A New Fault Diagnosis Model for Circuits in Railway Vehicle Based on the Principal Component Analysis and the Belief Rule Base

Hao Wu,Zhi Gao,Siyu Chen,Bangcheng Zhang,Qianying Bu,Chuan Li

doi:10.1155/2021/2516118

Abstract

Circuits are considered an important part of railway vehicles, and circuit fault diagnosis in the railway vehicle is also a research hotspot. In view of the nonlinearity and diversity of track circuit components, as well as the diversity and similarity of fault phenomena, in this paper, a new fault diagnosis model for circuits based on the principal component analysis (PCA) and the belief rule base (BRB) is proposed, which overcomes the shortcomings of the circuit fault diagnosis method based on data, model, and knowledge. In the proposed model, to simplify the model and improve the accuracy, PCA is used to reduce the dimension of the key fault features, and varimax rotation is used to deduce the fault features. BRB is used to combine qualitative knowledge and quantitative data effectively, and evidential reasoning (ER) algorithm is used to carry out the inference of knowledge. The initial parameters of the model are optimized, and the optimal precondition attributes, rule weights, and belief degree parameters are obtained to improve the accuracy. Through the training and testing of the model, the experimental results show that the method can accurately diagnose the fault of the driver controller potentiometer in the railway vehicle. Compared with other methods, the model shows high accuracy.

Highlights

Circuits are considered an important part of railway vehicles, and circuit fault diagnosis in the railway vehicle is a research hotspot
In view of the nonlinearity and diversity of track circuit components, as well as the diversity and similarity of fault phenomena, in this paper, a new fault diagnosis model for circuits based on the principal component analysis (PCA) and the belief rule base (BRB) is proposed, which overcomes the shortcomings of the circuit fault diagnosis method based on data, model, and knowledge
They cannot add expert knowledge and output the fault samples corresponding to the fault category label, which belong to the black box and cannot output other diagnostic information, such as the probability of the fault sample belonging to each fault category. ere are mainly backpropagation neural network (BPNN) [3], support vector machine (SVM) [4], and DS evidence theory fusion method [5]

Summary

Fault Diagnosis Theory Based on PCA-BRB

It is especially important to effectively reduce the number of features and find smaller dimensions and more representative features without changing the qualitative knowledge contained in the data itself and to simplify the fault diagnosis model at the same time. Principal component analysis (PCA) is an effective method for statistical analysis of data, which is based on the Karhunen–Loeve decomposition. Erefore, a method of combining principal component analysis with varimax rotation is proposed to reduce the dimension of fault feature. PCA is used to extract the principal component, and the principal component is used to carry out the reverse reasoning [14], using the method of maximum variance rotation in the factor analysis method to solve the load matrix. The PCA transforms P vectors (x1, x2, x3, . . . xp) to n vectors (y􏽰1, y 2, y3, . . . yn). where E represent the load matrix, E dig λnenp

Orthogonal Rotation of Load Matrix

PCA-BRB Fault Diagnosis Model for Circuits in Railway Vehicle

A Output

Method

Findings

Conclusion