Fault Diagnosis Method of Vehicle Power System Using Bayesian Network

Abstract

The fault diagnosis of vehicle power system that the structure and characteristics of components are complex, each module and internal modules exist coupling, cross-linked mutual relations and the uncertainties, the system status and working conditions are difficult to describe by precisely mathematical model, and test cost expensive, less fault samples. Thus its fault diagnosis is the decision problem of uncertain information in a small sample. it is proposed that combining multi-signal flow graph model with Bayesian network fault diagnosis method. The fault diagnosis model of power system and the corresponding Bayesian network structure are built, which achieve the fault diagnosis of power system, Diagnosis example shows that the method of the vehicle power has a higher failure troubleshooting capabilities of the system single and multiple faults.

Distributed Architecture of Power Grid Asset Management and Future Research Directions

“…Moreover, the operator analyzes and judges the fault, and evaluates the action correctness of the protection and circuit breaker according to the fault symptom information. The methods to research the power system fault diagnosis mainly include expert system (ES) [8][9][10], artificial neural network (ANN) [4,11], cluster analysis (CA) [12], Bayesian network (BN) [13,14], Petri network (PN) [15][16][17], information fusion and so on.…”

Fault Diagnosis of Lithium Battery based on Fuzzy Bayesian Network

“…A Bayesian network can realize an integrated probability description of system diagnosis with incomplete data sets. The complexity and uncertainty of lithium battery fault can be solved by combining these two methods [7][8].…”