Quantitative recommendation of fault diagnosis algorithms based on multi-order random graph convolution under case-learning paradigm

• Published in: Advanced engineering informatics Vol. 64; p. 103108
• Main Authors: Lu, Chen, Zou, Xinyu, Sun, Lulu, Zhao, Zhengduo, Tao, Laifa, Ding, Yu, Ma, Jian
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2025
• Subjects: Case-learning-based paradigm; Diagnosis algorithm recommendation; Diagnosis cases correlation modeling; Ensemble learning; Graph convolutional network; human-AI collaboration; Diagnosis cases correlation modeling; Case-learning-based paradigm; Diagnosis algorithm recommendation; Graph convolutional network; human-AI collaboration; Ensemble learning
• ISSN: 1474-0346
• DOI: 10.1016/j.aei.2025.103108

•Used AI to enhance PHM decisions, automating diagnosis algorithms selection and reducing reliance on individual expertise.•Developed a graph network-based correlation modeling and optimization method for fault diagnosis cases, improving information aggregation.•Proposed Multi-order Random Graph Convolution Network for diagnosis algorithm recommendation under a case-learning-based paradigm.•Enhanced accuracy and stability through ensemble learning, utilizing multiple primitive member recommenders within the MOR-GCN framework.•Validated with a gearbox fault diagnosis case set, demonstrating improvements in both single and fuzzy recommendation accuracies. The rapid development of intelligent algorithms has significantly expanded the range of algorithms available for Prognostics and Health Management (PHM). Selecting the appropriate algorithm for a specific task is crucial for effective PHM applications. Learning from past PHM cases is an effective way to automate algorithm recommendations, reducing reliance on expert experience. Human-AI collaboration provides new ideas for achieving this capability. However, in emerging fields or early-stage research, the limited number of cases—coupled with volatility and noise—often results in low recommendation accuracy and weak stability. To address this issue, we propose a multi-order random graph convolution network (MOR-GCN) within a case-learning paradigm. This method uses graphs to model and optimize case correlations, helping engineers narrow down algorithm choices to suitable candidates. We first develop a correlation modeling and optimization method based on a graph network, enhancing information aggregation between similar cases and reducing the impact of case noise on the recommendation model. Next, we design an ensemble recommender using MOR-GCN, which aggregates features of adjacent case nodes through a case correlation network graph (CCNG), further improving recommendation accuracy and stability through ensemble learning. Experimental results from a gearbox fault diagnosis case set demonstrate that the MOR-GCN model can automatically recommend fault diagnosis algorithms based on task attributes, achieving an average accuracy of 77.20 % for single recommendations and 89.90 % for fuzzy recommendations. This framework showcases the potential of artificial intelligence (AI) to assist human decision-making in PHM, minimizing the dependency on expert knowledge during the PHM design stage.