Fault Diagnosis of Planetary Gearbox Based on Hierarchical Refined Composite Multiscale Fuzzy Entropy and Optimized LSSVM

Efficient extraction and classification of fault features remain critical challenges in planetary gearbox fault diagnosis. A fault diagnosis framework is proposed that integrates hierarchical refined composite multiscale fuzzy entropy (HRCMFE) for feature extraction and a gray wolf optimization (GWO...

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Veröffentlicht in:Entropy (Basel, Switzerland) Jg. 27; H. 5; S. 512
Hauptverfasser: Xia, Xin, Wang, Xiaolu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 10.05.2025
MDPI
Schlagworte:
Algorithms
Classification
Datasets
Deep learning
Entropy
Fault diagnosis
Fault location (Engineering)
Feature extraction
Gearboxes
Gearing, Planetary
hierarchical entropy
Machine learning
Methods
optimized LSSVM
planetary gearbox
refined composite multiscale fuzzy entropy
Support vector machines
Time series
China
optimized LSSVM
planetary gearbox
fault diagnosis
refined composite multiscale fuzzy entropy
hierarchical entropy
ISSN:1099-4300, 1099-4300
Online-Zugang:Volltext
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Zusammenfassung:Efficient extraction and classification of fault features remain critical challenges in planetary gearbox fault diagnosis. A fault diagnosis framework is proposed that integrates hierarchical refined composite multiscale fuzzy entropy (HRCMFE) for feature extraction and a gray wolf optimization (GWO)-optimized least squares support vector machine (LSSVM) for classification. Firstly, the HRCMFE is developed for feature extraction, which combines the segmentation advantage of hierarchical entropy (HE) and the computational stability advantage of refined composite multiscale fuzzy entropy (RCMFE). Secondly, the hyperparameters of LSSVM are optimized by GWO using a proposed fitness function. Finally, fault diagnosis of the planetary gearbox is achieved by the optimized LSSVM using the HRCMFE-extracted features. Simulation and experimental study results indicate that the proposed method demonstrates superior effectiveness in both feature discriminability and diagnosis accuracy.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e27050512