Journal
KNOWLEDGE-BASED SYSTEMS
Volume 207, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2020.106396
Keywords
Fault diagnosis; Rotating machinery; Ensemble transfer CNN; Multi-channel signals; Decision fusion
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Funding
- National Natural Science Foundation of China [51905160]
- Natural Science Foundation of Hunan Province [2020JJ5072]
- Fundamental Research Funds for the Central Universities [531118010335]
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Automatic and reliable fault diagnosis of rotating machinery cross working conditions is of practical importance. For this purpose, ensemble transfer convolutional neural networks (CNNs) driven by multi-channel signals are proposed in this paper. Firstly, a series of source CNNs modified with stochastic pooling and Leaky rectified linear unit (LReLU) are pre-trained using multi-channel signals. Secondly, the learned parameter knowledge of each individual source CNN is transferred to initialize the corresponding target CNN which is then fine-tuned by a few target training samples. Finally, a new decision fusion strategy is designed to flexibly fuse each individual target CNN to obtain the comprehensive result. The proposed method is used to analyze multi-channel signals measured from rotating machinery. The comparison result shows the superiorities of the proposed method over the existing deep transfer learning methods. (C) 2020 Elsevier B.V. All rights reserved.
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