Intelligent vehicle fault reasoning method and system based on Bayesian network

Abstract

The invention discloses an intelligent vehicle fault reasoning method based on a Bayesian network, and the method comprises the steps: building a fault tree model based on the Bayesian network, and obtaining the prior probability of a fault tree node and a detection method of an associated fault tree node; inputting the fault tree model, the prior probability and the detection method into an inference engine to generate an optimal detection method; receiving a detection result of manually executing the optimal detection method, and inputting the detection result into an inference engine to obtain a posterior probability of the fault tree node; judging whether the posterior probability of a certain fault tree node reaches a preset locking probability or not; if yes, verifying a fault reasoncorresponding to the current fault tree node, pushing a maintenance procedure, and updating the prior probability of the fault tree node according to the current maintenance data; and if not, carrying out a new round of detection method. According to the method provided by the invention, the requirement of the fault tree on the accuracy of artificial experience is reduced, the accuracy of an inference system is improved, the troubleshooting step of fault causes is shortened, and the troubleshooting efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of vehicle intelligent diagnosis, in particular to a Bayesian network-based intelligent vehicle fault reasoning method and system. Background technique [0002] As the application of electronic control technology in automobiles becomes more and more mature, there are more and more electronic control units, electronic control devices, sensors, wiring harnesses and other components on commercial vehicles, and the electronic control system is becoming more and more complex. The maintenance of the control system also brings new challenges to the service station. In order to reduce the maintenance threshold of the electronic control system and improve the maintenance efficiency, the existing technology usually combines artificial intelligence algorithms. For example, the Bayesian network algorithm is introduced into the commercial vehicle fault maintenance process. Diagnostic instruments, TBOX and other equipmen...

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Problems solved by technology

However, this method often has the following disadvantages: first, the order of screening in the decision tree is fixed, and the order of screening cannot be automatically adjusted according to each reasoning result. Logical judgment can only troubleshoot the cause of the fault in the node, and cannot deal with uncertain situations, which will affect the accuracy of the troubleshooting results; third, because the decision tree has strong logic, self-learning is difficult and time-consuming. Not strong implementation

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