RESEARCH PAPER
Remaining useful life prediction with insufficient degradation data based on deep learning approach
| 1 | University of Electronic Science and Technology of China Zhongshan Institute, School of Computer, Zhongshan, China, 528400 |
| 2 | Guangdong University of Technology, Guangzhou, School of Automation, China, 510006 |
| 3 | University of Electronic Science and Technology of China, School of Computer Science and Engineering, Chengdu 611731 |
Publication date: 2021-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(4):745–756
HIGHLIGHTS
- Focus on the improvement of the RUL prediction effect in the case of insufficient degradation data.
- A data amplification network based on cycleGAN is designed to effectively increase the size of the degradation dataset.
- A RUL prediction framework is constructed with the sliding time window strategy and BiLSTM network.
- Experimental results show the RUL prediction performance has been significantly improved by the proposed data amplification approach.
KEYWORDS
deep learningremaining useful lifedegradation datadata amplificationcycle-consistentgenerative adversarial network
ABSTRACT
Remaining useful life (RUL) prediction plays a crucial role in decision-making in conditionbased maintenance for preventing catastrophic field failure. For degradation-failed products, the data of performance deterioration process are the key for lifetime estimation. Deep learning has been proved to have excellent performance in RUL prediction given that the degradation data are sufficiently large. However, in some applications, the degradation data are insufficient, under which how to improve the prediction accuracy is yet a challenging problem. To
tackle such a challenge, we propose a novel deep learning-based RUL prediction framework by amplifying the degradation dataset. Specifically, we leverage the cycle-consistent generative adversarial network to generate the synthetic data, based on which the original degradation dataset is amplified so that the data characteristics hidden in the sample space could be captured. Moreover, the sliding time window strategy and deep bidirectional long short-term memory network are employed to complete the RUL prediction framework. We show the effectiveness of the proposed method by running it on the turbine engine data set from the National Aeronautics and Space Administration. The comparative experiments show that our method outperforms a case without the use of the synthetically generated data.
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