RESEARCH PAPER
Degradation generation and prediction based on machine learning methods: A comparative study
1
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China
2
School of Mathematical Sciences, University of Chinese Academy of Sciences, China
Submission date: 2024-04-27
Final revision date: 2024-06-24
Acceptance date: 2024-08-08
Online publication date: 2024-08-09
Publication date: 2024-08-09
Corresponding author
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(1):192168
HIGHLIGHTS
- The degradation trajectories are generated based on TimeGAN, SVAE, diffusion model, and the segmented sampling method, respectively.
- The diffusion model is used to synthesize the degradation process.
- Based on several generative models and predictive networks, evaluation and comparison of both numerical simulations and case studies are realized.
KEYWORDS
TOPICS
ABSTRACT
In engineering practice, degradation analysis often suffers from the small-sample problem. To this end, several generative models are developed to expand the degradation data, based on which both the original data and the synthetic data are used to train neural networks for degradation prediction. However, these methods are rarely compared with each other, and the performances of competitive candidates are not explored. Given this, this paper reviews some machine learning-based methods and performs a comparison among them. Particularly, a segmented sampling method is proposed and the diffusion model is introduced for degradation generation. Results of both numerical simulations and case studies show that none of these methods can perform best in all cases, yet making use of synthetic data improves the predictive performance. Overall, the time-series generative adversarial network and the segmented sampling method are recommended for degradation generation, and the gated recurrent unit network is recommended for prediction.
ACKNOWLEDGEMENTS
We would like to thank the reviewers for their insightful comments and suggestions, which have significantly improved the quality of this paper.
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CITATIONS (1):
1.
Intelligent Systems in Production Engineering and Maintenance IV
Ziółkowski Jarosław, Grzybowski Bartłomiej
Ziółkowski Jarosław, Grzybowski Bartłomiej
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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