A novel gear RUL prediction method by diffusion model generation health index and attention guided multi-hierarchy LSTM.

Gears, as indispensable components of machinery, demand accurate prediction of their Remaining Useful Life (RUL). To enhance the utilization of ordered information within time series data and elevate RUL prediction precision, this study introduces the attention-guided multi-hierarchy LSTM (AGMLSTM). This innovative approach leverages attention mechanisms to capture the intricate interplay between high and low hierarchical features of the input data, marking the first application of such a technique in gear RUL prediction. Additionally, a refined health indicator (HI) is introduced, constructed through a diffusion model, to precisely reflect the gears' health condition. The proposed RUL prediction method unfolds as follows: firstly, HIs are computed from gear vibration data. Subsequently, leveraging the known HIs, AGMLSTM predicts future HIs, and the RUL of the gear is determined upon surpassing the failure threshold. Quantitative analysis of experimental results conclusively demonstrates the superiority of the proposed RUL prediction method over existing approaches for gear RUL estimation.