Condition-Aware Latent Gaussian Process Modeling for Temperature-Field Reconstruction from Randomly Available Sensors

Abstract

Accurate temperature-field information is essential for structural safety, condition monitoring, and predictive maintenance in mechanical systems. However, dense sensor deployment and extensive simulations are often costly, making full-field thermal characterization difficult in practical industrial scenarios. To address this challenge, this paper proposes a jointly optimized variational probabilistic framework for reconstructing full-field temperature distributions from sparse sensor observations under varying operating conditions (OCs) and randomly available sensor configurations. The proposed method combines latent Gaussian process modeling with structured OC integration to capture the intrinsic low-dimensional structure of temperature fields and explicitly model OC-induced variations. In addition, observation-pattern similarity is incorporated into latent regularization to improve robustness under random sensor availability. The framework enables accurate full-field reconstruction and uncertainty quantification from limited measurements within a unified probabilistic learning process. Experimental results demonstrate improved accuracy and robustness under varying OCs and sparse sensor configurations, showing its potential for cost-effective industrial monitoring and digital-twin applications.