Deep Residual Networks with Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes

IEEE Transactions on Industrial Electronics

Volumn 65, Issue 5, 2018, Pages 4290-4300

  Zhao, Minghang ^a   Kang, Myeongsu ^b   Tang, Baoping ^a   Pecht, Michael ^b  

^a CHONGQING UNIVERSITY   (China)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

Deep residual learning; fault diagnosis; feature learning; planetary gearbox; wavelet packet transform

Indexed keywords

DEEP LEARNING; FAILURE ANALYSIS; FREQUENCY BANDS; GEARS; LEARNING ALGORITHMS; WAVELET ANALYSIS; WAVELET TRANSFORMS;

DATA-DRIVEN FAULT DIAGNOSIS; DEEP RESIDUAL LEARNING; FEATURE LEARNING; PLANETARY GEARBOXES; STATISTICAL PARAMETERS; VARIABLE OPERATING CONDITION; WAVELET PACKET COEFFICIENT; WAVELET PACKET TRANSFORMS;

FAULT DETECTION;

EID: 85031806343     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2017.2762639     Document Type: Article

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