A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination

Zheng, Jianhua; Cheng, Junsheng; Yu, Yang; Luo, Songrong

doi:10.1016/j.mechmachtheory.2014.03.014

Cited by 183 publications

(130 citation statements)

References 24 publications

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“…The variance trends of SampEn and improved FuzzyEn demonstrate that the entropy values keep stable for a long-time period when bearing is running under normal condition and begin to decrease as early fault occurs, which means that the vibration signals of healthy bearing are more complex than those of faulty bearing. According to the literature [24], this can be explained by the fact that the vibration signals under normal condition have lower self-similarity because of its randomness and irregularity, and the self-similarity will increase as the fault appears. As for the original FuzzyEn, some of its values in bearing degradation area (nearby the final failure) are much larger than these under healthy condition, which will result in mistaken decision.…”

Section: Results and Analysismentioning

confidence: 99%

“…Nevertheless, the fuzzy function used in FuzzyEn by Chen et al was deemed to lack clear physical meanings [23]. Therefore, Zheng et al [24] defined a new fuzzy function and used the optimized one to measure the complexity of vibration signals of rolling element bearings. However, the limitation of FuzzyEn is that it neglects the global characteristics of the signal and thus may produce inaccurate results [25].…”

Section: Introductionmentioning

confidence: 99%

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Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

Zhu

Jiang

Chen

et al. 2017

Measurement Science Review

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Rolling element bearings are an important unit in the rotating machines, and their performance degradation assessment is the basis of condition-based maintenance. Targeting the non-linear dynamic characteristics of faulty signals of rolling element bearings, a bearing performance degradation assessment approach based on improved fuzzy entropy (FuzzyEn) is proposed in this paper. FuzzyEn has less dependence on data length and achieves more freedom of parameter selection and more robustness to noise. However, it neglects the global trend of the signal when calculating similarity degree of two vectors, and thus cannot reflect the running state of the rolling element bearings accurately. Based on this consideration, the algorithm of FuzzyEn is improved in this paper and the improved FuzzyEn is utilized as an indicator for bearing performance degradation evaluation. The vibration data from run-to-failure test of rolling element bearings are used to validate the proposed method. The experimental results demonstrate that, compared with the traditional kurtosis and root mean square, the proposed method can detect the incipient fault in advance and can reflect the whole performance degradation process more clearly.

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Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

Zhu

Jiang

Chen

et al. 2017

Measurement Science Review

View full text Add to dashboard Cite

show abstract

“…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

Self Cite

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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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“…Recently, entropy-based features are always used in IMFs and EMD such as information entropy and sample entropy [17][18][19]. Above these features, it has been proved that symbolic entropy has a good property in representing statistical regularity.…”

Section: Introductionmentioning

confidence: 99%

A Research on Maximum Symbolic Entropy from Intrinsic Mode Function and Its Application in Fault Diagnosis

Zhang

Liu

et al. 2017

Mathematical Problems in Engineering

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Empirical mode decomposition (EMD) is a self-adaptive analysis method for nonlinear and nonstationary signals. It has been widely applied to machinery fault diagnosis and structural damage detection. A novel feature, maximum symbolic entropy of intrinsic mode function based on EMD, is proposed to enhance the ability of recognition of EMD in this paper. First, a signal is decomposed into a collection of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal, and then IMFs are transformed into a serious of symbolic sequence with different parameters. Second, it can be found that the entropies of symbolic IMFs are quite different. However, there is always a maximum value for a certain symbolic IMF. Third, take the maximum symbolic entropy as features to describe IMFs from a signal. Finally, the proposed features are applied to evaluate the effect of maximum symbolic entropy in fault diagnosis of rolling bearing, and then the maximum symbolic entropy is compared with other standard time analysis features in a contrast experiment. Although maximum symbolic entropy is only a time domain feature, it can reveal the signal characteristic information accurately. It can also be used in other fields related to EMD method.

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A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination

Cited by 183 publications

References 24 publications

Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

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

A Research on Maximum Symbolic Entropy from Intrinsic Mode Function and Its Application in Fault Diagnosis

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