Fault Diagnosis of Engine Based on Supervision of Data-Driven
Several kinds of information generated during the operation process of the engine, generally speaking, there is no one-to-one correspondence between the characteristic parameters and status, whereas, there are often existing many types of faults. After analyze the problem of uncertainty and other issues which the fault diagnosis of engine is faced, in this paper, a new approach for fault diagnosis of engine based on supervision of data-driven is proposed. This algorithm begin with the given classification data, using the representative points on behalf of class mean values, using the weighted distances in place of Euclidean distances. Then employing the method to identify 8 kinds of common fault states for engine, the experiment results shows that the method based on optimal representative points clustering is an effective way to diagnosis the fault for engine.
supervision of data-driven generators fault diagnosis optimal representative point
Feng Li Zheng Mu Wei Liao
Hebei University of Engineering, Handan, China, 056038
国际会议
长沙
英文
1469-1472
2009-10-10（万方平台首次上网日期，不代表论文的发表时间）