Robust Distribution-Aware Ensemble Learning for Multi-Sensor Systems

作     者：Goodarzi, Payman Schauer, Julian Schuetze, Andreas 

作者机构：Saarland Univ Lab Measurement Technol D-66123 Saarbrucken Germany 

出 版 物：《SENSORS》 (Sensors)

年 卷 期：2025年第25卷第3期

页      面：831-831页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：German Ministry for Education and Research (BMBF) within the projects "Edge-Power" [16ME0574, 1501631A] German Ministry for Education and Research (BMBF) 

主　　题：prognostics and health management (PHM) sensor-based systems AutoML deep ensemble learning out-of-distribution (OOD) detection domain adaptation structural health monitoring condition monitoring anomaly detection 

摘      要：Detecting distribution and domain shifts is critical in decision-sensitive applications, such as industrial monitoring systems. This paper introduces a novel, robust multi-sensor ensemble framework that integrates principles of automated machine learning (AutoML) to address the challenges of domain shifts and variability in sensor data. By leveraging diverse model architectures, hyperparameters (HPs), and decision aggregation strategies, the proposed framework enhances adaptability to unnoticed distribution shifts. The method effectively handles tasks with various data properties, such as the number of sensors, data length, and information domains. Additionally, the integration of HP optimization and model selection significantly reduces the training cost of ensemble models. Extensive evaluations on five publicly available datasets demonstrate the effectiveness of the proposed framework in both targeted supervised tasks and unsupervised distribution shift detection. The proposed method significantly improves common evaluation metrics compared to single-model baselines. Across the selected datasets, the framework achieves near-perfect test accuracy for classification tasks, leveraging the AutoML approach. Additionally, it effectively identifies distribution shifts in the same scenarios, with an average AUROC of 90% and an FPR95 of 20%. This study represents a practical step toward a distribution-aware front-end approach for addressing challenges in industrial applications under real-world scenarios using AutoML, highlighting the novelty of the method.