Entropy-based Grey Correlation Fault Diagnosis Prediction Model

Abstract

In order to solve the fault diagnosis problem of automobile engine, the thesis puts forward an entropy-based grey correlation fault diagnosis prediction model. In light of the momentary of oil parameter for automobile engine, entropy-based data fusion can determine the weight of each factor in comprehensive evaluation. Then it makes forecast by grey correlation and evaluation of system oil. The result indicates that, the model is reliable, with strong generalization ability and higher failure recognition rate than that of the single models.