A novel deep autoencoder feature learning method for rotating machinery fault diagnosis

Mechanical Systems and Signal Processing

Volumn 95, Issue , 2017, Pages 187-204

  Shao, Haidong ^a   Jiang, Hongkai ^a   Zhao, Huiwei ^a   Wang, Fuan ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

Artificial fish swarm algorithm; Deep autoencoder; Fault diagnosis; Feature learning; Maximum correntropy

Indexed keywords

FAILURE ANALYSIS; LEARNING SYSTEMS; MACHINERY; ROLLER BEARINGS; ROTATING MACHINERY;

ARTIFICIAL FISH SWARM ALGORITHMS; AUTO ENCODERS; CORRENTROPY; FEATURE LEARNING; MACHINERY FAULT DIAGNOSIS; OPERATION CONDITIONS; SIGNAL FEATURES; VIBRATION SIGNAL;

FAULT DETECTION;

EID: 85018771228     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2017.03.034     Document Type: Article

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