A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy

Li, Yongbo; Xu, Min; Wang, Rixin; Huang, Wenhu

doi:10.1016/j.jsv.2015.09.016

Cited by 197 publications

(132 citation statements)

References 34 publications

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“…Rolling bearings are the most important parts of rotating machinery, which are easily damaged by load, friction and damping in the course of operation [1]. Therefore, the feature extraction and pattern recognition of bearings diagnosis are very important.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

Gao

Villecco

et al. 2017

Entropy

140

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Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing.

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Section: Introductionmentioning

confidence: 99%

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

Gao

Villecco

et al. 2017

Entropy

140

View full text Add to dashboard Cite

show abstract

“…There is no doubt that fault diagnosis technology, based on vibration signal processing, is critical for monitoring the health of key structures or equipment [1][2][3][4][5]. Generally, different sensors are utilized to obtain mechanical vibration information, from which the features of the running state are characterized [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Research on the Blind Source Separation Method Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD and Its Application in Diagnosing Rolling-Bearing Faults

Xiao

et al. 2017

Applied Sciences

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Abstract:To improve the performance of single-channel, multi-fault blind source separation (BSS), a novel method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD) is proposed in this paper. The RPSEMD method is used to decompose the original single-channel vibration signal into several intrinsic mode functions (IMFs), with the obtained IMFs and original signal together forming a new observed signal for the dimensional lifting. Therefore, an undetermined problem is transformed into a positive definite problem. Compared with the existing EMD method and its improved version, the proposed RPSEMD method performs better in solving the mode mixing problem (MMP) by employing sinusoid-assisted technology. Meanwhile, it can also reduce the computational load and reconstruction errors. The number of source signals is estimated by adopting singular value decomposition (SVD) and Bayes information criterion (BIC). Simulation analysis has demonstrated the superiority of this method being applied in multi-fault BSS. Furthermore, its effectiveness in identifying the multi-fault features of rolling-bearing has been also verified based on a test rig.

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“…However, in MSE (SampEn) the step function used for measuring similarity will cause the mutation of similarity measurements for shorter time series. Aimed at resolving this problem, multiscale fuzzy entropy (MFE) was proposed [13,14] by using fuzzy entropy [15,16] replacing sample entropy in MSE and the research indicates that MFE can get much better stability and consistency than MSE.…”

Section: Introductionmentioning

confidence: 99%

A Refined Composite Multivariate Multiscale Fuzzy Entropy and Laplacian Score-Based Fault Diagnosis Method for Rolling Bearings

Zheng

Pan

et al. 2017

Entropy

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Abstract:The vibration signals of rolling bearings are often nonlinear and non-stationary. Multiscale entropy (MSE) has been widely applied to measure the complexity of nonlinear mechanical vibration signals, however, at present only the single channel vibration signals are used for fault diagnosis by many scholars. In this paper multiscale entropy in multivariate framework, i.e., multivariate multiscale entropy (MMSE) is introduced to machinery fault diagnosis to improve the efficiency of fault identification as much as possible through using multi-channel vibration information. MMSE evaluates the multivariate complexity of synchronous multi-channel data and is an effective method for measuring complexity and mutual nonlinear dynamic relationship, but its statistical stability is poor. Refined composite multivariate multiscale fuzzy entropy (RCMMFE) was developed to overcome the problems existing in MMSE and was compared with MSE, multiscale fuzzy entropy, MMSE and multivariate multiscale fuzzy entropy by analyzing simulation data. Finally, a new fault diagnosis method for rolling bearing was proposed based on RCMMFE for fault feature extraction, Laplacian score and particle swarm optimization support vector machine (PSO-SVM) for automatic fault mode identification. The proposed method was compared with the existing methods by analyzing experimental data analysis and the results indicate its effectiveness and superiority.

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A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy

Cited by 197 publications

References 34 publications

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

Research on the Blind Source Separation Method Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD and Its Application in Diagnosing Rolling-Bearing Faults

A Refined Composite Multivariate Multiscale Fuzzy Entropy and Laplacian Score-Based Fault Diagnosis Method for Rolling Bearings

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