AutoML for multi-class anomaly compensation of sensor drift
Melanie Schaller, Mathis Kruse, Antonio J. Ortega, Marius Lindauer, Bodo Rosenhahn · Measurement · 2025
Addressing sensor drift is essential in industrial measurement systems, where precise data output is necessary for maintaining accuracy and reliability in monitoring processes, as it progressively degrades the performance of machine learning models over time. Our findings indicate that the standard cross-validation method used in existing model training overestimates performance by inadequately accounting for drift. This is primarily because typical cross-validation techniques allow data instances to appear in both training and testing sets, thereby distorting the accuracy of the predictive evaluation. As a result, these models are unable to precisely predict future drift effects, compromising their ability to generalize and adapt to evolving data conditions. This paper presents two solutions: (1) a novel sensor drift compensation learning paradigm for validating models, and (2) automated machine learning (AutoML) techniques to enhance classification performance and compensate sensor drift. By employing strategies such as data balancing, meta-learning, automated ensemble learning, hyperparameter optimization, feature selection, and boosting, our AutoML-DC (Drift Compensation) model significantly improves classification performance against sensor drift. AutoML-DC further adapts effectively to varying drift severities. • Our analysis demonstrates that the conventional training configurations are suboptimal in learning and compensating for sensor drift. Thus, we propose a novel sensor drift compensation learning training paradigm that closely matches real-world scenarios. • Our findings further indicate, that AutoML techniques along with the proposed training paradigm enable effective drift adaptation to evolving levels of drift severity and complex drift dynamics in sensor measurements. • By utilizing meta-learning, AutoML-DC starts from initial configurations based on prior data, lowering the extensive data requirements normally needed for training neural networks or ensemble models. • We make use of AutoML techniques to enhance model robustness (see standard deviation) by combining multiple models to capture diverse data patterns, optimizing feature selection, and preventing overfitting through smart training termination. • We conduct extensive benchmarking experiments against existing models and highlight the significant accuracy improvements realized when adopting AutoML-DC in practical drift compensation scenarios in industrial measurements.