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Fault Diagnosis in HVAC ChillersModern buildings are being equipped with increasingly sophisticated power and control systems with substantial capabilities for monitoring and controlling the amenities. Operational problems associated with heating, ventilation, and air-conditioning (HVAC) systems plague many commercial buildings, often the result of degraded equipment, failed sensors, improper installation, poor maintenance, and improperly implemented controls. Most existing HVAC fault-diagnostic schemes are based on analytical models and knowledge bases. These schemes are adequate for generic systems. However, real-world systems significantly differ from the generic ones and necessitate modifications of the models and/or customization of the standard knowledge bases, which can be labor intensive. Data-driven techniques for fault detection and isolation (FDI) have a close relationship with pattern recognition, wherein one seeks to categorize the input-output data into normal or faulty classes. Owing to the simplicity and adaptability, customization of a data-driven FDI approach does not require in-depth knowledge of the HVAC system. It enables the building system operators to improve energy efficiency and maintain the desired comfort level at a reduced cost. In this article, we consider a data-driven approach for FDI of chillers in HVAC systems. To diagnose the faults of interest in the chiller, we employ multiway dynamic principal component analysis (MPCA), multiway partial least squares (MPLS), and support vector machines (SVMs). The simulation of a chiller under various fault conditions is conducted using a standard chiller simulator from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE). We validated our FDI scheme using experimental data obtained from different types of chiller faults.
Document ID
20060051821
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Choi, Kihoon
(Connecticut Univ. Storrs, CT, United States)
Namuru, Setu M.
(Connecticut Univ. Storrs, CT, United States)
Azam, Mohammad S.
(Connecticut Univ. Storrs, CT, United States)
Luo, Jianhui
(Connecticut Univ. Storrs, CT, United States)
Pattipati, Krishna R.
(Connecticut Univ. Storrs, CT, United States)
Patterson-Hine, Ann
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 23, 2013
Publication Date
August 1, 2005
Publication Information
Publication: IEEE Instrumentation and Measurement Magazine
ISSN: 1094-6969
Subject Category
Quality Assurance And Reliability
Funding Number(s)
CONTRACT_GRANT: NAG2-1635
Distribution Limits
Public
Copyright
Other

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