A new approach to fault pattern classification of gasoline engine vibration
This paper presents a new approach to fault pattern classification of gasoline engine vibration based on statistics analysis, rough set and the support vector machines. First, different time domain statistical features are extracted from the resultant subband signals which derived from multiscale analysis of the raw vibration data, to acquire more fault characteristic information. Second, a rough set model is utilized to select the most superior features from the initial feature set. Finally, the selected superior features are input into the support vector machines classifier to accomplish faulty pattern classification. The experimental result show that the proposed method can extract the faulty features with better classification ability and at the same time reduce lots of features in case of assuring the classification accuracy, accordingly a better performance of fault diagnosis is obtained.
fault pattern classfication statistics analysis rough set support vector machines
Ning Li Rui Zhou
School of Mechanical and Electrical Engineering Shanghai Second Polytechnic University Shanghai, P. Department of Mechatronics System Engineering China Ship Development and Design Center Shanghai, P.
国际会议
上海
英文
1943-1947
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）