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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Sensors (Basel, Switzerland) Ročník 24; číslo 8; s. 2382
Hlavní autori:	Gao, Yangde, Ahmad, Zahoor, Kim, Jong-Myon
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 09.04.2024 MDPI
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!