Conference proceeding
Comparison of two model based automated fault detection and diagnosis methods for centrifugal chillers
ES2008: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ENERGY SUSTAINABILITY - 2008, VOL 1, v 1, pp 577-588
01 Jan 2009
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Research has been ongoing during-the last several years on developing robust automated fault detecting and diagnosing (FDD) methods applicable for process faults in chillers used in commercial buildings. These FDD methods involve using sensor data from available thermal, pressure and electrical measurements from commercial chillers to compute characteristic features (CF) which allow more robust and sensitive fault detection than using the basic sensor data itself one of the proposed methods is based on the analytical redundancy approach using polynomial black-box multiple linear regression models for each CF that are identified from fault-free data in conjunction with a diagnosis table. The second method is based on a classification approach involving linear discriminant analysis to identify the classification models whereby both the detection and diagnosis can be done simultaneously. This paper describes the mathematical basis of both methods, illustrates how they are to be tuned using the same fault-free data set in conjunction with limited faulty data, and then compares their performance when applied to different fault severity levels. The relative advantages and disadvantages of each method are highlighted and future development needs are pointed out.
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Details
- Title
- Comparison of two model based automated fault detection and diagnosis methods for centrifugal chillers
- Creators
- T. Agami Reddy
- Publication Details
- ES2008: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ENERGY SUSTAINABILITY - 2008, VOL 1, v 1, pp 577-588
- Conference
- ES2008: 2nd International Conference on Energy Sustainability - 2008, 2nd (Jacksonville, Florida, United States, 10 Aug 2008–14 Aug 2008)
- Publisher
- ASME
- Number of pages
- 12
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000265637200066
- Other Identifier
- 991019185111804721
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- Web of Science research areas
- Energy & Fuels
- Engineering, Mechanical