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IEEE/CAA Journal of Automatica Sinica

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X. Li, Y. X. Xu, N. P. Li, B. Yang, and Y. G. Lei, “Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 1–14, Jan. 2023. doi: 10.1109/JAS.2022.105935
Citation: X. Li, Y. X. Xu, N. P. Li, B. Yang, and Y. G. Lei, “Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 1–14, Jan. 2023. doi: 10.1109/JAS.2022.105935

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

doi: 10.1109/JAS.2022.105935
Funds:  This work was supported by the National Science Fund for Distinguished Young Scholars of China (52025056), and Fundamental Research Funds for the Central Universities (xzy012022062)
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  • In recent years, intelligent data-driven prognostic methods have been successfully developed, and good machinery health assessment performance has been achieved through explorations of data from multiple sensors. However, existing data-fusion prognostic approaches generally rely on the data availability of all sensors, and are vulnerable to potential sensor malfunctions, which are likely to occur in real industries especially for machines in harsh operating environments. In this paper, a deep learning-based remaining useful life (RUL) prediction method is proposed to address the sensor malfunction problem. A global feature extraction scheme is adopted to fully exploit information of different sensors. Adversarial learning is further introduced to extract generalized sensor-invariant features. Through explorations of both global and shared features, promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions. The experimental results suggest the proposed approach is well suited for real industrial applications.

     

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