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<p>In the manufacturing sector, abnormal production can disrupt production schedules, leading to significant economic and reputational losses for manufacturers. To address this issue, in this study, we present an explainable mechanism for production process anomalies (EM2PA) designed to clarify the complex coupling relationships among various manufacturing factors, analyze the impact of these factors on the production process, identify abnormal production, provide explanations for its causes, and enable trace-back analysis. EM2PA consists of three modules: the data augmenter, the influence factor recognizer, and the causal interpreter. Specifically, the data augmenter generates small sample data of abnormal production, the influence factor recognizer decouples the complex coupling relationships and identifies the factors influencing abnormal production, and the causal interpreter provides causal explanations. Furthermore, through a case study based on the actual production process of a discrete manufacturing workshop, we demonstrate the effectiveness of EM2PA in identifying the root causes of problems, while highlighting the importance of explainability and causal analysis of production process anomalies.</p>

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Explainable mechanism for production process anomalies based on digital twin

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月10日

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  • Weiwei Qian,
  • Litong Zhang,
  • Yu Guo,
  • Sai Geng,
  • Mingjie Jiang,
  • Yuhan Zheng,
  • Shengbo Wang,
  • Shaohua Huang

摘要

In the manufacturing sector, abnormal production can disrupt production schedules, leading to significant economic and reputational losses for manufacturers. To address this issue, in this study, we present an explainable mechanism for production process anomalies (EM2PA) designed to clarify the complex coupling relationships among various manufacturing factors, analyze the impact of these factors on the production process, identify abnormal production, provide explanations for its causes, and enable trace-back analysis. EM2PA consists of three modules: the data augmenter, the influence factor recognizer, and the causal interpreter. Specifically, the data augmenter generates small sample data of abnormal production, the influence factor recognizer decouples the complex coupling relationships and identifies the factors influencing abnormal production, and the causal interpreter provides causal explanations. Furthermore, through a case study based on the actual production process of a discrete manufacturing workshop, we demonstrate the effectiveness of EM2PA in identifying the root causes of problems, while highlighting the importance of explainability and causal analysis of production process anomalies.