Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

Signal Processing

Volumn 130, Issue , 2017, Pages 377-388

  Lu, Chen ^a   Wang, Zhen Ya ^a   Qin, Wei Li ^a   Ma, Jian ^a  

^a BEIHANG UNIVERSITY   (China)

Author keywords

Deep learning; Fault diagnosis; Health state identification; Stacked denoising autoencoder

Indexed keywords

FAILURE ANALYSIS; HEALTH; ITERATIVE METHODS; LEARNING SYSTEMS; MACHINERY; ROTATING MACHINERY;

AUTO ENCODERS; DEEP LEARNING; ECONOMICAL EFFICIENCY; FAULT DIAGNOSIS METHOD; FEATURE REPRESENTATION; HEALTH STATE; HIERARCHICAL STRUCTURES; RELIABILITY IMPROVEMENT;

FAULT DETECTION;

EID: 84982792319     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2016.07.028     Document Type: Article

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