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Bayesian networks for whole building level fault diagnosis and isolation.

Number: pap. 3214

Author(s) : CHEN Y., WEN J., CHEN T., et al.

Summary

Buildings consume about 40% of primary energy in the U.S., and 51% of the primary energy usage in commercial buildings are consumed by heating, ventilation and air conditioning (HVAC) system. Malfunctioning sensors, components, and control systems, as well as degrading HVAC and lighting components are main the reasons for energy waste and unsatisfactory indoor environment. In building HVAC systems, faults occurring in one component or equipment can cause abnormality in other closed subsystems. Therefore, a system level fault diagnosis method is helpful to locate root-cause for such faults. Bayesian network (BN) is a prevalent tool in fault diagnosis which can handle probabilistic reasoning of uncertainty. In this paper, a two-layer Bayesian network which consists of fault layer and fault symptom layer is developed to diagnose system level faults that have an impact on multiple subsystems for building HVAC system during a cooling operation mode. Weather/schedule information based Pattern Matching (WPM) method is developed create the baseline data and to generate LEAK probabilities for the developed BN. BAS data from a campus building during the cooling season are collected to evaluate the effectiveness of the proposed method.

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Pages: 10

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Details

  • Original title: Bayesian networks for whole building level fault diagnosis and isolation.
  • Record ID : 30024747
  • Languages: English
  • Subject: Environment
  • Source: 2018 Purdue Conferences. 5th International High Performance Buildings Conference at Purdue.
  • Publication date: 2018/07/09

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