Physics-sensing framework driven by non-intrusion hyper-reduced-order model with extremely sparse data: Application to an industrial high-temperature component

Hongjiang Wang, Han Dong, Chaohui Huang, Weizhe Wang and Yingzheng Liu

Energy, 2025, vol. 325, issue C

Abstract: Condition monitoring are critical for ensuring the long-term stability and efficiency of equipment operations. In particular, under extreme conditions, the number of sensors is often severely limited, resulting in extremely sparse sensor data. This scarcity renders it challenging to obtain interpretable high-dimensional physical information in real-time. Many methods for condition monitoring predominantly rely on sensor data analysis, such as nonlinear fitting, which often lack physical interpretability. Hyper projection-based reduced order models (HPROMs) incorporating the physics, provide strong physical interpretability and high dimensional physical field real-time computing capability. However, HPROMs strictly adhere to forward calculation procedures because of approximation process of intrusive operators. To address these challenges, this paper introduces a novel physics-sensing framework (PSF) driven by a non-intrusive, inverse, hyper projection-based reduced order model (NII-HPROM) with extremely sparse sensor data. The NII-HPROM circumvents the approximation process of intrusive operators, enabling direct inverse computation of physical fields at hyper-reduced speeds. Moreover, the PSF incorporates a reliability evaluation system (RES), a physical noise-filtering system (PNFS), and an abnormal condition identification system (ACIS), not only offering a comprehensive solution but also ensuring reliable evaluation, noise filtering, and fault identification for high-temperature components. In this study, the PSF is applied to an industrial high-temperature component, the rotor, using only two sensors to achieve rapid inverse nonlinear temperature field calculations, which are 579.6 times faster than HPROM forward iterative computations and 17,500 times faster than full-order model forward iterative calculations.

Keywords: Reduced-order models; Inverse problem; Sparse data; Hyper-reduction; Condition monitoring (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:325:y:2025:i:c:s0360544225016615

DOI: 10.1016/j.energy.2025.136019

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