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Physics-based dynamic bayesian network for fault detection and diagnostics in building HVAC systems.

Number: 3521

Author(s) : CHEN D., SUN Q. Z., QIAO Y.

Summary

Heating, ventilation and air conditioning (HVAC) systems, as key components in commercial building air conditioning systems, significantly impact indoor environmental quality and building energy efficiency. This paper focuses on developing a physics-based dynamic Bayesian network (PBDBN) that integrates a dynamic scheme for detecting
and diagnosing faults in building HVAC systems. Contrary to traditional data-driven BN methods that infer structure from statistical processes, our approach constructs the BN structure grounded on physical equations, with coefficients determined through data-driven processes. Moreover, unlike reference model-based BN methods that keep the building
model separate from the BN structure, our approach significantly simplifies and incorporates the building model directly into the BN structure. A detailed structure construction of the proposed PBDBN is demonstrated in this paper. The model is evaluated by injecting minor sensor faults into the HVAC system. The results show that the proposed
PBDBN-FDD has significant improvements compared to the existing DBN-FDD methods.

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  • Original title: Physics-based dynamic bayesian network for fault detection and diagnostics in building HVAC systems.
  • Record ID : 30032921
  • Languages: English
  • Subject: Technology
  • Source: 2024 Purdue Conferences. 8th International High Performance Buildings Conference at Purdue.
  • Publication date: 2024/07/15

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