The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder

The prediction of the remaining useful life (RUL) is important for the conditions of rotating machinery to maintain reliability and decrease losses. This study proposes an efficient approach based on an adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD) and a convolutional...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 24; no. 8; p. 2382
Main Authors:	Gao, Yangde, Ahmad, Zahoor, Kim, Jong-Myon
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 09.04.2024 MDPI
Subjects:	Bearings Communication convolutional LSTM autoencoder Deep learning Eigenvalues health index analysis Kurtosis Machinery Neural networks remaining useful life Signal processing Support vector machines Useful life health index analysis convolutional LSTM autoencoder remaining useful life
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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