Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

Abstract

In the realm of manufacturing processes, equipment failures can result in substantial financial losses and pose significant safety hazards. Consequently, prior research has primarily been focused on preemptively detecting anomalies before they manifest. However, within industrial contexts, the precise interpretation of predictive outcomes holds paramount importance. This has spurred the development of research in Explainable Artificial Intelligence (XAI) to elucidate the inner workings of predictive models. Previous studies have endeavored to furnish explanations for anomaly detection within these models. Nonetheless, rectifying these anomalies typically necessitates the expertise of seasoned professionals. Therefore, our study extends beyond the mere identification of anomaly causes; we also ascertain the specific adjustments required to normalize these deviations. In this paper, we present novel research avenues and introduce three methods to tackle this challenge. Each method has exhibited a remarkable success rate in normalizing detected errors, scoring 97.30%, 97.30%, and 100.0%, respectively. This research not only contributes to the field of anomaly detection but also amplifies the practical applicability of these models in industrial environments. It furnishes actionable insights for error correction, thereby enhancing their utility and efficacy in real-world scenarios.