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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Neural computing & applications Ročník 34; číslo 8; s. 5883 - 5904
Hlavní autori:	Wang, Chao, Tao, Laifa, Ding, Yu, Lu, Chen, Ma, Jian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.04.2022 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!