Prediction method of tool remaining useful life based on u-shapelets clustering

Considering that the performance degradation mode of different tools shows various trends, a single fixed global model is difficult to accurately predict the remaining life of tools with different performance degradation modc.Thus, a tool remaining useful life prediction method based on u-shapclcts clustering method and Long Short Term Memory (LSTM.) neural network model was proposed. The u-shapclcts set was extracted from the tool processing monitoring signals, and the distance between each u-shapclct and the processing monitoring signals was calculated to get the distance matrix. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method was used to cluster the distance matrix to obtain the cluster results of process monitoring signals. Different LSTM neural network models were trained to predict the remaining useful life of tools according to different clusters. The validity of the proposed method was verified by processing monitoring signals of wheel groove milling cutter processing, and compared with K-means clustering method, spectral clustering method, hierarchical clustering and DBSCAN clustering method. © 2024 CIMS. All rights reserved.