基于多尺度-高效通道注意力网络的刀具故障诊断方法

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

目前,生产加工流程正向着智能化迈进,设备的故障诊断及预测性维护在保障企业生产效率,降低生产成本方面起着至关重要的作用。刀具作为数控机床的切削工具,其实时健康状态直接影响着机床的加工效率和产品质量。对刀具磨损状态的精准监测有助于避免因刀具失效导致的产品质量问题。基于此背景,研究一种基于深度学习的刀具故障诊断方法,将高效通道注意力应用到多尺度卷积神经网络中,提出基于多尺度-高效通道注意力网络的刀具故障诊断方法,利用通道特征学习将机床主轴不同方向的振动信号进行自适应的特征融合,从而提升刀具磨损状态诊断精度。此外,设计刀具磨损试验平台,用于采集符合实际生产的数据,在实际生产场景中验证所提算法的性能。试验结果表明,所提出方法较多尺度网络的刀具故障诊断准确率提高4.47%。

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%.