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Gearbox damage identification strategy based on RCMFME and AO-ELM

Published:2024-03-06 author:SHEN Yu, ZHAO Xu. Browse: 466 Check PDF documents

Gearbox damage identification strategy based on RCMFME and AO-ELM

SHEN Yu1, ZHAO Xu2

(1.School of Mechanical and Electrical Engineering, Henan Quality Polytechnic, Pingdingshan 467000, China; 2.School of

Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Haikou 571126, China)

Abstract: Aiming at the defect that the fuzzy entropy only considered the local feature of the signal and ignored the global feature of the signal, which led to the poor accuracy of gearbox fault identification, a gearbox fault diagnosis method based on the refined composite multiscale fuzzy measure entropy (RCMFME) and the aquila optimizer (AO)optimized extreme learning machine (ELM) was proposed. Firstly, based on the refined composite multiscale fuzzy entropy, a new RCMFME method was proposed to consider both local and global features of time series by improving the construction of vectors.Subsequently, RCMFME index was used to extract the entropy value of the gearbox vibration signal and construct a fault feature vector.Then, the AO algorithm was used to adaptively search for the parameters of the extreme learning machine, generating a multi class classifier with the best parameters.Finally, the fault feature vectors of the training samples were input into the AO-ELM classification model, and the model was trained to construct the best performing classifier, achieving the goal of fault recognition for gearbox test samples.Experiments were conducted using two types of gearbox vibration datasets, and the recognition accuracy and stability were compared with relevant feature extraction methods. The research results show that the fault diagnosis methods based on RCMFME and AO-ELM can respectively achieve 100% and 98% classification accuracy, and the average recognition accuracy respectively reaches 100% and 98%, which is superior to refined composite multiscale global fuzzy entropy(RCMGFE), refined composite multiscale fuzzy entropy(RCMFE), refined composite multiscale sample entropy(RCMSE), and it has significant application potential.
Key words: gearbox fault diagnosis; refined composite multiscale fuzzy measure entropy(RCMFME); aquila optimizer(AO); extreme learning machine(ELM); AO-ELM classification model; feature extraction

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