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Entity clustering-based meta-learning for link prediction in evolutionary fault diagnosis event graphs

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Abstract

Fault diagnosis plays an important role in intelligent manufacturing. Knowledge modelling is often used for intelligent fault diagnosis purposes, and link prediction is performed in knowledge graphs to locate and trace system faults. However, due to the sparsity of data during the training process, meta-learning methods have been introduced, but they can lead to the overgeneralization of the training parameters and affect the accuracy of link prediction. To address this issue, this paper proposes an entity clustering-based meta-learning model for link prediction in evolutionary fault diagnosis event graphs. The model consists of three parts: triple clustering based on graph convolutional networks, meta-learning for divide-and-conquer data, and meta-learning model reinforcement based on scene information. Through data preprocessing and additional model fitting steps, the weaknesses of the existing meta-learning approaches regarding dissimilated data learning are overcome. The experimental results show that compared with traditional embedding-based models and the existing meta-learning-based link prediction methods, the proposed method can not only improve the link prediction accuracy achieved on public datasets but also have attain performance on datasets with fault diagnosis background information.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work is supported by the National Defense Basic Scientifc Research Program of China (JCKY2019205A003).

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Author notes

  1. Jianming Shen, Xuebing Zhang and Dandan Shan contributed equally to this work.

Authors and Affiliations

  1. School of mechanical engineering, Zhejiang Universit, Zheda Road 38, Hangzhou, 310027, Zhejiang Province, China

    Tian Wang & Qiang Fang

  2. College of Computer Science and Technology, Zhejiang Universit, Zheda Road 38, Hangzhou, 310027, Zhejiang Province, China

    Meng Chi

  3. AVIC Shenyang Aircraft Corporation, 1 Lingbei Road, Shenyang, 110034, Liaoning Province, China

    Jianming Shen, Xuebing Zhang & Dandan Shan

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  1. Tian Wang
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Contributions

Tian Wang received the bachelor’s degree in the School of Mechanical and Automotive Engineering, South China University of Technology, in 2017. He is currently pursuing the Doctor’s degree in School of Mechanical Engineering, Zhejiang University. His research interests include edge computing, deep learning and automatic diagnostic system for aircraft wiring.

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Correspondence to Meng Chi.

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Wang, T., Fang, Q., Chi, M. et al. Entity clustering-based meta-learning for link prediction in evolutionary fault diagnosis event graphs. Appl Intell 54, 10525–10540 (2024). https://doi.org/10.1007/s10489-024-05749-8

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