A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems

2026-04-10

AI Summary

This work addresses the challenges of low diagnostic accuracy and inadequate uncertainty quantification in monitoring complex nonlinear industrial systems by proposing an uncertainty-aware framework that synergistically integrates data-driven and physics-based models. The approach employs a lightweight physics-informed residual design enhanced with temporal features at the feature level, while leveraging model-level ensembling and conformal prediction to effectively fuse sensor measurements, time-lagged features, and physical residuals. Evaluated on the continuous stirred-tank reactor (CSTR) benchmark, the proposed method achieves a 2.9% improvement in diagnostic accuracy over the best-performing baseline and produces smaller, well-calibrated prediction sets, thereby significantly enhancing both decision reliability and the fidelity of uncertainty quantification.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

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

Abstract

Hybrid approaches that combine data-driven learning with physics-based insight have shown promise for improving the reliability of industrial condition monitoring. This work develops a hybrid condition monitoring framework that integrates primary sensor measurements, lagged temporal features, and physics-informed residuals derived from nominal surrogate models. Two hybrid integration strategies are examined. The first is a feature-level fusion approach that augments the input space with residual and temporal information. The second is a model-level ensemble approach in which machine learning classifiers trained on different feature types are combined at the decision level. Both hybrid approaches of the condition monitoring framework are evaluated on a continuous stirred-tank reactor (CSTR) benchmark using several machine learning models and ensemble configurations. Both feature-level and model-level hybridization improve diagnostic accuracy relative to single-source baselines, with the best model-level ensemble achieving a 2.9\% improvement over the best baseline ensemble. To assess predictive reliability, conformal prediction is applied to quantify coverage, prediction-set size, and abstention behavior. The results show that hybrid integration enhances uncertainty management, producing smaller and well-calibrated prediction sets at matched coverage levels. These findings demonstrate that lightweight physics-informed residuals, temporal augmentation, and ensemble learning can be combined effectively to improve both accuracy and decision reliability in nonlinear industrial systems.

Problem

• uncertainty-aware
• condition monitoring
• industrial systems
• hybrid intelligent framework
• predictive reliability

Innovation

• hybrid intelligent framework
• physics-informed residuals
• temporal feature augmentation
• model-level ensemble
• conformal prediction