A feature fusion deep belief network method for intelligent fault diagnosis of rotating machinery

Author:

Jiang Hongkai1,Shao Haidong1,Chen Xinxia2,Huang Jiayang2

Affiliation:

1. School of Aeronautics, Northwestern Polytechnical University, Xi’an, People’s Republic of China

2. Shanghai Engineering Research Center of Civil Aircraft Monitoring, Shanghai, People’s Republic of China

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference20 articles.

1. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis;Jiang;Mechanical Systems and Signal Processing,2013

2. Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet;Shao;ISA Transactions,2017

3. Wavelet transform based on inner product in fault diagnosis of rotating machinery: Areview;Chen;Mechanical Systems and Signal Processing,2016

4. Automatic Feature extraction of time-seriesapplied to fault severity evaluation of helical gearbox instationary and non-stationary speed operation;Cabrera;Applied SoftComputing,2017

5. A multidimensional hybrid intelligent method for gear fault diagnosis;Lei;Expert Systems with Applications,2010

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