基于张量主成分分析的非线性双转子系统故障诊断方法

Method of fault diagnosis of nonlinear dual-rotor system based on multilinear principal component analysis of tensor objects

针对双转子在高速运转时难以从高、低压转子耦合出现的复杂振动现象中提取到有效的振动特征,及目前缺乏对其相应的研究等问题,本文提出一种采用张量主成分分析(multilinear principal component analysis of tensor objects,MPCA)与K-最近邻(K-nearest neighbor,KNN)分类相结合的方法,并将其用于非线性双转子系统的故障诊断。首先采用集中质量法创建非线性裂纹双转子模型及其动力学方程,针对裂纹开合角度变化分析高、低压转子的振动特性。再将振动能量信号与振动信号归一化为彩色图像样本,使用MPCA算法对故障特征进行压缩提取。最后使用KNN分类算法对不同裂纹开合角度情况进行特征分类,并计算相应的分类率。实验结果表明,在转子高速区域含有低噪声的情况下,MPCA可以有效地区分不同裂纹程度的特征信号,为非线性双转子裂纹系统的故障诊断提供了新的检测策略。

In view of difficulties in extracting effective vibration characteristics from complex vibration phenomena that are occurred when coupled with high pressure and low pressure rotors of a dual-rotor runs high-speed operation,and there aren′t corresponding researches.So,this paper proposes a method that combines multilinear principal component analysis of tensor objects(MPCA)and K-nearest neighbor(KNN)classification and applies it to fault diagnoses of nonlinear dual-rotor systems.Firstly,a nonlinear cracked dual-rotor model and its dynamic equations are created using the concentrated mass method,and the vibration characteristics of high pressure and low pressure rotors are analyzed based on the changes of crack angles.Then,the vibration energy signal and the vibration signal are normalized into color image samples,and the MPCA algorithm is used to compress and extract the fault features.Lastly,the KNN classification algorithm is used to classify the features of different crack angles,and the corresponding classification rates are calculated.The experimental results show that,in the high-speed region of the rotor,MPCA can effectively distinguish different degrees of cracked characteristic signals in the case of low noise,and provides a new detection method for fault diagnoses of nonlinear cracked dual-rotor systems.