SWDAE: A New Degradation State Evaluation Method for Metro Wheels with Interpretable Health Indicator Construction based on Unsupervised Deep Learning

Machine learning has shown advantages in assessing wheel degradation in metro vehicles for fault prognostic and health management (PHM). However, practical implementation faces challenges due to disturbances in wheel vibration signals caused by factors like load, road conditions, and temperature, in...

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Vydáno v:	IEEE transactions on instrumentation and measurement Ročník 73; s. 1
Hlavní autoři:	Mao, Wentao, Wang, Yu, Feng, Ke, Kou, Linlin, Zhang, Yanna
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Deep autoencoder Deep learning Degradation Degradation evaluation Feature extraction Frequencies Health indicator Interpretability Machine learning Maintenance engineering Principal components analysis Road conditions Second-generation wavelet transform Time-frequency analysis Vibrations Wavelet analysis Wavelet transforms Wheels
ISSN:	0018-9456, 1557-9662
On-line přístup:	Získat plný text
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