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Vehicle fault intelligent reasoning method and system based on Bayesian network

A technology of Bayesian network and vehicle faults, which is applied in the field of intelligent reasoning of vehicle faults based on Bayesian networks, and can solve problems that affect the accuracy of troubleshooting results, difficult learning, and inability to deal with uncertainties, etc.

Active Publication Date: 2021-05-18
广州瑞修得信息科技有限公司
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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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  • Vehicle fault intelligent reasoning method and system based on Bayesian network
  • Vehicle fault intelligent reasoning method and system based on Bayesian network
  • Vehicle fault intelligent reasoning method and system based on Bayesian network

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Embodiment Construction

[0068] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0069] It should be understood that the step numbers used in the text are only for the convenience of description, and are not intended to limit the order in which the steps are performed.

[0070] It should be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in this specification and the appended claim...

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Abstract

The invention discloses a vehicle fault intelligent reasoning method based on Bayesian network, which includes constructing a fault tree model based on Bayesian network, obtaining the prior probability of fault tree nodes and a detection method for associated fault tree nodes; Input the model, prior probability and detection method to the inference engine to generate the optimal detection method; receive the detection result of the manual execution of the optimal detection method and input it to the inference engine to obtain the posterior probability of the fault tree node; determine whether there is a The posterior probability of the fault tree node reaches the preset locking probability; if so, check the fault cause corresponding to the current fault tree node, push the maintenance procedure, and update the prior probability of the fault tree node according to the current maintenance data; if not, then Carry out a new round of detection methods. The method provided by the invention reduces the requirement of the fault tree on the accuracy of manual experience, improves the accuracy of the reasoning system, shortens the troubleshooting steps of the fault cause, and improves the troubleshooting efficiency.

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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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/00G06K9/62G06N5/04
CPCG06Q10/20G06N5/04G06F18/23G06F18/24155G06F18/24323
Inventor 李留海许铁强桑叶漫谢玉琰李含
Owner 广州瑞修得信息科技有限公司
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