Learning to monitor machine health with convolutional Bi-directional LSTM networks

Sensors (Switzerland)

Volumn 17, Issue 2, 2017, Pages

  Zhao, Rui ^a,b   Yan, Ruqiang ^a   Wang, Jinjiang ^c   Mao, Kezhi ^b  

^a SOUTHEAST UNIVERSITY   (China)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c CHINA UNIVERSITY OF PETROLEUM   (China)

Author keywords

Bi directional long short term memory network; Convolutional neural network; Machine health monitoring; Recurrent neural network; Tool wear prediction

Indexed keywords

BRAIN; CONVOLUTION; DEEP LEARNING; DEEP NEURAL NETWORKS; FEATURE EXTRACTION; FORECASTING; HEALTH; INDUSTRIAL RESEARCH; MANUFACTURE; MONITORING; NEURAL NETWORKS; RECURRENT NEURAL NETWORKS; REGRESSION ANALYSIS; WEAR OF MATERIALS;

CONVOLUTIONAL NEURAL NETWORK; DATA-DRIVEN METHODS; LONG-TERM DEPENDENCIES; MACHINE HEALTH MONITORING; NEURAL NETWORK STRUCTURES; SHORT TERM MEMORY; TEMPORAL INFORMATION; TOOL WEAR;

LONG SHORT-TERM MEMORY;

ARTICLE; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; FEATURE EXTRACTION; HUMAN; LINEAR REGRESSION ANALYSIS; LONG SHORT TERM MEMORY NETWORK; NOISE; PREDICTION;

EID: 85011676262     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s17020273     Document Type: Article

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