Tool vibration signal processing solution adapted to practical machining environments

Vibration signals serve as an important means for tool condition monitoring. However, the vibration signals collected during actual machining are often more complex and differ significantly from the open source signal that previous studies have relied on. Therefore, in order to effectively apply the existing high-precision and high-robustness tool health management methods to the production environment, it is necessary to preprocess the actual signal according to its characteristics. In this paper, a highly automated signal preprocessing method is designed to extract effective information of tool condition change from the complex actual signals. The method eliminates the need for human expertise. It can remove irrelevant signals, accurately locate the segments corresponding to the target step, and provide essential support for subsequent feature extraction and model training. The experimental results show that the proposed method not only reduces the signal complexity by 25%, but also effectively locates the signal segment that can extract strong temporal correlation features, and performs well in processing efficiency.