Relational Conduction Graph Network for Intelligent Fault Diagnosis of Rotating Machines Under Small Fault Samples

Abstract

Fault samples obtained in real-world environment are limited, which makes it hard to diagnose faults of rotating machines (RM) accurately by using the existing intelligent diagnosis methods. To solve the issue above, a new relational conduction graph network (RCGN) is proposed in this paper, which is trained on dataset produced in lab environment to identify fault types of the RM operated in real-world environments. First, feature extractor is constructed to mine fault features from input sample. Second, relational graph network is designed to treat each sample pair as a relational node, and then propagate and aggregate the similarities and relations between samples, so as to mine more discriminative relational characteristics from sample pairs. Moreover, a similarity function is introduced to assess whether the consisting samples in relational node are from the same class to determine fault types. Finally, extensive experiments on two datasets produced in real-world environments are used to validate the superior performance of the RCGN method. The results show that the RCGN method can correctly diagnose fault types of several RM operated in real-world environments, even when each fault type of these RM has only one sample. The diagnostic performance has been greatly improved as compared to state-of-the-art methods.