Fault diagnosis of bearing-rotor system based on infrared thermography: ReSPP with multi-scaled training method

Abstract

The fault diagnosis method of bearing-rotor system based on infrared thermography can reflect the global fault information of the equipment, which is an advanced non-contact monitoring measure. Current diagnosis methods focus on the analysis of single-scale input images, and the network only accepts fixed-size inputs. The model is not capable of fusing multi-scaled fault features while processing single-scaled images, and compression for the size constraint will cause geometric distortion. This paper proposed a novel fault diagnosis model ReSPP (Residual Network with Least Spatial Pyramid Pooling (LSPP)) based on the improved LSPP. LSPP solves the problem of fault feature distortion caused by fixed-size constraint of the network. By replacing the single-scaled training with the proposed multi-scaled training method applied to fault diagnosis, the weight parameters of ReSPP pool the deep fault features of the bearing-rotor system at multiple scales, retaining the critical fault semantic information. The proposed model solves the fault feature loss in the process of feature extraction by improving the subsampling residual block. Experimental results show that ReSPP with multi-scaled training method (ReSPP-MSTM) classifies the fault conditions of the bearing-rotor system with an average diagnostic accuracy of 99.18%.