A Novel Spatiotemporal Enhanced Convolutional Autoencoder Network for Unsupervised Health Indicator Construction

The health indicator (HI) of rotating machinery affects the reliability and accuracy of its remaining useful life (RUL) prediction. Convolutional autoencoder (CAE) is widely used for HI construction with the advantage of being unsupervised, but it still suffers from two problems: 1) the shallow lear...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 73; S. 1 - 10
Hauptverfasser:	Li, Shuaiyong, Zhang, Chao, Zhang, Xuyuntao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Convolution Data mining Feature extraction Health indicator (HI) Machinery Mining Modules remaining useful life (RUL) Roller bearings Rotating machinery spatiotemporal enhanced convolutional autoencoder (STEACE) Time-domain analysis Time-varying systems unsupervised learning
ISSN:	0018-9456, 1557-9662
Online-Zugang:	Volltext
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