The Oil parameter fault diagnosis for automobile engine based on ANFIS

This paper builds the fault diagnosis model and optimizes the input interface of the model by normalizing the initial data of the Oil parameter for the automobile engine, carrying on information fusion and adopting the Adaptive Neural Fuzzy Interference System (ANFIS). The recognition rate of the model reaches 90.26% under the test of field test data. The experiment indicates that the model enjoys reliability, strong generalization ability, and high failure recognition rate. Moreover, it can effectively detect the oil parameter failure for the automobile engine.