Research on tool fault self-sensing system based on deep residual shrinkage network
With the application of next-generation artificial intelligence technologies,CNC machine tools are encountering new development opportunities,moving towards high precision,high efficiency,automation,and intelligence.Traditional machine learning faces challenges in feature extraction due to manual feature engineering requirements,limitations,the curse of dimensionality,insufficient handling of non-linear relationships,and low accuracy.To address these issues,proposes an improved Deep Residual Shrinkage Network(DRSN)fused with Multidimensional Cooperative Attention-alter(MCA-a),Spatial Recalibration Unit(SRU),and Channel Recalibration Unit(CRU),termed Deep Residual Shrinkage Improvement of Attention Mechanism(DRSN-IAM).This effectively tackles problems such as gradient explosion and vanishing gradients caused by deep networks in traditional neural networks.By optimizing the model,it reduces issues such as insufficient feature extraction capabilities due to the soft threshold function.Validation using the publicly available dataset,specifically the open data from the 2010 PHM High-Speed CNC Machine Tool Health Prediction Competition by the American Society of Mechanical Engineers(ASME),achieves a classification accuracy of 99.2%.Compared to classical neural networks,the proposed improved Deep Residual Shrinkage Network(DRSN-IAM)demonstrates superior classification performance.
NETL
NSTL
万方数据
