SPC and Kalman filter-based fault detection and diagnosis for an air-cooled chiller

Frontiers of Electrical and Electronic Engineering in China

Volumn 6, Issue 3, 2011, Pages 412-423

  Sun, Biao ^a   Luh, Peter B ^a,b   O'Neill, Zheng ^c  

^a TSINGHUA UNIVERSITY   (China)

^b UNIVERSITY OF CONNECTICUT   (United States)

^c UNITED TECHNOLOGIES RESEARCH CENTER   (United States)

Author keywords

air cooled chiller; fault detection and diagnosis (FDD); Kalman filter; statistical process control (SPC)

Indexed keywords

EID: 80052300646     PISSN: 16733460     EISSN: 16733584     Source Type: Journal    
DOI: 10.1007/s11460-011-0164-9     Document Type: Article

References (17)

1. Lausten J. Energy efficiency requirements in building codes, energy efficiency policies for new buildings. Paris: IEA Information Paper. International Energy Agency, 2008.
2. Rossi T M, Braun J E. Minimizing operating costs of vapor compression equipment with optimal service scheduling. International Journal of Heating, Ventilating, Air Conditioning and Refrigerating Research, 1996, 2(1): 3-25.
3. Sun B, Luh P B. Energy doctors: SPC and Kalman filterbased fault detection and diagnosis. In: Proceedings of the 7th IEEE Conference on Automation Science and Engineering. 2011.
4. Luh P B, Sun B. Energy doctors: SPC and Kalman filterbased fault detection and diagnosis. UTRC report, 2010.
5. Katipamula S, Brambley M R. Methods for fault detection, diagnostics, and prognostics for building systems - A review, Part I. International Journal of Heating, Ventilating, Air Conditioning and Refrigerating Research, 2005, 11(1): 3-25.
6. Rossi T M, Braun J E. A statistical, rule-based fault detection and diagnostic method for vapor compression air conditioners. International Journal of Heating, Ventilating, Air Conditioning and Refrigerating Research, 1997, 3(1): 19-37.
7. Breuker M S, Braun J E. Evaluating the performance of a fault detection and diagnostic system for vapor compression equipment. International Journal of Heating, Ventilating, Air Conditioning and Refrigerating Research, 1998, 4(4): 401-425.
8. Reddy T A, Niebur D, Andersen K K, Pericolo P P, Cabrera G. Evaluation of the suitability of different chiller performance models for on-line training applied to automated fault detection and diagnosis. International Journal of Heating, Ventilating, Air Conditioning and Refrigerating Research, 2003, 9(4): 385-414.
9. Jiang Y Q, Yao Y, Deng S, Ma Z L. Applying grey forecasting to predicting the operating energy performance of air cooled water chillers. International Journal of Refrigeration, 2004, 27(4): 385-392.
10. McIntosh I B D, Mitchell J W, Beckman W A. Fault detection and diagnosis in chillers, Part 1: Model development and application. ASHRAE Transactions, 2000, 106(2): 268-282.
11. Wang S W, Cui J T. Sensor-fault detection, diagnosis, and estimation for centrifugal chiller systems using principal-component analysis method. Applied Energy, 2005, 82(3): 197-213.
12. Wang S W, Zhou Q, Xiao F. A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults. Energy and Building, 2010, 42(4): 477-490.
13. Breuker M S, Braun J E. Common faults and their impacts for rooftop air conditioners. International Journal of Heating, Ventilating, Air Conditioning and Refrigerating Research, 1998, 4(3): 303-318.
14. Gordon J M, Ng K C. Cool Thermodynamics. Cambridge: Cambridge International Science Publishing, 2000.
15. Jiang W, Reddy T A. Reevaluation of the Gordon-Ng performance models for water-cooled chillers. ASHRAE Transactions, 2003, 109(2): 272-287.
16. Bersimis S, Psarakis S, Panaretos J. Multivariate statistical process control charts: An overview. Quality and Reliability Engineering International, 2007, 23(5): 517-543.
17. Bar-Shalom Y, Li X R. Estimation and Tracking-Principles, Techniques, and Software. London: Artech House, 1993.