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A transfer convolutional neural network for fault diagnosis based on ResNet-50

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Abstract

With the rapid development of smart manufacturing, data-driven fault diagnosis has attracted increasing attentions. As one of the most popular methods applied in fault diagnosis, deep learning (DL) has achieved remarkable results. However, due to the fact that the volume of labeled samples is small in fault diagnosis, the depths of DL models for fault diagnosis are shallow compared with convolutional neural network in other areas (including ImageNet), which limits their final prediction accuracies. In this research, a new TCNN(ResNet-50) with the depth of 51 convolutional layers is proposed for fault diagnosis. By combining with transfer learning, TCNN(ResNet-50) applies ResNet-50 trained on ImageNet as feature extractor for fault diagnosis. Firstly, a signal-to-image method is developed to convert time-domain fault signals to RGB images format as the input datatype of ResNet-50. Then, a new structure of TCNN(ResNet-50) is proposed. Finally, the proposed TCNN(ResNet-50) has been tested on three datasets, including bearing damage dataset provided by KAT datacenter, motor bearing dataset provided by Case Western Reserve University (CWRU) and self-priming centrifugal pump dataset. It achieved state-of-the-art results. The prediction accuracies of TCNN(ResNet-50) are as high as 98.95% ± 0.0074, 99.99% ± 0 and 99.20% ± 0, which demonstrates that TCNN(ResNet-50) outperforms other DL models and traditional methods.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China (NSFC) under Grants 51825502, 51805192 and 51775216, Natural Science Foundation of Hubei Province Grant No. 2018CFA078, China Postdoctoral Science Foundation under Grant 2017M622414, and Supported by Program for HUST Academic Frontier Youth Team.

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  1. The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Long Wen, Xinyu Li & Liang Gao

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  1. Long Wen
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Correspondence to Liang Gao.

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Wen, L., Li, X. & Gao, L. A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Comput & Applic 32, 6111–6124 (2020). https://doi.org/10.1007/s00521-019-04097-w

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