An effective crack position diagnosis method for the hollow shaft rotor system based on the convolutional neural network and deep metric learning

• Published in: Chinese journal of aeronautics Vol. 35; no. 9; pp. 242 - 254
• Main Authors: JIN, Yuhong, HOU, Lei, CHEN, Yushu, LU, Zhenyong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2022; School of Astronautics,Harbin Institute of Technology,Harbin 150001,China%Institute of Dynamics and Control Science,Shandong Normal University,Ji'nan 250014,China
• Subjects: Convolutional neural networks; Cracked rotor; Deep metric learning; Fault diagnosis; Hollow shaft rotor; Fault diagnosis; Cracked rotor; Hollow shaft rotor; Deep metric learning; Convolutional neural networks; Convolutional neural net-works
• ISSN: 1000-9361
• DOI: 10.1016/j.cja.2021.09.010

In recent years, the crack fault is one of the most common faults in the rotor system and it is still a challenge for crack position diagnosis in the hollow shaft rotor system. In this paper, a method based on the Convolutional Neural Network and deep metric learning (CNN-C) is proposed to effectively identify the crack position for a hollow shaft rotor system. Center-loss function is used to enhance the performance of neural network. Main contributions include: Firstly, the dynamic response of the dual-disks hollow shaft rotor system is obtained. The analysis results show that the crack will cause super-harmonic resonance, and the peak value of it is closely related to the position and depth of the crack. In addition, the amplitude near the non-resonant region also has relationship with the crack parameters. Secondly, we proposed an effective crack position diagnosis method which has the highest 99.04% recognition accuracy compared with other algorithms. Then, the influence of penalty factor on CNN-C performance is analyzed, which shows that too high penalty factor will lead to the decline of the neural network performance. Finally, the feature vectors are visualized via t-distributed Stochastic Neighbor Embedding (t-SNE). Naive Bayes classifier (NB) and K-Nearest Neighbor algorithm (KNN) are used to verify the validity of the feature vectors extracted by CNN-C. The results show that NB and KNN have more regular decision boundaries and higher recognition accuracy on the feature vectors data set extracted by CNN-C, indicating that the feature vectors extracted by CNN-C have great intra-class compactness and inter-class separability.