Online diagnosis method for linear control system network

Abstract

The invention relates to an online diagnosis method for a linear control system network. The method comprises the steps that first, a model including two adaptive neural fuzzy inference systems is established, linear control system network state parameters obtained in a fault environment are used as sample data input, a mixed learning algorithm is used for conducting offline training on the two adaptive neural fuzzy inference systems, fuzzy control rules are extracted respectively, then the fuzzy control rules are converted into a loop-up table, a network health degree calculation function and a network fault reason judging function are generated and are added into a controller node of a linear control system, an linear control system online diagnosis algorithm based on the adaptive neural fuzzy inference systems is formed, and online diagnosis is conducted on the network health degree and the fault reason of the linear control system.

Description

technical field [0001] The invention relates to an online diagnosis method for a line control system network based on an adaptive neuro-fuzzy reasoning system. Background technique [0002] With the continuous development of modern electronic technology, automotive electronics is developing in the direction of intelligent control, and uses the bus to connect various automotive electronic control modules into a vehicle network, which does not need traditional mechanical mechanisms to transmit control signals, but uses electronic The means to drive the car, this electronic means is the wire control technology. The continuous application of wire control technology brings convenience and intelligence to the internal communication of automobiles, but also brings new challenges to the development of automotive electronics. As it is directly related to the safety of the car, it has extremely high requirements for the reliability of each component it contains, especially including ...

AI Technical Summary

Problems solved by technology

This may lead to a situation that even if there are errors on the bus, as long as these errors do not affect normal communication, the number of errors is within an acceptable range, and the traditional online diagnosis system cannot find these hidden faults. Gradually accumulating, it is likely to cause the system to fail to communicate normally

[0004] Aiming at the problems that cannot be solved by the traditional online diagnosis system, scholars at home and abroad conduct research from multiple perspectives such as data fusion, network management and diagnosis protocol, diagnosis algorithm, etc. , master thesis, Hefei University of Technology) designed the CAN-FlexRay gateway network management strategy, which can diagnose node timeout and message timeout, etc. However, this diagnosis strategy only stays at the monitoring level and does not further process the fault data. Ability to analyze failure causes

In the document "Diagnostic Fusion for Time-Triggered Automotive Networks" (Utsav Drolia et al., Carnegie Mellon University, 2012IEEE 14th International Symposium on High-Assurance Systems Engineering), a neural network-based diagnostic method for the FlexRay bus system is designed, which can correct the errors of the FlexRay bus However, the neural network algorithm designed by this method can only obtain the cause of the network failure, and cannot give a qualitative judgment on the current network operation status, such as the current network has potential dangers, or the current network has errors, but it is still possible. operation, etc., so as to provide a basis for the fault handling module

At present, there is no online diagnosis method that can simultaneously and efficiently give the network operation status and the cause of the network failure based on the historical data and the currently monitored data.

Images