An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm

•Deep base models are constructed to adaptively learn latent features from vibration signals.•Multiple diverse deep base models are acquired by variants of AEs and Bootstrap.•EWV with class-specific thresholds is designed to implement selective ensemble.•BAS algorithm is used to optimize the class-s...

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Vydané v:Mechanical systems and signal processing Ročník 142; s. 106752
Hlavní autori: Li, Xingqiu, Jiang, Hongkai, Niu, Maogui, Wang, Ruixin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin Elsevier Ltd 01.08.2020
Elsevier BV
Predmet:
Algorithms
Beetle antennae search algorithm
Deep learning
Enhanced selective ensemble deep learning
Enhanced weighted voting combination strategy
Fault diagnosis
Machine learning
Noise reduction
Performance enhancement
Roller bearings
Rolling bearing
Search algorithms
Teaching methods
Thresholds
Fault diagnosis
Enhanced selective ensemble deep learning
Enhanced weighted voting combination strategy
Beetle antennae search algorithm
Rolling bearing
ISSN:0888-3270, 1096-1216
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Shrnutí:•Deep base models are constructed to adaptively learn latent features from vibration signals.•Multiple diverse deep base models are acquired by variants of AEs and Bootstrap.•EWV with class-specific thresholds is designed to implement selective ensemble.•BAS algorithm is used to optimize the class-specific thresholds of the EWV. Rolling bearing fault diagnosis is a meaningful yet challengeable task. To improve the performance of rolling bearing fault diagnosis, this paper proposes an enhanced selective ensemble deep learning method with beetle antennae search (BAS) algorithm. Firstly, multiple deep base models are constructed to automatically capture sensitive features from raw vibration signals. Secondly, to ensure the diversity of the base models, sparse autoencoder, denoising autoencoder and linear decoder are used to construct different deep autoencoders, respectively, and also Bootstrap is used to design distinctive training data subsets for each base model. Thirdly, an enhanced weighted voting (EWV) combination strategy with class-specific thresholds is proposed to implement selective ensemble learning. Finally, BAS algorithm is used to adaptively select the optimal class-specific thresholds. Experimental bearing data are used to verify the effectiveness of the proposed method. The results suggest that the proposed method can more accurately and robustly recognize different kind of faults than both the individual base models and other existing ensemble learning methods.
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ISSN:0888-3270
1096-1216
DOI:10.1016/j.ymssp.2020.106752