108-126. doi: 10.1016/j.ymssp.2012.09.015
[21]	Y. Wang, F. Liu, Z. Jiang, et al. Complex variational mode decomposition for signal processing applications, Mech. Syst. Signal Pr., 86 (2017), 75-85. doi: 10.1016/j.ymssp.2016.09.032
[22]	N. Lu, Z. Xiao, O. P. Malik, Feature extraction using adaptive multiwavelets and synthetic detection index for rotor fault diagnosis of rotating machinery, Mech. Syst. Signal Pr., 52 (2015), 393-415.
[23]	J. Zheng, H. Pan, S. Yang, et al. Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis, Signal Process., 130 (2017), 305-314. doi: 10.1016/j.sigpro.2016.07.023
[24]	F. Cheng, J. Wang, L. Qu, et al. Rotor-current-based fault diagnosis for DFIG wind turbine drivetrain gearboxes using frequency analysis and a deep classifier, IEEE T. Ind. Appl., 54 (2017), 1062-1071.
[25]	H. Ocak, K. A. Loparo, HMM-based fault detection and diagnosis scheme for rolling element bearings, J. Vib. Acoust., 127 (2005), 299-306. doi: 10.1115/1.1924636
[26]	N. Gebraeel, M. Lawley, R. Liu, et al. Residual life predictions from vibration-based degradation signals: A neural network approach, IEEE T. Ind. Electron., 51 (2004), 694-700. doi: 10.1109/TIE.2004.824875
[27]	C. Sun, Z. Zhang, Z. He, Rescarch on bearing life prediction based on support vector machine and its application, J. Phys. Conf. Ser., 305 (2011), 1-9.
[28]	P. J. Vlok, M. Wnek, M. Zygmunt, Utilising statistical residual life estimates of bearings to quantify the influence of preventive maintenance actions, Mech. Syst. Signal Pr., 18 (2004), 833-847. doi: 10.1016/j.ymssp.2003.09.003
[29]	X. Zhang, R. Xu, C. Kwan, et al. An integrated approach to bearing fault diagnostics and prognostics, In: Proceedings of the 2005, American Control Conference, 2005, 2750-2755.
[30]	S. Lu, Q. He, J. Wang, A review of stochastic resonance in rotating machine fault detection, Mech. Syst. Signal Pr., 116 (2019), 230-260. doi: 10.1016/j.ymssp.2018.06.032
[31]	H. Zhao, D. Li, W. Deng, et al. Research on vibration suppression method of alternating current motor based on fractional order control strategy, P. I. Mech. Eng. E-J. Pro., 231 (2017), 786-799. doi: 10.1177/0954408916637380
[32]	Y. Shao, K. Nezu, Prognosis of remaining bearing life using neural networks, P. I. Mech. Eng-I. Sys., 214 (2000), 217-230.
[33]	W. Deng, J. Xu, Y. Song, et al. An effective improved co-evolution ant colony optimization algorithm with multi-strategies and its application, Int. J. Bio-Inspired Comput., 2019 (2019),1-10.
[34]	H. Shao, J. Cheng, H. Jiang, et al. Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing, Knowl-Based Syst., 188 (2020), 1-14.
[35]	H. Chen, Q. Zhang, J. Luo, et al. An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine, Appl. Soft Comput., 86 (2020), 1-24.
[36]	W. Yang, M. Qian, D. Huang, Detection of exons with deletions and insertions by hidden markov models, Prog. Biochem. Biophys., 29 (2002), 56-59.
[37]	W. Deng, J. Xu, H. Zhao, An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem, IEEE Access, 7 (2019), 20281-20292. doi: 10.1109/ACCESS.2019.2897580
[38]	Y. Liu, Y. Mu, K. Chen, et al. Daily activity feature selection in smart homes based on pearson correlation coefficient, Neural Process. Lett., 51 (2020), 1771-1787. doi: 10.1007/s11063-019-10185-8
[39]	Z. He, H. Shao, X. Zhang, et al. Improved deep transfer auto-encoder for fault diagnosis of gearbox under variable working conditions with small training samples, IEEE Access, 7 (2019), 115368-115377. doi: 10.1109/ACCESS.2019.2936243
[40]	W. Deng, W. Li, X. Yang, A novel hybrid optimization algorithm of computational intelligence techniques for highway passenger volume prediction, Expert Syst. Appl., 38 (2011), 4198-4205. doi: 10.1016/j.eswa.2010.09.083
[41]	J. B. Ali, B. Chebel-Morello, L. Saidi, et al. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network, Mech. Syst. Signal Pr., 56 (2015), 150-172.
[42]	M. Chen, Q. Li, Application of oil pressure curve monitoring in sliding bearing wear diagnosis, Machine Tool & Hydraulics, 21 (2010), 145-148.
[43]	B. Samanta, K. R. Al-Balushi, Artificial neural network based fault diagnostics of rolling element bearings using time-domain features, Mech. Syst. Signal Pr., 17 (2003), 317-328.
[44]	Y. Zhang, S. Qin, Fault detection of nonlinear processes using multiway kernel independent component analysis, Ind. Eng. Chem. Res., 46 (2007), 7780-7787. doi: 10.1021/ie070381q
[45]	A. Hyvärinen, E. Oja, Independent component analysis: Algorithms and applications, Neural Networks, 13 (2000), 411-430. doi: 10.1016/S0893-6080(00)00026-5
[46]	R. B. W. Heng, M. J. M. Nor, Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition, Appl. Acoust., 53 (1998), 211-226. doi: 10.1016/S0003-682X(97)00018-2
[47]	N. Tandon, A comparison of some vibration parameters for the condition monitoring of rolling element bearings, Measurement, 12 (1994), 285-289. doi: 10.1016/0263-2241(94)90033-7
[48]	R. Li, J. Chen, X. Wu, et al. Fault diagnosis of rotating machinery based on SVD, FCM and RST, Int. J. Adv. Manuf. Tech., 27 (2005), 128-135. doi: 10.1007/s00170-004-2140-5
[49]	N. Gebraeel, M. Lawley, R. Liu, et al. Vibration-based condition monitoring of thrust bearings for maintenance management, In: Proc. ANNIE 2002 Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Artificial Life and Data Mining, 2002, 543-551.

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