A Fault Diagnosis Method of Engine Rotor Based on Random Forests
Qi Yao; Jian Wang; Lu Yang; Haixia Su; Guigang Zhang
2016
会议名称2016 IEEE International Conference on Prognostics and Health Management
会议录名称2016PHM
会议日期2016-6-20
会议地点加拿大渥太华
摘要Rotor is the main part of the engine, the vibration fault is very common in the process of running, it must be monitored, checked, excluded in a timely manner for improving the reliability of engine and aircraft safety. This paper mainly studies four kinds of rotor fault, including unbalance, misalignment, surge, bearing failure. The frequency spectrum of the vibration signal of a rotor system is an important basis for rotor fault diagnosis, using the spectrum of rotor to build decision tree analysis is an important method for rotor fault detection. As the single decision tree’s anti-interference ability is very poor, this paper presents an engine rotor fault diagnosis method based on Random Forests. Experimental results show that the accuracy of this diagnosis method is high, the failures can be diagnosed timely and effectively to keep the engine in normal operation. To evaluate the validity of Random Forests, a SVM classifier is trained for comparison. Compare with SVM, we obtain better classification in Random Forests algorithm. This result demonstrates that Random Forests algorithm is a valid method for engine rotor.
关键词Fault Diagnosis Engine Rotor Random Forests Svm
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11812
专题数字内容技术与服务研究中心_智能技术与系统工程
通讯作者Jian Wang
作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Qi Yao,Jian Wang,Lu Yang,et al. A Fault Diagnosis Method of Engine Rotor Based on Random Forests[C],2016.
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