摘要
针对航空发动机转子轴心轨迹难以准确自动识别的问题,提出了基于二维形状不变矩和支持向量机(SVM)决策树的识别方法。对信号滤波降噪和倍频提纯,形成比较清晰的轴心轨迹;利用二维形状不变矩提取轴心轨迹的图形特征,得到不变矩特征向量,进而构造特征故障的训练和测试样本;采用SVM进行训练和学习,构造SVM决策树,识别故障类别,分类正确率达93.3%以上。应用实测弹支振动应力信号对该方法的准确性进行了验证,结果表明,该方法有效地解决了航空发动机转子轴心轨迹自动识别准确率低和小样本问题。
It was difficult to automatically and exactly identify shift orbits of aero-engine rotors, a recognition method was proposed based on two-dimensional feature invariant moments and SVM decision tree. First, the relatively clear shift orbits could be acquired after the noise reduction and frequency purification of the signals. The invariant moments were used to extract the graphic features of the shift orbits, then the invariant moment vectors were obtained, constructing the training and test samples of the characteristic faults. Finally, in order to construct SVM decision tree and identify faults, support vector machine was trained and studied. The accuracy rate of classification reaches 93.3%. The method was verified by measured signals of elastic support vibration stress. The results show that the method effectively resolve low recognition accuracy and small sample problems for shaft orbits of aero-engine rotors.
作者
何刘海
吴桂娇
王平
HE Liuhai;WU Guijiao;WANG Ping(AECC Hunan Aviation Powerplant Research Institute,AECC Key Laboratory of Aero-engine Vibration Technology, Zhuzhou, Hunan, 412002)
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2019年第8期969-974,共6页
China Mechanical Engineering
基金
中航工业技术创新基金资助项目(2012B60804R)
关键词
航空发动机
轴心轨迹
二维形状不变矩
支持向量机决策树
aero-engine
shift orbit
two-dimensional feature invariant moment
support vector machine(SVM) decision tree
