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.
Oil parameter ANFIS fault diagnosis fuzzy model
Li-fang Kong Hong Zhang Li-fang Kong Wei Zhang
School of Information and Electrical Engineering China University of Mining and Technology Jiangsu X Basic Departments Xuzhou Air Force College Jiangsu Xuzhou 221002. China
国际会议
长春
英文
550-553
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）