Physics-informed few-shot data augmentation technology for industrial predictive maintenance
TL;DR
This approach provides robust, physically-grounded data to effectively support the training of deep learning-based Remaining Useful Life (RUL) prediction models under few-shot constraints.
Abstract
To address the critical scarcity of full life-cycle failure data for ball screws in industrial applications, this paper proposes the Hybrid Physics-Informed Data Augmentation (HPIDA) framework. This framework integrates an autoregressive (AR) system identification model with Archard’s wear law and Hertzian contact mechanics, constructing a comprehensive physical mapping chain from microscopic wear to macroscopic vibration signals. Furthermore, a domain randomization strategy is employed to synthesize high-fidelity, virtual full-life trajectories that encompass diverse degradation rates. Validation on the PredMAIN dataset demonstrates that the Wasserstein-1 distance for all generated signals strictly remains below 0.08, while both the power spectrum cosine similarity and the autocorrelation Pearson coefficient consistently exceed 0.9. The synthesized data exhibit a high degree of concordance with authentic signals across three dimensions: statistical distribution, frequency-domain structure, and temporal characteristics. Consequently, this approach provides robust, physically-grounded data to effectively support the training of deep learning-based Remaining Useful Life (RUL) prediction models under few-shot constraints.