Research on Multi-channel Signal Denoising Method for Multiple Faults Diagnosis of Rolling Element Bearings Based on Tensor Factorization

doi:10.3901/JME.2019.12.050

Abstract

Abstract: Aiming at simultaneously multi-channel signal filtering and multiple faults diagnosis of the roller bearings, a novel multi-channel signal denoising technique is proposed based on tensor factorization. The original vibration signals are first formulated as a 3-way tensor via temporal signal, frequency and channel in a high dimensional space. The tensor model is factorized using Tucker-3 decomposition. Then, key parameters used in the truncated HOSVD are obtained according to the L-curve criterion. Finally, tensor model is solved by those selected truncated parameters, while target tensor is reconstructed by inverse transformation. The performance of the developed technique in detecting faults of roller bearings has been thoroughly evaluated through simulation signals. The presented approach is then applied to reduce noise in vibration signal collected on bearing test-rigs. Experimental results showed that the feasibility and validity of the proposed method in multiple faults detection of the bearings. Multidimensional signal filtering based on tensor will broaden the view in dealing with heterogeneous and multiaspect data in an age of big data.

Key words: multi-channel denoising, multiple faults diagnosis, rolling element bearing, tensor factorization, truncated HOSVD

CLC Number:

TH133

HU Chaofan, WANG Yanxue. Research on Multi-channel Signal Denoising Method for Multiple Faults Diagnosis of Rolling Element Bearings Based on Tensor Factorization[J]. Journal of Mechanical Engineering, 2019, 55(12): 50-57.

References

[1] 王建国,李健,万旭东. 基于奇异值分解和局域均值分解的滚动轴承故障特征提取方法[J]. 机械工程学报,2015,51(3):104-110. WANG Jianguo,LI Jian,WAN Xudong. Fault feature extraction method of rolling bearings based on singular value decomposition and local mean decomposition[J]. Journal of Mechanical Engneering,2015,51(3):104-110.
[2] WANG Yanxue,HE Zhengjia,ZI Yanyang. Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform[J]. Mechanical Systems and Signal Processing,2010,24(1):119-137.
[3] WANG Yanxue,LIANG Ming. An adaptive SK technique and its application for fault detection of rolling element bearings[J]. Mechanical Systems and Signal Processing,2011,25(5):1750-1764.
[4] LEI Yaguo. Planetary gearbox fault diagnosis using an adaptive stochastic resonance method[J]. Mechanical Systems and Signal Processing,2013,38(1):113-124.
[5] ZHANG Suofeng,WANG Yanxue,HE Shuilong,et al. Bearing fault diagnosis based on variational mode decomposition and total variation denoising[J]. Measurement Science and Technology,2016,27(7):075101.
[6] LI Yifan. Detection of bearing faults using a novel adaptive morphological update lifting wavelet[J]. Chinese Journal of Mechanical Engineering,2017,30(6):1305-1313.
[7] WANG Yanxue,XIANG Jiawei. Spectral kurtosis for fault detection,diagnosis and prognostics of rotating machines:A review with applications[J]. Mechanical Systems and Signal Processing,2016,66(67):679-698.
[8] WANG Yanxue,WEI Zexian,YANG Jianwei. Feature trend extraction and adaptive density peaks search for intelligent fault diagnosis of machines[J]. IEEE Transactions on Industrial Informatics,2018,15(1):105-115.
[9] 雷亚国,贾峰,孔德同,等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报,2018,54(5):94-104. LEI yaguo,JIA Feng,KONG Detong,et al. The opportunity and challenge of mechanical intelligent fault diagnosis under big data[J]. Journal of Mechanical Engineering,2018,54(5):94-104.
[10] ZHANG Yu. Fast nonnegative tensor factorization based on accelerated proximal gradient and low-rank approximation[J]. Neurocomputing,2016,198:148-154.
[11] HAO Zhifeng. A linear support higher-order tensor machine for classification[J]. IEEE Transactions on Image Processing,2013,22(7):2911-2920.
[12] HU Chaofan,WANG Yanxue. Multidimensional denoising of rotating machine based on tensor factorization[J]. Mechanical Systems and Signal Processing,2019,122:273-289.
[13] KOLDA T G,BADER B W. Tensor decomposition and applications[J]. Siam Review,2009,51(3):455-500.
[14] FIGUEIREDO M,RIBEIRO B,DE ALMEIDA A. Analysis of trends in seasonal electrical energy consumption via non-negative tensor factorization[J]. Neurocomputing,2015,170:318-327.
[15] HANSEN P C. The truncatedsvd as a method for regularization[J]. BIT Numerical Mathematics,1987,27(4):534-553.
[16] REZGHI M,HOSSEINI S M. A new variant of L-curve for Tikhonov regularization[J]. Journal of Computational and Applied Mathematics,2009,231(2):914-924.
[17] MUTI D,BOURENNANE S,MAROT J. Lower-rank tensor approximation and multiway filtering[J]. SIAM Journal on Matrix Analysis and Applications,2008(30):1172-1204.
[18] ERICSSON S. Towards automatic detection of local bearing defects in rotating machines[J]. Mechanical systems and signal processing,2005,19(3):509-535.
[19] WANG Y,LIANG M. Identification of multiple transient faults based on the adaptive spectral kurtosis method[J]. Journal of Sound & Vibration,2012,331(2):470-486.