Wu, J.-Y., Wu, M., Chen, Z., Li, X.-L., & Yan, R. (2021). Degradation-Aware Remaining Useful Life Prediction With LSTM Autoencoder. IEEE Transactions on Instrumentation and Measurement, 70, 1–10. https://doi.org/10.1109/tim.2021.3055788
Abstract:
Remaining Useful Life (RUL) prediction plays a pivotal role in the predictive maintenance of industrial manufacturing systems. However, one major problem with the existing RUL estimation algorithms is the assumption of single health degradation trend for different machine health stages. To improve the RUL prediction accuracy with various degradation trends, this paper proposes an algorithm dubbed Degradation-awarE LSTM (Long Short Term Memory) Autoencoder (DELTA). First, the Hibert transform is adopted to evaluate the degradation stage and factor with real-time sensory signal. Second, we adopt LSTM autoencoder to predict RUL based on multi-sensor time-series data and the degradation factor. Distinct from the existing studies, the proposed framework is able to dynamically model the degradation factor and explore latent variables to improve RUL prediction accuracy. The performance of DELTA is evaluated with the open-source FEMTO bearing data-set. Compared with the existing algorithms, DELTA achieves appreciable improvements in RUL prediction accuracy.
License type:
Publisher Copyright
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)