Electrical-STGCN: An Electrical Spatio-Temporal Graph Convolutional Network for Intelligent Predictive Maintenance

• Published in: IEEE transactions on industrial informatics 2022-12, Vol.18 (12), p.8509-8518
• Main Authors: Jiang, Yuchen, Dai, Pengwen, Fang, Pengcheng, Zhong, Ray Y., Cao, Xiaochun
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Artificial neural networks; attribute interactions; Feature extraction; Hidden Markov models; Industrial applications; industrial Internet of Things (IIoT); Intelligent sensors; Internet of Things; Machine learning algorithms; Prediction algorithms; Predictive maintenance; Reliability; Support vector machines; temporal dependence
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2022.3143148

With the rapid improvement of Industrial Internet of Things and artificial intelligence, predictive maintenance (PdM) has attracted great attention from both academia and industrial practitioners. When equipment is running, the electrical attributes have intrinsic relations. Meanwhile, they are changing over time. However, existing PdM models are often limited as they lack considering both attribute interactions and temporal dependence of the dynamic working system. To address the problem, in this article, we propose an electrical spatio-temporal graph convolutional network (Electrical-STGCN) for PdM. First, it takes a sequence of electrical records as input. Next, both attribute interactions and temporal dependence are established to extract features. Then, the extracted features are fed into a prediction component. Finally, the output of the Electrical-STGCN (i.e., remaining useful life) can help the workers decide whether to carry out equipment maintenance. The effectiveness of the proposed method is verified in real-world cases. Our method achieves 85.2% Accuracy and 0.9 F1-Score, which are better than the other approaches.