Hierarchical Anomaly Detection and Model Update Framework for Steel Manufacturing

In an integrated steelmaking process, equipment failures can significantly impact overall operations. Therefore, predictive detection and prevention of failures are critical. In this study, A three-layer hierarchical predictive detection system has been developed in order to utilize large-scale, multivariate operational data. This system is designed to identify overall trends by leveraging big data, detect correlation breakdowns through domain knowledge, and detect shifts in single-signal levels. The effectiveness of the proposed system has been confirmed through its application to actual operational data from the steel manufacturing process. In addition, general anomaly detection models, including our system, rely on quantifying deviations from a normal state as an anomaly score. In manufacturing settings, data drift often occurs due to factors such as equipment part replacements or changes in operational conditions. When data drift occurs, it becomes necessary to redefine the normal state. However, in manufacturing environments, temporary runs or experimental operations mean that the data following a drift is not necessarily guaranteed to normal data. Therefore, it is necessary to evaluate whether the data distribution is normal before and after the drift on a case-by-case basis. Current approaches do not provide a quantitative means to make this decision, leading to the issue that model updates depend on the judgment of experts. To address this, we propose a method that utilizes similar equipment conditions to guide the timing and procedure for model updates. By applying Jensen–Shannon divergence to measure differences among four data distributions—derived from two machines and two distinct periods— we provide appropriate guidance for model construction based on a table of potential anomalies. Through validation using real data from two adjacent continuous casters, we confirmed that identifying abnormal equipment and time periods enables us to propose appropriate normal operating windows. These validation results indicate that the proposed system allows for comprehensive predictive maintenance, integrating domain knowledge and thereby contributing to stable operations in steelmaking facilities.