Abstract
Deep learning models are widely used in fault diagnosis to learn hierarchical representations from collected signals. However, most of the models depend considerably on the assumption that training (source domain) and test (target domain) data sets are from the same feature distribution. This assumption is difficult to meet in practical scenarios of industrial applications because the working conditions of rotating machinery change with different machining tasks and labelled data with fault information are difficult and expensive to collect. Therefore, a novel fault diagnosis method, called multilayer adaptation convolutional neural network (MACNN), is constructed to solve the above-mentioned problems. The method regards raw temporal signals as input and uses wide kernels following a multiscale convolutional module to capture low-frequency features at multiple scales in shallow layers. Then, small convolutional kernels are used to implement multilayer nonlinear mapping in deep layers. Adaptive batch normalisation and multi-kernel maximum mean discrepancy are combined to reduce the feature distribution discrepancy in shallow and deep layers of the model, respectively, which improves the domain adaptation capability of the model. The proposed method is validated through 12 fault diagnosis experiments. The average 99.21% diagnosis precision demonstrates the reliability and stability of the method under different working loads.
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Acknowledgments
The authors would like to thank the editors, referees and all the workmates who dedicated their precious time to this research and provided insightful suggestions. All their help contributes greatly to this article.
Funding
This work was supported by the National Natural Science Foundation of China (Grant No. 51975249), Key Research and Development Plan of Jilin Province (20190302017GX), Industry Innovation Project of Jilin Province (2019C037-1), Fundamental Research Funds for the Central Universities, and JLUSTIRT.
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Tongtong Jin: Background research, methodology, data curation, software, validation writing—original draft, editing
Chuliang Yan: Review and editing, supervision
Chuanhai Chen: Review and editing, supervision, project administration, funding acquisition
Zhaojun Yang: Supervision, project administration, funding acquisition
Hailong Tian: Review and suggestion
Jinyan Guo: Review and editing
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Jin, T., Yan, C., Chen, C. et al. New domain adaptation method in shallow and deep layers of the CNN for bearing fault diagnosis under different working conditions. Int J Adv Manuf Technol 124, 3701–3712 (2023). https://doi.org/10.1007/s00170-021-07385-9
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DOI: https://doi.org/10.1007/s00170-021-07385-9