A multi-level root cause analysis method for production anomalies in manufacturing workshops

Abstract

Production anomalies, being one of the main causes of disrupted production schedules and product quality issues, have driven the manufacturing industry to focus on real-time monitoring and effective management, as these measures undoubtedly ensure production continuity and enhance efficiency. The complexity of discrete manufacturing workshops—characterized by diverse products, complex process routes, and frequent disturbances—leads to a corresponding complexity in the occurrence and evolution of production anomalies. Unlike point-to-point models for root cause analysis of production anomalies, this paper proposes a multi-level root cause analysis model for production anomalies to reveal the key influencing factors in their evolution process. First, to address the challenge of single-dimensional manufacturing data failing to effectively represent complex production states, production state representation models of manufacturing elements are built based on a first-order graph model of manufacturing elements, enabling consistent expression of production states. Second, a production anomaly evolution pattern analysis model based on a nonlinear Granger model is proposed to answer the questions of how production anomalies arise and evolve. Then, considering the imbalance in production anomalies, a meta-learning Transformer model is designed to learn evolution patterns of production anomalies and enable root cause analysis. Finally, using a real discrete manufacturing workshop as an example, the proposed method can accurately analyze the evolution patterns of production anomalies. In the evolution pattern learning task, it achieves better learning performance and is at least 69.5 % higher than the baseline models on the root mean square error. Additionally, the method achieves an accuracy of 81.67 % in identifying the top three root cause states of production anomalies. The research results demonstrate that nonlinear networks can effectively analyze the complex evolution processes of production anomalies and enhance the Granger model's accuracy in identifying the evolution patterns. The meta-learning framework improves the generalization ability of the evolution pattern learning model, enabling more precise root cause identification. Consequently, the proposed method offers a new perspective for evolution analysis and root cause analysis of production anomalies.