An autoencoder with adaptive transfer learning for intelligent fault diagnosis of rotating machinery. (26th March 2021)

• Record Type: Journal Article
• Authors: Tang, Zhi; Bo, Lin; Liu, Xiaofeng; Wei, Daiping
• Is Part Of: Measurement science & technology. Volume 32:Number 5(2021)
• Publication Date: 2021-03-26
• Subjects: rotating machinery -- intelligent fault diagnosis -- autoencoder -- transfer learning -- adaptive optimization; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/abd650
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Under variable working conditions, a problem arises, which is that it is difficult to obtain enough labeled data; to address this problem, an adaptive transfer autoencoder (ATAE) is established to diagnose faults in rotating machinery. First, a data adaptation module, which calculates the maximum mean discrepancy for the network hidden-layer data in reproducing kernel Hilbert space, is introduced to the autoencoder network, thus making the classification model operate under variable working conditions. Variation particle-swarm optimization is then invoked to optimize the data adaptation parameters. Finally, the k-nearest neighbors algorithm, as the classification layer of the network, identifies the state of health of the rotating machinery. The capabilities of the intelligent fault-diagnosis network are verified using vibration signals from a bearing test rig and a gearbox test rig. The experimental results suggest that, compared with state-of-the-art diagnosis methods, the proposed ATAE network can significantly boost diagnostic performance in the absence of target vibration signal labels.