期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 16, 期 1, 页码 339-349出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2917233
关键词
Task analysis; Fault diagnosis; Feature extraction; Kernel; Training; Training data; Deep learning; Convolutional neural network (CNN); fault diagnosis; rotary machinery; transfer learning
类别
资金
- National Key R&D Program of China [2018YFB1702400]
- National Natural Science Foundation of China [51875208]
Deep neural networks present very competitive results in mechanical fault diagnosis. However, training deep models require high computing power while the performance of deep architectures in extracting discriminative features for decision making often suffers from the lack of sufficient training data. In this paper, a transferable convolutional neural network (CNN) is proposed to improve the learning of target tasks. First, a one-dimensional CNN is constructed and pretrained based on large source task datasets. Then a transfer learning strategy is adopted to train a deep model on target tasks by reusing the pretrained network. Thus, the proposed method not only utilizes the learning power of deep network but also leverages the prior knowledge from the source task. Four case studies are considered and the effects of transfer layers and training sample size on classification effectiveness are investigated. Results show that the proposed method exhibits better performance compared with other algorithms.
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