A Physics-Regularized Degradation Model for Cooling System Health Management

Xiao Liu () and Mohammadmahdi Hajiha ()
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Xiao Liu: University of Arkansas
Mohammadmahdi Hajiha: University of Arkansas

A chapter in Handbook of Smart Energy Systems, 2023, pp 607-623 from Springer

Abstract: Abstract This chapter describes how engineering domain knowledge and governing physics are integrated with sensor data for health prognostics of complex engineered systems (e.g., cooling systems). In particular, a flexible two-layer physical-statistical modeling framework is presented that enables the integration of system physics into the modeling and interpretation of sensor monitoring data. A case study on the modeling of system state degradation for data center cooling systems is presented. Based on the thermodynamic law that governs the relationship between system internal states, operating conditions and cooling efficiency, a two-stage physics-based statistical approach for modeling the cooling efficiency is presented. The approach takes into account the statistical dependence among system state variables, and captures the complex dependence structure by the Archimedean family of copulas with its generator function approximated by cubic B-splines. The discussions and case studies demonstrate how engineering knowledge and governing physics can be integrated into data-driven models for the health prognostics of complex physical systems.

Keywords: Prognostics; Physics-informed statistical learning; Archimedean copula; Reliability; Degradation; Dynamical models (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_111

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DOI: 10.1007/978-3-030-97940-9_111

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