An intelligent pipeline fault diagnosis system

Abstract

A method of pipeline fault diagnosis is proposed in this paper to solve the problem of fault diagnosis with insufficient historical fault data. Firstly, the corresponding relationship between the sensing data and fault category is studied to determine the SVM fault classification model; then, based on the fault diagnosis training data set, the most matching Bayesian network structure is formed and the parameters are determined according to the fault category and fault causes. In the fault diagnosis application, the fault is classified by the trained SVM fault classification model based on the real-time sensing data of each node in the pipeline; Then, according to the classification results, the Bayesian network structure and parameters are used for reasoning to determine the failure probability of each node, and the node with the highest failure probability and the failure category are taken as the fault diagnosis results.