Rolling Bearing Diagnosis Based on Composite Multiscale Weighted Permutation Entropy

Gan, Xiong; Lü, Hong; Yang, Guangyou; Liu, Jing

doi:10.3390/e20110821

Cited by 12 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Under the coarse-graining framework, the MPE and CMPE usually present better performance and extract more potential information on the time series, compared with the single-scale PE method. Therefore, many works have employed MPE and CMPE to the application of feature extraction and health condition recognition to bearing diagnosis [28]- [30]. Moreover, under the multiple-scale framework, WPE applies for analyzing complex signals in entropy analysis compared to PE.…”

Section: Original and Composite Multiscale Permutation Entropymentioning

confidence: 99%

Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

et al. 2020

View full text Add to dashboard Cite

Fault diagnosis of rolling bearing is of great importance to ensure high reliability and safety in the industrial machinery system. Entropy measures are useful non-linear indicators for time series complexity analysis and have been widely applied in bearing fault diagnosis in the past decade. In this paper, an improved entropy measure is proposed, named Adaptive Multiscale Weighted Permutation Entropy (AMWPE). Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, an experimental bearing dataset is analyzed using the AMWPE and conventional entropy measures, and then multi-class SVM is adopted for fault type classification. Further, the robustness of different entropy measures against noise is studied by analyzing noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in bearing fault diagnosis under different fault types, severity degrees, and SNR levels. INDEX TERMS Fault diagnosis, rolling bearing, entropy measure, support vector machine. YU ZHANG received the B.Eng. degree from the

show abstract

Section: Original and Composite Multiscale Permutation Entropymentioning

confidence: 99%

Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Based on the improvement of entropy, Zhang et al 19 utilized the multiscale bubble entropy to identify the fault types and the damage degree of rolling bearings. Gan et al 20 investigated the influence of multi-scale, fluctuation of each scale and amplitude fluctuation of data, and then proposed composite multiscale weighted permutation entropy for bearing diagnosis. Li et al 21 explored the application of refined composite multivariate multiscale dispersion entropy in rolling bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing state assessment based on the composite multiscale weight slope entropy and hierarchical prototype-based approach

Zhang¹,

Pan

Zhang³

et al. 2022

Advances in Mechanical Engineering

View full text Add to dashboard Cite

To evaluate the state of rolling bearing more accurately, a new feature called composite multiscale weight slope entropy was proposed for the complexity measurement of vibration signals. On the basis of analyzing the fault signal structure, the new feature could consider the influence of the nonlinearity, the multiscale characteristics, the fluctuation of the amplitude, and the amplitude itself on the fault signals. In the following, different fault types and the corresponding damage degree of rolling bearings were identified with the hierarchical prototype-based approach. Compared with the results of different modified slope entropy, it is shown that composite multiscale weight slope entropy could significantly improve the identification accuracy. In the two designed testing schemes, ten and sixty state types of rolling bearings are respectively calculated, and the identification accuracy could reach up to 100% and 96.5% respectively, which illustrate the effectiveness and the validity of the proposed approach.

show abstract

“…However, WPE is only based on a single-scale measure. Therefore, a multiscale weighted permutation entropy (MWPE), which takes into account the complex temporal fluctuations inherent in the sequence, is proposed as an improved version of WPE [ 30 , 31 , 32 ]. However, it also exposes some shortcomings of MWPE.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve

Song

Wang

Zhao

et al. 2022

Entropy

View full text Add to dashboard Cite

In order to effectively extract the key feature information hidden in the original vibration signal, this paper proposes a fault feature extraction method combining adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multiscale weighted permutation entropy (RTSMWPE). The proposed method focuses on two aspects: solving the serious modal aliasing problem of local mean decomposition (LMD) and the dependence of permutation entropy on the length of the original time series. First, by adding a sine wave with a uniform phase as a masking signal, adaptively selecting the amplitude of the added sine wave, the optimal decomposition result is screened by the orthogonality and the signal is reconstructed based on the kurtosis value to remove the signal noise. Secondly, in the RTSMWPE method, the fault feature extraction is realized by considering the signal amplitude information and replacing the traditional coarse-grained multi-scale method with a time-shifted multi-scale method. Finally, the proposed method is applied to the analysis of the experimental data of the reciprocating compressor valve; the analysis results demonstrate the effectiveness of the proposed method.

show abstract

Rolling Bearing Diagnosis Based on Composite Multiscale Weighted Permutation Entropy

Cited by 12 publications

References 22 publications

Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

Rolling bearing state assessment based on the composite multiscale weight slope entropy and hierarchical prototype-based approach

Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve

Contact Info

Product

Resources

About