AutoML for Multi-Class Anomaly Compensation of Sensor Drift

TL;DR

This paper introduces AutoML-DC, an automated approach that improves sensor drift compensation in industrial systems by employing meta-learning, ensemble methods, and hyperparameter tuning, leading to better model generalization over time.

Contribution

It proposes a novel AutoML framework specifically designed for sensor drift compensation, addressing the limitations of standard cross-validation in drift scenarios.

Findings

AutoML-DC significantly outperforms baseline models in drift scenarios.

The approach adapts effectively to different levels of sensor drift.

Enhanced classification accuracy and robustness over time.

Abstract

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.