Rolling Bearing Fault Diagnosis Based on Multi-Modal Variational Autoencoders

With the development of Industry 4.0, more and more attention has been paid to system intelligent maintenance by various industries, among which rolling bearing is an indispensable and most important component. Existing methods have such limitations as the need for prior knowledge and manual feature...

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Vydáno v:2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) s. 1 - 5
Hlavní autoři: Xiong, Manjun, Wu, Yifan, Li, Chuan, Yang, Zhe
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 30.11.2022
Témata:
Fault diagnosis
Federated learning
Industries
intelligent computing
Maintenance engineering
Manuals
multi-modality
rolling bearing
Rolling bearings
Training
variational autoencoder
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Shrnutí:With the development of Industry 4.0, more and more attention has been paid to system intelligent maintenance by various industries, among which rolling bearing is an indispensable and most important component. Existing methods have such limitations as the need for prior knowledge and manual feature extraction. For this reason, a multi-modal variational autoencoder (MMVAE) is proposed to extract useful features from multiple modalities. Firstly, the fault characteristics of multiple modalities are extracted separately by different variational autoencoders containing complementary information. Secondly, a collaborative training method is proposed to maximize mutual consistency. Specifically, feature extraction and clustering for all modalities are employed for collaborative learning. Fault diagnosis experiments on a benchmark rolling bearing dataset were carried out. Compared with other methods, MMVAE showed remarkable results, with an accuracy of 99.13%.
DOI:10.1109/ICSMD57530.2022.10058404