Research on anti-interference and training set updating ability of fault diagnosis model of high voltage common rail system

Abstract

In the fault diagnosis of high-pressure common rail diesel engine, the problem of low accuracy of fault diagnosis is caused by the inability to classify untrained state problems and training sets. To solve the above problems, an anti-interference classification model for fault diagnosis of high-voltage common rail system based on alpha shapes algorithm and K-means algorithm is proposed in this paper. This model can improve the anti-interference ability and training set updating ability of the fault diagnosis model. Using the anti-interference classification model composed of anti-jamming device and classifier, the samples for fault diagnosis are screened in advance, and the singular values of untrained state and trained state are classified. By constructing an anti-interference classification model composed of anti-interference device and classifier, the fault is diagnosed, and then the dependency of singular values is obtained through cluster analysis, which improve the anti-interference ability and training set updating ability of the fault diagnosis model. By comparing the diagnosis results of the fault diagnosis model with and without anti-interference classifier, we can found that the addition of anti-interference classifier makes the fault diagnosis model of high-voltage common rail system obtain the anti-interference ability to the untrained state and the ability to update the training set of the singular value of the trained state, and can slightly improve the accuracy of fault diagnosis.