A novel spike detection model for dynamic stress monitoring of bogie frame

Abstract

The fatigue evaluation of the bogie frame is an important part of the structural health monitoring of the vehicle. During the dynamic stress monitoring, some signal spikes, which are much larger than the normal fluctuation range due to the interference of the complex electromagnetic environment, affect the accuracy of the structural damage assessment and need to be accurately detected and replaced. Aiming at the drawbacks of traditional detection methods that are overly dependent on engineering experience and not universal, a novel spike detection model is proposed in this paper. By the process of data transformation, spike region features are effectively separated. Based on the isolation forest algorithm, the normalized anomaly score of each point is calculated, and the threshold is determined adaptively. The spike detection rate and damage sensitivity are proposed as the evaluation indices of the detection effect of the method. The results show that the spike detection rate is improved by 7.86% on average, and the damage sensitivity is improved by 15.59% on average. The spike detection model in this paper is significantly improved compared to the existing methods.