Wu, J.-Y., Wu, M., Chen, Z., Li, X., & Yan, R. (2021). A joint classification-regression method for multi-stage remaining useful life prediction. Journal of Manufacturing Systems, 58, 109–119. https://doi.org/10.1016/j.jmsy.2020.11.016
Abstract:
Remaining Useful Life (RUL) prediction plays an important role in increasing the availability and productivity of industrial manufacturing systems. This paper proposes a joint classification-regression scheme for multi-stage RUL prediction. First, the time domain and frequency domain features are extracted from various types of raw sensory data (e.g., acoustic, current, vibration and temperature) to constitute the training data set. Second, the system health stage is classified based on the trained model and real-time sensory data. Third, we perform stage-level RUL prediction with regression algorithm to estimate overall useful life. Distinct from the existing RUL estimation algorithms, the proposed Multi-Stage Remaining Useful Life (MS-RUL) prediction effectively integrates the machine/deep learning based classification and regression to improve overall estimation accuracy. We conduct the performance evaluation with sensory data from real manufacturing systems. Experimental results demonstrate that the proposed MS-RUL achieves approximately 6:5% accuracy improvements over the state-of-the-art algorithms in the RUL prediction.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the A*STAR - RIE2020 IAF-PP
Grant Reference no. : A1788a0023
Partially supported by National Natural Science Foundation of China (No. 51835009)