Fault Diagnosis of a Rolling Bearing Based on Adaptive Sparest Narrow-Band Decomposition and RefinedComposite Multiscale Dispersion Entropy

Luo, Songrong; Yang, Wenxian; Luo, Yi

doi:10.3390/e22040375

Cited by 24 publications

(22 citation statements)

References 31 publications

(49 reference statements)

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“…Then the probability of each coarse-grained sequence’s distribution pattern is calculated, and the average of the probability is calculated. Finally, RCMDE is calculated according to Equation (7) [ 23 ]. RCMDE not only reduces the loss of information in the coarse-grained process of the MDE algorithm but also effectively solves the influence of the initial point position on the signal-processing results.…”

Section: Methodsmentioning

confidence: 99%

Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings

Wang

Zhang

2021

Sensors

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We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD–RCMDE–SSA–SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.

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Section: Methodsmentioning

confidence: 99%

Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings

Wang

Zhang

2021

Sensors

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“…Where  represents the scale factor. (6) According to Equations (1) -(5), the MHMDE values corresponding to the hierarchical component , k e X of the original random series X can be calculated by Equation (10). It can be seen from the principle of MHMDE that this approach combines MHE and MMSE.…”

Section: B Modified Hierarchical Multiscale Dispersion Entropymentioning

confidence: 99%

“…Entropy can effectively measure the dynamic features of complex time series, so it has been diffusely applied to identify different fault states of rotating machinery in recent years [10], [11]. Commonly applied entropy approaches include sample entropy (SE) [12], fuzzy entropy (FE) [13], and permutation entropy (PE) [14].…”

Section: Introductionmentioning

confidence: 99%

Modified Hierarchical Multiscale Dispersion Entropy and its Application to Fault Identification of Rotating Machinery

et al. 2020

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The rotating machinery possesses complicated structures and various fault types, whose health state monitoring is essential for the normal production and operation of the equipment. To distinguish different working states of rotating machinery efficiently and accurately, this paper presents a novel approach for extracting fault features of vibration signals called modified hierarchical multiscale dispersion entropy (MHMDE). And on this basis, an innovative approach for fault diagnosis of rotating machinery based on MHMDE, multi-cluster feature selection (MCFS) and particle swarm optimization kernel extreme learning machine (PSO-KELM) is developed. Firstly, MHMDE is employed to extract the high-dimensional fault features of rotating machinery. This approach can effectively overcome the shortcomings that multi-scale entropy only focuses on the information in the low-frequency components but discards the high-frequency information, as well as the significant dropping of efficiency if the number of hierarchical layers of hierarchical entropy is large. Then MCFS is employed to screen the sensitive features from the high-dimensional fault features. Finally, the sensitive feature vectors are input into the PSO-KELM-based fault classifier to complete the rotating machinery fault diagnosis. It is proved that the presented approach can effectively identify different fault states of rotating machinery through three typical examples. Meanwhile, the presented approach is compared with multi-scale dispersion entropy (MDE) and hierarchical dispersion entropy (HDE), etc. The results show that the presented approach possesses more superior performance.

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“…For this, Yi et al [13] proposed the multiscale dispersion entropy which could realize fault feature extraction of vibration signals under multiscale. Luo et al [14] proposed refined composite multi-scale dispersion entropy to realize fault diagnosis of rolling bearing by improving coarsening process. is method realized the feature extraction in the multiscale of vibration signals.…”

Section: Introductionmentioning

confidence: 99%

A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy

Peng

Zhao

Jiang

2021

Shock and Vibration

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In the process of fault feature extraction of rolling bearing, the feature information is difficult to be extracted fully. A novel method of fault feature extraction called hierarchical dispersion entropy is proposed in this paper. In this method, the vibration signals firstly are decomposed hierarchically. Secondly, dispersion entropies of different nodes are calculated. Hierarchical dispersion entropy can realize the comprehensive feature extraction of the high- and low-frequency band information of vibration signals and overcome the problems that dispersion entropy and multiscale dispersion entropy are insufficient to extract the fault feature information of vibration signals. The feasibility of hierarchical dispersion entropy is obtained by analyzing the hierarchical dispersion entropy of Gaussian white noise and compared with the multiscale dispersion entropy of Gaussian white noise. Meanwhile, a fault diagnosis method for rolling bearings based on hierarchical dispersion entropy and k-nearest neighbor (KNN) classifier is developed. Finally, the superiority of the proposed fault diagnosis method is verified in the realization of fault diagnosis of the rolling bearing in different positions and different degrees of damage.

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Fault Diagnosis of a Rolling Bearing Based on Adaptive Sparest Narrow-Band Decomposition and RefinedComposite Multiscale Dispersion Entropy

Cited by 24 publications

References 31 publications

Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings

Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings

Modified Hierarchical Multiscale Dispersion Entropy and its Application to Fault Identification of Rotating Machinery

A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy

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