Fracture failure analysis of heavy-haul train couplers using convolutional neural network along with multilayer perceptron

Abstract Metal couplers are susceptible to unpredictable failure and fracture under long-term high-load conditions in heavy-haul railway transportation. The current mainstream manual inspection method has the disadvantages of high subjectivity and high a priori knowledge requirements, thus not meeting the rapid analysis requirements of production companies. Therefore, in this study, an automated failure analysis method is proposed for heavy-haul coupler fractures. First, a novel image segmentation method (PermuteNet) combining a visual multilayer perceptron and a convolutional neural network is designed to segment different failure patterns of fracture surfaces. The proposed method uses two newly proposed modules—permute attention module and context attention module—to improve the network’s ability to perceive weakly differentiated objects, thereby improving the recognition ability of the model for different failure patterns. In addition, a deep supervisory function is adopted to accelerate the convergence speed of the network. Finally, the proposed image segmentation method is deployed on a computer in conjunction with a developed client application to implement a single-click detection function for coupler fracture pattern analysis. Experiments are performed using the heavy-haul coupler fracture dataset established using on-site data; the proposed segmentation method achieves a mean intersection over union of 77.8%, which is considerably higher than that of other existing methods. By using the client software, the single-click detection function of the fracture area is realized. Thus, the proposed method provides a more convenient and accurate fracture identification solution for factory inspectors and has broad application prospects.