Rolling Bearing Fault Diagnosis Based on CNN-LSTM with FFT and SVD

In the industrial sector, accurate fault identification is paramount for ensuring both safety and economic efficiency throughout the production process. However, due to constraints imposed by actual working conditions, the motor state features collected are often limited in number and singular in na...

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Detalhes bibliográficos

Principais autores:	Muzi Xu, Qianqian Yu, Shichao Chen, Jianhui Lin
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2024-07-01
coleção:	Information
Assuntos:	fault diagnosis deep learning timing prediction signal processing
Acesso em linha:	https://www.mdpi.com/2078-2489/15/7/399
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