AutoML for Multi-Class Anomaly Compensation of Sensor Drift

2025-02-26

AI Summary

Industrial sensor drift causes persistent degradation of machine learning model performance, while standard cross-validation—ignoring temporal dependencies and inducing data leakage—severely overestimates model robustness. To address this, we propose a drift-aware model validation paradigm that eliminates the artificial inflation of drift robustness inherent in conventional validation protocols. Furthermore, we introduce AutoML-DC, the first AutoML system designed for adaptive compensation of diverse sensor drift types, capable of dynamically adjusting to varying drift severities. AutoML-DC integrates data balancing, meta-learning, automated ensemble construction, hyperparameter optimization, feature selection, and gradient boosting into an end-to-end drift compensation learning pipeline. Extensive experiments demonstrate that AutoML-DC significantly outperforms state-of-the-art methods in anomaly detection accuracy, generalizability, and robustness across multiple drift categories, while maintaining consistently high performance under varying drift intensities.

Technology Category

• Machine Learning: Auto ML and Hyperparameter Tuning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Addressing sensor drift in industrial measurement systems
• Improving accuracy of predictive evaluation in ML models
• Enhancing classification performance with AutoML techniques

Innovation

• Automated machine learning techniques
• Novel sensor drift compensation paradigm
• Data balancing and meta-learning strategies