An intelligent fault diagnosis approach integrating cloud model and CBR

Abstract

The study is dedicated to an intelligent fault diagnosis approach for vehicle maintenance which integrates both the cloud model and case-based reasoning (CBR). The cloud model is used to transform the uncertainty of the subjective quantitative information into qualitative values to calculate the case similarity, which greatly simplifies the input conditions in case retrieval and improves the operability of fault diagnosis. The improved Euclidean distance formula is taken as a measure of the similarity between the fault cases. Compared with the traditional method, it eliminates the similarity deviation and improves the accuracy of case retrieval. The case study of vehicle electrical and electronic equipment is reported, which can prove the approach proposed in this paper is correct and efficient.