Improved Multivariate Hierarchical Multiscale Dispersion Entropy: A New Method for Industrial Rotating Machinery Fault Diagnosis

This paper proposes an Improved Multivariate Multiscale Dispersion Entropy(IMMDE) combined with Hierarchical Entropy(HE) for vibration signal feature extraction. The traditional coarse-grained calculation is missing the relationship between neighboring sample points in the shift operation, which may...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.1-1
Main Authors: Tang, Zhuang, Liu, Jie, Li, Chaofeng
Format: Article
Language:English
Subjects:
Dispersion
Entropy
Fault diagnosis
Feature extraction
Health Condition Identification
Improved Multivariate Hierarchical Multiscale Dispersion Entropy
Mathematical analysis
MIMO communication
mRMR
Multivariate analysis
Redundancy
Rotating machinery
Support vector machines
Time series analysis
Vibrations
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Summary:This paper proposes an Improved Multivariate Multiscale Dispersion Entropy(IMMDE) combined with Hierarchical Entropy(HE) for vibration signal feature extraction. The traditional coarse-grained calculation is missing the relationship between neighboring sample points in the shift operation, which may lead to missing fault information. Secondly, as the scale increases, the original sequence is gradually shortened, leading to instability and inaccuracy in entropy estimation when dealing with short-term sequences. The improved coarse-grained calculation method overcomes its limitations to improve stability, and the HE method extracts deep fault frequency information from the high and low-frequency components of the multivariate signal. Then, the extracted features are dimensioned using the Max-Relevance Min-Redundancy (mRMR) to create a new set of fault features to improve diagnosis efficiency. Finally, the Support Vector Machine(SVM) determines the degree and type of fault. Experiments were conducted with three examples, the results show that IMHMDE can effectively extract the feature information according to mechanical faults' features and improve the efficiency of fault diagnosis.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3209231