A hybrid fault diagnosis scheme for milling tools using MWN-CBAM-PatchTST network with acoustic emission signals

Milling tools are critical to machining and manufacturing processes. Accurate diagnosis and identification of faults occurring in milling tools during their operation are of utmost importance for maintaining the reliability and availability of these tools, and to minimise machine downtime and overall machining costs. This paper presents a milling tools fault diagnosis network model based on acoustic emission signals. The model integrates a multilayer wavelet CNN (MWN) consisting of a discrete wavelet transform (DWT) and a convolutional neural network (CNN), a convolutional block attention module (CBAM), and a PatchTST module. MWN uses wavelet transformation to withdraw multi-scale features from signals, thus improving sensitivity to small variations in acoustic emission. CBAM improves feature representation by focusing on critical feature channels and regions, while PatchTST uses a self-attention mechanism to optimise processing of long-range feature dependencies. This synergy of mechanisms results in superior performance, outperforming traditional diagnostic methods. Bayesian optimisation is used to select model hyperparameters, eliminating the subjective bias associated with manual range setting. Validation experiments using the milling dataset, including ablation studies and comparative tests, demonstrated that the model achieves an identification accuracy of over 98%, validating its generalisation capability and effectiveness in diagnosing tool faults using acoustic emission signals.