A case based defect diagnosis model is proposed in this paper. A normalized hypercube mapping method is proposed according to the distribution characteristics of oil chromatogram data. Oil chromatogram data in the hypercube space domain can be applied for diagnosing directly. Meanwhile, the case similarity degree method and the judging method of diagnosis result based on weighting election are put forward, and the selection and optimization of parameters in the model are confirmed by simulation experiments. The model shows high correct rate in the cross validation of the case database. The average accuracy rate was 88.53%, higher than the existing BP neural network and support vector machine technology. It can diagnose the defects of the oil-immersed equipment. It is verified that the model proposed in this paper has significant practical application value in engineering.