Compound Fault Diagnosis of Harmonic Drives Using Deep Capsule Graph Convolutional Network

Abstract

The harmonic drive is a key component of the industrial robot. Because of its large reduction ratio and excessive dynamic loading, various kinds of faults may occur. In particular, since the robot is an integrated system, it is not unusual to have multiple harmonic drive malfunctions simultaneously, which is difficult to diagnose. In practice, these kinds of compound faults are often mislabeled as single faults causing missing repair. In this article, we propose a deep capsule graph convolutional network (DCGCN) approach to diagnose compound faults of harmonic drives. First, the multisensor data are used to obtain the frequency spectrum of the fault signal and construct the label relationship map of the adjacency matrix. Second, the deep capsule network is used to learn the representation of the fault vector, and the graph convolutional network is used to learn the relationship between different single-label faults. Third, the two networks are combined to obtain diagnosing results. Finally, the dynamic routing algorithm and the margin loss function are used to optimize the DCGCN. The experimental results show that the proposed DCGCN can effectively diagnose compound faults under varying working conditions, outperforming other existing state-of-the-art methods.