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Application of hierarchical symbolic fuzzy entropy and sparse Bayesian ELM to bearing fault diagnosis

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Abstract

Bearing fault diagnosis is essential for reducing equipment operating and maintenance costs. We propose a bearing fault diagnosis method that combines hierarchical symbolic fuzzy entropy (HSFE) and sparse Bayesian extreme learning machine (SBELM). Multiscale symbolic fuzzy entropy (MSFE) is a recently proposed fault diagnosis method. Compared with multiscale sample entropy (MSE), multiscale permutation entropy (MPE), and multiscale fuzzy entropy (MFE), MSFE has high noise resistance and computational efficiency. The multiscale analysis method using the average operator can only extract information in the low frequency component, but cannot use the feature information in the high frequency component. Aiming at this defect, symbolic fuzzy entropy is formed by combining the hierarchical decomposition with the symbolic fuzzy entropy. Hierarchical decomposition uses average and difference operators to decompose the sequence, which can extract the fault information of high-frequency and low-frequency components at the same time. Then, the extracted fault information is efficiently identified and classified using the SBELM. The effectiveness and superiority of the HSFE method are verified by simulation signals and experimental vibration signals. At the same time, experimental comparisons were carried out using MPE, HPE, MSE, HSE, MSFE and HSFE. The experimental results indicate that the HSFE method has the best effect on the identification of rotating machinery fault types.

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Abbreviations

AE :

Approximate entropy

SE :

Sample entropy

PE :

Permutation entropy

FE :

Fuzzy entropy

MSE :

Multi-scale entropy

SVM :

Support vector machine

ELM :

Extreme learning machine

RVM :

Relevance vector machines

MPE :

Maximum entropy partitioning

KELM :

Kernel-based extreme learning machine

SBELM :

Sparse Bayesian extreme learning machine

MPE :

Multi-scale permutation entropy

MSE :

Multi-scale sample entropy

MFE :

Multi-scale fuzzy entropy

MSFE :

Multi-scale symbolic fuzzy entropy

SDF :

Symbol dynamic filtering

HSFE :

Hierarchical symbolic fuzzy entropy

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Authors and Affiliations

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

    Liying Yuan & Hongqi Wang

Authors
  1. Liying Yuan
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  2. Hongqi Wang
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Corresponding author

Correspondence to Hongqi Wang.

Additional information

Liying Yuan, Professor, received her Ph.D. from Harbin Institute of Technology in 2008. Her main research interests are image processing and fault detection.

Hongqi Wang is currently a Master’s student at Harbin University of Science and Technology. His main research direction is fault diagnosis.

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Yuan, L., Wang, H. Application of hierarchical symbolic fuzzy entropy and sparse Bayesian ELM to bearing fault diagnosis. J Mech Sci Technol 37, 2241–2252 (2023). https://doi.org/10.1007/s12206-023-0401-1

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  • DOI: https://doi.org/10.1007/s12206-023-0401-1

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