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...

Celý popis

Podrobná bibliografia

Vydané v:	IEEE transactions on instrumentation and measurement Ročník 73; s. 1 - 10
Hlavní autori:	Li, Shuaiyong, Zhang, Chao, Zhang, Xuyuntao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!