MSRCN: A cross-machine diagnosis method for the CNC spindle motors with compound faults

Abstract

The cross-machine diagnosis of CNC spindle motors with compound faults is essential and challenging because of the subsystem coupling and individual difference. This paper proposed an in-situ fault diagnosis method for cross machine-level individual diagnosis. Plug-and-play modules are specifically designed inspired by signal processing theory, and are embedded into mainstream CNN-based models as an effective industrial diagnostic model, the multiscale spatial–temporal residual capsule neural networks (MSRCN). The internal mechanism of these new modules is explored through ablation experiments and visualization on real industrial motor signals, which shows MSRCN-based models can enrich the multi-scale feature extraction capabilities and benefits the interference resistance of individual related features. In addition, new evaluation operators for degree of confidence are proposed to comprehensively evaluate the performance of deep learning in classification tasks and the reliability of the decision-making.