Research on deep learning has been increasing in recent years in the field of fault diagnosis in rotary machine. However, compared to training data, real world data is collected from different system conditions and environments. Therefore, real world data has different data
distribution and various noise with the training data, leading to domain shift between data. Due to the problem mentioned above, deep learning often fails to apply on industrial data.
Domain generalization is an emerging deep learning technique to generalize domain discrepancy. In this study, domain adversarial neural network (DANN)-based domain
generalization is proposed for multi-label fault diagnosis of rotary machine. Frequency domain image data were generated via implementing short time fourier transform
(STFT) to the sensor data collected from the test rig. Then, the features are utilized as training data to diagnosis multi-label fault via DANN-based domain generalization. Moreover, the upper boundary of rotating speed domain of the rotary machine where domain generalization can effectively diagnosis multi-label fault is suggested.
Domain Generalization, Multi-Label Fault Diagnosis, Domain Adversarial Neural Network
Chongchong, Y., Yaqian, N., Yong, Q., Weijun, S., & Xia, Z. (2021) Multi-label fault diagnosis of rolling bearing based on meta-learning. Neural Computing and Applications, vol. 33, pp. 5393-5407. doi:10.1007/s00521-020-05345-0
Yaroslav, G., Evgeniya, U., Hana, A., Pascal, G., Hugo, L., Francois, L., Mario, M., & Victor, L. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, vol. 17, pp. 2096-2030.
Xiang, L., Wei, Z., Hui, M., Zhong, L., & Xu, L. (2020). Domain generalization in rotating machinery fault diagnostics using deep neural networks. Neurocomputing, vol. 403, pp. 409-420. doi:10.1016/j.neucom.2020.05.014
Yixiao, L., Ruyi, H., Jipu, L., Zhuyun, C., & Weihua, L. (2020). Deep semisupervised domain generalization network for rotary machinery fault diagnosis under variable speed. IEEE Transactions on Instrumentation and Measurement, vol. 69, pp. 8064-8075. doi:10.1109/TIM.2020.2992829
This work is licensed under a Creative Commons Attribution 3.0 Unported License.