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Shock and Vibration
To enhance the performance of deep auto-encoder (AE) under complex working conditions, a novel deep auto-encoder network method for rolling bearing fault diagnosis is proposed in this paper. First, multiscale analysis is adopted to extract the multiscale features from the raw vibration signals of rolling bearing. Second, the sparse penalty term and contractive penalty term are used simultaneously to regularize the loss function of auto-encoder to enhance the feature learning ability ofdoi:10.1155/2020/8891905 fatcat:u5udyz4o6ndynljuuttbpt6eo4