Fault Diagnosis of Linear Motion Guide System in PMSLM via STFC-RN Implemented on Edge Computing Node

A state monitoring and fault diagnosis method is proposed to achieve real-time state monitoring of the linear guide and balls of a permanent magnet synchronous linear motor (PMSLM) and ensure the reliable operation of the equipment. A PMSLM linear guide fault diagnosis platform is constructed to collect fault signals of the linear guide and balls. A novel deep learning framework, namely, short-time Fourier convolution residual network (STFC-RN), is put forward, which integrates an STFC layer in the first layer of the model to extract the time-frequency features of vibration signals. The main structure of STFC-RN is the residual structure containing backbone and branch. The backbone is mainly composed of residual blocks stacked to learn features extracted by the STFC layer. The branch consists of the depth-wise separable (DS) layer and the squeeze-and-excitation (SE) attention mechanism, realizing the reuse of primary features and reducing the computational burden. The model achieved a fault signal recognition rate of 97.3% at a speed of 600 mm/s, outperforming other models such as CNN, ResNet, DenseNet, and visual geometry group (VGG). Finally, the model is deployed to an edge computing device for real-time condition monitoring of linear guide system.