Multi-sensor fusion fault diagnosis method of wind turbine bearing based on adaptive convergent viewable neural networks
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Elsevier BV
Reference55 articles.
1. A multi-module generative adversarial network augmented with adaptive decoupling strategy for intelligent fault diagnosis of machines with small sample;Zhang;Knowl-Based Syst,2022
2. Latest innovations in the field of condition-based maintenance of rotatory machinery: A review;Kumar;Meas Sci Technol,2023
3. Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application;Han;ISA Trans,2020
4. Learn generalized features via multi-source domain adaptation: Intelligent diagnosis under variable/constant machine conditions;Si;IEEE Sens J,2021
5. A comprehensive study on developing an intelligent framework for identification and quantitative evaluation of the bearing defect size;Kumar;Reliab Eng Syst Saf,2024
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