Tool Fault Diagnosis Method Based on Multiscale-efficient Channel Attention Network

Currently, the production process is moving towards intelligence, the equipment fault diagnosis and predictive maintenance play a vital role in ensuring the production efficiency and reducing the production cost. As the milling tool of CNC machine tool, the health state of the machine tool directly affects the processing efficiency and product quality. Precise monitoring of tool wear condition is helpful to avoid product quality problems caused by tool failure. Therefore, A tool fault diagnosis method based on deep learning, which applies the efficient channel attention to the multiscale convolutional neural network. The tool fault diagnosis method based on multiscale-efficient channel attention network (MS-ECA Net) is proposed. MS-ECA Net uses channel feature learning to adaptively fuse the vibration signals of the machine tool spindle in different directions, so as to improve the accuracy of tool wear state diagnosis. In addition, in order to verify the performance of the proposed algorithm in the actual production scenario, A tool wear test platform to collect the data in line with the actual production, and uses the data to verify the performance of the proposed algorithm. Experiment results show that the proposed method improves the accuracy of tool fault diagnosis by 4.47%. © 2024 Chinese Mechanical Engineering Society. All rights reserved.