Review of local mean decomposition and its application in fault diagnosis of rotating machinery

doi:10.21629/JSEE.2019.04.17

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (4): 799-814.doi: 10.21629/JSEE.2019.04.17

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Review of local mean decomposition and its application in fault diagnosis of rotating machinery

Yongbo LI¹(), Shubin SI¹(), Zhiliang LIU²(), Xihui LIANG^3,*()

¹ MIIT Key Laboratory of Dynamics and Control of Complex Systems, Northwestern Polytechnical University, Xi'an 710072, China
² School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
³ Department of Mechanical Engineering, University of Manitoba, Winnipeg R3T 5V6, Canada

Received:2019-03-04 Online:2019-08-01 Published:2019-09-01
Contact: Xihui LIANG E-mail:yongbo@nwpu.edu.cn;sisb@nwpu.edu.cn;Liu@uestc.edu.cn;Xihui.Liang@umanitoba.ca
About author:LI Yongbo was born in 1986. He received his master degree from Harbin Engineering University, Harbin, China, in 2012. He received his Ph.D. degree in general mechanics from Harbin Institute of Technology, Harbin, China, in 2017. He is currently an associate professor in the School of Aeronautics, Northwestern Polytechnical University, China. Prior to joining Northwestern Polytechnical University in 2017, he was a visiting student with the University of Alberta, Canada. His research interests include signal processing, fault feature extraction, and fault pattern identification. E-mail:yongbo@nwpu.edu.cn|SI Shubin was born in 1974. He received his B.S. and M.S. degrees in mechanical engineering and Ph.D. degree in management science and engineering from Northwestern Polytechnical University (NPU), Xi'an, China, in 1997, 2002, and 2006, respectively. He is a professor with the School of Mechanical Engineering, NPU. He has published more than 60 academic papers and articles in journals and conferences in the past five years. His research interests include system reliability theory, importance measures, and fault diagnosis. E-mail:sisb@nwpu.edu.cn|LIU Zhiliang was born in 1984. He received his Ph.D. degree in the School of Automation Engineering from University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2013. From 2009 to 2011, he studied at the University of Alberta as a visiting scholar for two years. Currently, he is an associate professor with the School of Mechanical and Electrical Engineering, UESTC. His research interests include fault diagnosis and prognostics of rotating machinery by using advanced signal processing and data mining methods. E-mail:Zhiliang Liu@uestc.edu.cn|LIANG Xihui was born in 1984. He received his B.S. and M.S. degrees from Shandong University, China, in 2007 and 2009, respectively, and Ph.D. degree in the University of Alberta, Canada, in 2016. He is currently an assistant professor in the Department of Mechanical Engineering of the University of Manitoba, Canada. His research interests include nonlinear dynamics, machinery condition monitoring, fault diagnostics and prognostics, reliability modelling and analysis, predictive maintenance, and intelligent manufacturing. E-mail:Xihui.Liang@umanitoba.ca
Supported by:
the National Natural Science Foundation of China(51805434);the National Natural Science Foundation of China(71771186);the National Natural Science Foundation of China(71631001);the Postdoctoral Innovative Talent Plan of China(BX20180257);the Postdoctoral Science Funds of China(2018M641021);the Key Research Program of Shaanxi Province(2019KW-017);the Natural Science and Engineering Research Council of Canada(RGPIN-2019-05361);This work was supported by the National Natural Science Foundation of China (51805434; 71771186; 71631001), the Postdoctoral Innovative Talent Plan of China (BX20180257), the Postdoctoral Science Funds of China (2018M641021), the Key Research Program of Shaanxi Province (2019KW-017), and the Natural Science and Engineering Research Council of Canada (RGPIN-2019-05361)

Abstract

Abstract:

Rotating machinery is widely used in the industry. They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions. Early detection of these damages is important, otherwise, they may lead to large economic loss even a catastrophe. Many signal processing methods have been developed for fault diagnosis of the rotating machinery. Local mean decomposition (LMD) is an adaptive mode decomposition method that can decompose a complicated signal into a series of mono-components, namely product functions (PFs). In recent years, many researchers have adopted LMD in fault detection and diagnosis of rotating machines. We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines. First, the LMD is described. The advantages, disadvantages and some improved LMD methods are presented. Then, a comprehensive review on applications of LMD in fault diagnosis of the rotating machinery is given. The review is divided into four parts:fault diagnosis of gears, fault diagnosis of rotors, fault diagnosis of bearings, and other LMD applications. In each of these four parts, a review is given to applications applying the LMD, improved LMD, and LMD-based combination methods, respectively. We give a summary of this review and some future potential topics at the end.

Key words: local mean decomposition (LMD), signal processing, gear, rotor, bearing

Yongbo LI, Shubin SI, Zhiliang LIU, Xihui LIANG. Review of local mean decomposition and its application in fault diagnosis of rotating machinery[J]. Journal of Systems Engineering and Electronics, 2019, 30(4): 799-814.

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RMSE		OI	Energy loss
PF1	PF2	OI	Energy loss
0.172 7	0.140 5	0.064 3	LMD method

Combination method	Reference
LMD + Morlet wavelets	[69]
Order tracking + LMD	[58]
LMD + Chirp-Z transform (CZT)	[70]
SGWD + LMD	[60]
LMD + SE + TFPF	[61]
LMD + Cyclostationary demodulation	[71]
LMD + Synchrosqueezing transform + TFR	[62]
MED+ LMD + Cyclic autocorrelation function demodulation	[63]
LMD + Kurtosis	[72]

Classifier	Feature extraction method
BP	LMD + Energy characteristic [64]
LSSVM	LMD + Energy characteristic [65]
KFCM	LMD + GMFDs + Mutual information entropy value [59]
ANFIS model	LMD + Fuzzy entropy [66]
ELM	LMD + PE [67]
RWSVM	LMD + Statistic features [68]

Combination method	Reference
LMD + Pattern spectrum	[78]
LMD + TEK	[79]
LMD + Self-adaptive Wigner-Ville distribution	[81]
LMD + Multi-Class RWSVM	[68]
LMD + AR + Neural network	[82]

Combination method	Reference
LMD + WVD + WVSE	[89]
LMD + Fourier transform	[92]
LMD + Correlation analysis + WHMS	[98]
LMD + HT + Teager energy operator (TEO) + Fourier transform}	[104]
LMD + Ensemble empirical mode decomposition (EEMD) + Time-frequency analysis	[105]
LMD + Wavelet threshold denoising method + Kurtosis	[106]
LMD + SVD + Time frequency map + marginal spectrum	[107]

Review of local mean decomposition and its application in fault diagnosis of rotating machinery

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References 141

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Classifier	Feature extraction method
VPMCD	LMD + Computed order tracking (COT) [91]
SVM	LMD + Fault characteristic amplitude ratios [93, 108], LMD + Mutiscale sample entropy (MSE) [94], LMD + SE + energy ratio [95], HLMD + Adaptive mutiscale fuzzy entropy (AMFE) [102], HLMD + MFE [97]; LMD+ MPE +Laplacian score [103]; LMD + Morphological filtering [100], LMD + Autoregressive model [109], LMD + PCA [110], LMD + Energy entropy [111], LMD + Kernel principal component analysis [112]
ELM	LMD + SVD [96]
K-means clustering	LMD + PE [99]
HMM	LMD + Multiscale permutation entropy (MPE) [101]
MDTW	LMD + PE [113]
BP	LMD + Energy characteristics [114], LMD + Power spectrum [115]
FCM	ELMD + SVD [116]
Mahalanobis distance	LMD + Kullback-leibler divergence [117]
Neural network	ELMD + Energy characteristic [118]

Classifier	Application	Feature extraction method
	High voltage circuit breakers	LMD+TSEE + SVDD [127]
FCM	Shearer cutting	LMD + Time-frequency analysis + LS [129]
	Wind turbine	ELMD + Singular values [130]
ELM	Pipeline	LMD + Information entropy [131]
LR	Hydraulic pump	LMD + PCA [132]
	Natural gas pipeline	LMD + Wavelet packet decomposition + Envelope spectrum entropy [122]
	Diesel engine	LMD + Fault characteristics [133]
SVM	Analog circuit	LMD + Correlation analysis [134]
	Shearer cutting	LMD+ MFE [48]
	Hydraulic pump	LMD + SVD [125] LMD + Approximate entropy [135]
	Reciprocating compressor	LMD+MSE [136]
Sparse representation classifiers	Natural gas pipeline	ELMD + Kullback-Leibler divergence [128]
RWSVM	Natural gas pipeline	LMD + PCA [126]
BP	Reciprocating compressor gas valve	LMD + Lempel-Ziv complexity [121] LMD + Autoregressive-generalized autoregressive conditional heteroscedasticity model [137]
	Subway auxiliary inverter	LMD + Approximate entropy [138]
Classifier	Applications	Feature extraction method

Technique	Application	Reference
CBSR + LMD	Reciprocating compressor	[123]
LMD + IAMMA	Hydraulic pump	[124]
ELMD + Kullback-Leibler divergence + HAF	Natural gas pipeline	[42]
LMD+ Independent component analysis (ICA)	Biomedical source separation	[28]
LMD+Time-frequency entropy	DC traction power supply system	[139]
LMD+Detrended fluctuation analysis (DFA)	Ionospheric scintillation	[140]
WT+ LMD	Hydro turbine	[141]

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