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

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.     

力矩输出能力最优化混合执行机构操纵律设计

当航天器执行高动态敏捷机动或者姿态动态跟踪控制等任务时，常使用控制力矩陀螺（control moment gyroscope，简称CMG）和飞轮（reaction wheel，简称RW）构成的混合执行机构来提供大力矩。提出了基于力矩输出能力最优化的混合执行机构操纵律，从几何角度出发，给出了力矩输出能力最优的CMG框架角速度和RW角加速度，通过引入参数，并讨论参数的设置的最优，使得框架转速误差和输出力矩误差的混合二次型达到最小，保证了混合执行机构在输出力矩误差最小的情况下，力矩输出能力最优。以金字塔构型的CMG集群和正交的RW集群构成的混合执行机构为例，对基于力矩输出能力最优化的混合执行机构操纵律进行合理化分析，证明了引入参数的作用，并且证明了混合执行机构不存在CMG奇异情况。仿真结果表明，基于力矩输出能力最优化的混合执行机构操纵律解决了CMG奇异的问题并使得RW不陷入饱和，输出力矩误差较小，输出力矩能力强，能够应用于航天器大角度机动任务。