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Fault Diagnosis of Bearing Based on Improved Refined Composite Hierarchical Fuzzy Entropy and Least Squares Support Vector Machine

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Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Purpose

To improve the recognition accuracy of the existing fuzzy entropy method for bearing fault signals, a novel processing method by combining improved refined composite hierarchical fuzzy entropy (IRCHFE) and least square support vector machine (LSSVM) is proposed.

Methods

In this method, an improved refined composite hierarchical analysis applies to solve the problems of incomplete extraction of the existing multi-scale fuzzy entropy (MFE) fault information, inaccurate estimation of the entropy value of the hierarchical fuzzy entropy (HFE), and the inability of the hierarchical analysis to handle arbitrary data lengths. First, the IRCHFE method applies to extract fault information from the vibration signal. Then, LSSVM effectively recognizes and classifies the extracted fault information. Furthermore, the application of PSO to adjust the parameters of LSSVM increase classification accuracy.

Results

The superiority of the IRCHFE-LSSVM method is verified by the bearing experimental data of Case Western Reserve University and Paderborn University. The experimental results show that the IRCHFE-LSSVM has a fault recognition accuracy of 100%. The comparative experiment results display that the proposed IRCHFELSSVM method is superior to MFE-LSSVM and HFE-LSSVM in identifying bearing fault types.

Conclusions

The combined method of IRCHFE and LSSVM for fault diagnosis can provide certain guidance for the engineering application of fault diagnosis.

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Abbreviations

ApEn:

Approximate entropy

EMD:

Empirical mode decomposition

FE:

Fuzzy entropy

HFE:

Hierarchical fuzzy entropy

IRCHFE:

Improved refined composite hierarchical fuzzy entropy

LSSVM:

Least square support vector machine

MFE:

Multi-scale fuzzy entropy

MF:

Morphological filtering

SampEn:

Sample entropy

SVM:

Support vector machines

PCA:

Principal component analysis

PSO:

Particle swarm optimization

WT:

Wavelet transform

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Authors and Affiliations

  1. School of Automation, Harbin University of Science and Technology, Harbin, China

    Hong-qi Wang & Li-ying Yuan

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Correspondence to Hong-qi Wang.

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Wang, Hq., Yuan, Ly. Fault Diagnosis of Bearing Based on Improved Refined Composite Hierarchical Fuzzy Entropy and Least Squares Support Vector Machine. J. Vib. Eng. Technol. 10, 3025–3036 (2022). https://doi.org/10.1007/s42417-022-00534-8

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  • DOI: https://doi.org/10.1007/s42417-022-00534-8

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