Principal-feature-guided degradation trend prediction algorithm based on gear fault dynamics model

Abstract

The long-term remaining useful life (RUL) prediction of gears is one of the key technologies for gear failure prediction and health management, which has significant practical significance for maintenance decision-making in the traditional mechanical industry. However, due to the lack of sufficient failure data, most RUL prediction methods face significant challenges when predicting gear degradation trends under different fault conditions. The purpose of this article is to solve the dependency of predictive models on data. The paper proposes a principal-feature-guided degradation trend prediction algorithm based on the gear fault dynamics model. Based on the degradation mechanisms under different failure modes, this paper establishes a high-fidelity faulty gears dynamic model. The accuracy of the dynamic model for two failure modes has reached 90% and 88%. The model can provide adequate failure data for RUL faulty gear prediction. Furthermore, a principal-feature-guided Transformer-TCN-ATT (TTA) algorithm was proposed to achieve trend prediction of faulty gears. The prediction algorithm introduces a principal feature reinforcement to reduce the impact of high-frequency fluctuations on feature extraction. Compared with the various typical methods, the proposed algorithm improves prediction accuracy by 76% and 77% for two failure modes.