Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems. (1st December 2022)

• Record Type: Journal Article
• Authors: Chen, Yimin; Wen, Jin; Pradhan, Ojas; Lo, L. James; Wu, Teresa
• Is Part Of: Applied energy. Volume 327(2022)
• Publication Date: 2022-12-01
• Subjects: HVAC system -- Cross-level fault -- Root cause fault diagnosis -- Discrete Bayesian Network -- Pattern matching; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.120050
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A discrete Bayesian Network (DisBN) method is developed to diagnose cross-level faults in HVAC systems. Weather and schedule-based pattern matching (WPM) method is used to generate the baseline and part of DisBN parameters. System operation interval data is collected to demonstrate the effectiveness of the developed WPM-DisBN method. The root causes of cross-level faults can be diagnosed and isolated to address the shortcomings of component-level fault diagnostic solutions. Abstract: Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. This paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.