Graph Continual Learning Network: An Incremental Intelligent Diagnosis Method of Machines for New Fault Detection

Abstract

Streaming data of machines is continuously collected in practical applications, which produces new fault information with respect to the health change. Therefore, a lifelong-learning intelligent diagnosis model is desired for new fault type recognition based on the streaming data. However, existing research in intelligent fault diagnosis always treats new fault type detection and class incremental learning as two independent problems, which reduces their practicality in industrial applications. To tackle this limitation, a graph continual learning network is constructed for incremental intelligent diagnosis of new faults. The method integrates the advantages of both new fault type detection and class incremental learning. In the method, a graph convolutional network (GCN) based model is formulated for detecting new classes to prejudge whether the DL model needs to be updated. Once any new class is detected, class incremental learning is started automatically to update the DL model without leading to catastrophic forgetting. The proposed method is applied to a pump fault diagnosis case with incremental fault types. Results show that the proposed method offers an effective solution for online intelligent fault diagnosis with satisfactory classification performance <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i>—Existing DL-based intelligent diagnosis models often assume the closed-set assumption, i.e., fault types of the monitoring data are the same as those of the training data. In the situations where the assumption is held, DL models receive high-precision recognition results towards instances from the monitoring data stream. However, when a new fault pattern appears in the monitoring data stream, the previous well-trained diagnosis model will inevitably classify the instances into one of the known patterns, resulting in untrustworthy results. This paper proposes a method with the aim of tackling the above issue. The method is able to realize the following functions: Once the instances in the streaming data are detected as a new fault pattern, the class incremental learning will be started automatically. The detected class is used to update the diagnosis model. On the contrary, if the instances are not detected as a new class, they will be sent to the diagnosis model for fault recognition. An incremental diagnosis task is designed under a centrifugal pump application scenario. The results demonstrate the feasibility of the proposed method. The method is anticipated with more application scenarios to verify its superiority.

Topics & Concepts