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.