Abstract
Supervised deep learning methods have been widely applied in the field of machinery fault diagnosis in recent years, which learns quite well the mapping relationship between the monitoring data and the corresponding labels. In many practical industrial applications, however, the monitoring data are unlabeled due to the requirement of expert knowledge and a large amount of labor, which limits the use of supervised methods for fault diagnosis. In view of this problem, in this work a self-supervised representation learning method named contrastive predicting coding (CPC) is employed to automatically extract high-quality features from one-dimensional machinery monitoring signals without the requirement of labels. The method is validated on a benchmark dataset for bearing fault diagnosis, and the quality of extracted features of different classes is quantitatively evaluated. The results show that features extracted by the CPC method are more representative than those obtained by autoencoders and statistics.
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Acknowledgements
This research is partially supported by the National Natural Science Foundation of China (52005103, 71801046, 51775112, 51975121), the Guangdong Basic and Applied Basic Research Foundation (2020A1515110139, 2019B1515120095), the Chongqing Natural Science Foundation (cstc2019jcyj-zdxmX0013), and the Intelligent Manufacturing PHM Innovation Team Program (2018KCXTD029, TDYB2019010).
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Wei, Y., Cai, X., Long, J., Yang, Z., Li, C. (2021). Self-supervised Contrastive Representation Learning for Machinery Fault Diagnosis. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_25
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DOI: https://doi.org/10.1007/978-981-16-5188-5_25
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