A Multisource State Space-Based Tool Remaining Useful Life Prediction Method Considering Multistage Degradation Characteristics

Stochastic process models have been widely used in the field of remaining useful life (RUL) prediction due to their notable generality and high interpretability. Traditional methods typically employ a single degradation function to describe the degradation of an equipment. However, the degradation process of a tool exhibits multistage characteristics. A single degradation model ignores this characteristic and is challenging to accurately track the tool wear process. To address this issue, this article proposes a multisource state space-based tool RUL prediction method considering multistage degradation characteristics. First, the mapping between multisource monitoring signals and tool degradation states is established based on the multisource state space model. Then, a multistage degradation model based on the Wiener process is established to describe the tool wear degradation. The method fully integrates the historical wear information and the real-time multisource monitoring signals during tool machining to achieve the online RUL prediction. Through a detailed numerical simulation analysis and a tool wear experiment, the proposed method is verified with better comprehensive performance. The average cumulative relative accuracy (CRA) of the prediction results is 0.789, which outperforms the commonly used tool RUL prediction method.