Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

摘要：

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

关键词： aerospace propulsion system fault diagnosis FLS-SVM artificial intelligence

作者: Quhongchun Dingxiebin

作者单位: School of Mechanical Engineering, Tianjin University, Tianjin,300070, P.R.China College of Aeronauti School of Mechanical Engineering, Tianjin University, Tianjin,300070, P.R.China

会议类型: 国际会议

会议名称: 2011 3nd International Conference on Mechanical and Electronics Engineering(2011年第三届机械与电子工程国际会议 ICMEE2011)

会议地点: 合肥

会议语种:英文

页码: 2047-2050

在线出版日期: 2011-09-23（万方平台首次上网日期，不代表论文的发表时间）