An adversarial model for electromechanical actuator fault diagnosis under nonideal data conditions

Electromechanical actuators (EMAs) are safety-critical components that work under various conditions and loads. Realizing robust and precise fault diagnosis for an EMA increases the overall availability/safety of the whole system. However, the monitoring data of an EMA are collected under different...

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Veröffentlicht in:	Neural computing & applications Jg. 34; H. 8; S. 5883 - 5904
Hauptverfasser:	Wang, Chao, Tao, Laifa, Ding, Yu, Lu, Chen, Ma, Jian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.04.2022 Springer Nature B.V
Schlagworte:	Actuators Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Critical components Data Mining and Knowledge Discovery Fault diagnosis Feature extraction Image Processing and Computer Vision Original Article Probability and Statistics in Computer Science Robustness Safety critical Signal processing Training Working conditions Fault diagnosis Few-shot learning Unbalanced samples Electromechanical actuator
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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