Degradation generation and prediction based on machine learning methods: A comparative study

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.