an energy graph-based approach to fault diagnosis of a transcritical co2 heat pump

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ID: 142346
2020
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Abstract
The objective of this paper is to describe an energy-based approach to visualize, identify, and monitor faults that may occur in a water-to-water transcritical CO 2 heat pump system. A representation using energy attributes allows the abstraction of all physical phenomena present during operation into a compact and easily interpretable form. The use of a linear graph representation, with heat pump components represented as nodes and energy interactions as links, is investigated. Node signature matrices are used to present the energy information in a compact mathematical form. The resulting node signature matrix is referred to as an attributed graph and is populated in such a way as to retain the structural information, i.e., where the attribute points to in the physical system. To generate the energy and exergy information for the compilation of the attributed graphs, a descriptive thermal–fluid model of the heat pump system is developed. The thermal–fluid model is based on the specifications of and validated to the actual behavioral characteristics of a physical transcritical CO 2 heat pump test facility. As a first step to graph-matching, cost matrices are generated to represent a characteristic residual between a normal system node signature matrix and a faulty system node signature matrix. The variation in the eigenvalues and eigenvectors of the characteristic cost matrices from normal conditions to a fault condition was used for fault characterization. Three faults, namely refrigerant leakage, compressor failure and gas cooler fouling, were considered. The paper only aims to introduce an approach, with the scope limited to illustration at one operating point and considers only three relatively large faults. The results of the proposed method show promise and warrant further work to evaluate its sensitivity and robustness for small faults.
Reference Key
uren2020energiesan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Kenneth R. Uren;George van Schoor;Martin van Eldik;Johannes J. A. de Bruin
Journal acs combinatorial science
Year 2020
DOI
10.3390/en13071783
URL
https://www.mdpi.com/1996-1073/13/7/1783
Keywords
fault detection

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