An improved method of rail health monitoring based on CNN and multiple acoustic emission events

摘要

Rail health monitoring plays an important role in the railway system, and how to accurately obtain the rail state is very significant for the railway safety. This paper proposes an improved method of rail health monitoring based on convolutional neural network (CNN) and probability analysis of multiple acoustic emission (AE) events. By tensile testing machine, AE signals with safe and unsafe states are obtained. The CNN method of deep learning (DL) is employed to classify the defects, and the results of CNN are also compared with that of other methods. From the output of CNN, the probability values of each sample belonging to a class can be obtained, and then the improved classification method based on multiple events is investigated. The detection errors caused by one-time classification are eliminated, and the classification accuracy are improved. The results illustrate that the proposed method can effectively recognize the rail state for rail health monitoring.