Robust concept drift handling in dynamic industrial systems: A multi-objective optimization approach

Machine learning (ML) systems are increasingly integral to modern industrial environments, yet their performance is susceptible to “concept drift”—changes in data distribution or relationships over time—which can degrade predictive accuracy and operational efficiency. Conventional approaches often rely on periodic model retraining or drift detectors with fixed parameters, which can lead to suboptimal adaptation, frequent false alarms, or missed drifts. Moreover, the interplay between concept drift detection and retraining strategies is often overlooked in the current literature. Through a comprehensive multi-objective optimization analysis of drift detector parameters and retraining strategies, this work quantifies critical trade-offs and parameter importance across diverse drift types (abrupt, gradual, recurring, local, global) for classification and regression tasks. The analysis is grounded in both synthetic benchmark datasets and a real-world industrial case study from automotive component manufacturing. Our findings reveal that abrupt drifts demand distinct sensitivity parameters (e.g., low delta, lambda) and immediate retraining, while gradual drifts prioritize adaptation timing (e.g., high retraining lag) and larger data windows. Localized drifts further necessitate extended contextual windows. Results demonstrate that configurations optimized via our analysis significantly outperform defaults, improving detection timeliness and adaptation effectiveness in synthetic benchmarks as well as the industrial use case. The study provides practitioners with actionable, evidence-based and comprehensive guidelines for tailoring drift-handling mechanisms to specific operational constraints and drift characteristics, ultimately enhancing the robustness of industrial ML systems.