A Review of Fault Detection and Diagnosis Methodologies for Air-Handling Units
PDF

Keywords

Heating, Ventilation and Air-Conditioning systems
Air-Handling Units
Fault Detection and Diagnosis systems
Predictive maintenance.

How to Cite

1.
F. Guarino, V. Filomena, L. Maffei, S. Sibilio, A. Rosato. A Review of Fault Detection and Diagnosis Methodologies for Air-Handling Units. Glob. J. Energ. Technol. Res. Updat. [Internet]. 2019 Dec. 30 [cited 2024 Nov. 21];6(1):26-40. Available from: https://avantipublisher.com/index.php/gjetru/article/view/777

Abstract

 HVAC (Heating, Ventilation and Air-Conditioning) systems for space heating, space cooling and ventilation of buildings consume nearly 40% of the world energy demand and present the least expensive opportunities for reducing the greenhouse gases emission. Fault Detection and Diagnosis (FDD) methods could monitor the operation of various processes and/or components allowing to detect and, if possible, even predict the presence of defects (deviations from normal or expected operation) as well as ideally identify (diagnose) the fault and/or its location, giving instructions for undertaking corrective actions. FDD techniques could be successfully used for managing the predictive maintenance and/or optimizing the energy/economic/environmental performance of HVAC units while assuring the comfort of occupants. This paper examines the current state of the art of the research on the development and implementation of FDD systems when applied to Air-Handling Units (AHUs), the main and most important device of HVAC systems. This paper describes the existing methodologies, approaches and tools for the utilization of FDD techniques, summarizes the most important findings available in current literature in reference to several case studies where FDD systems have been applied with reference to AHUs and indicates the main gaps to be further investigated.
https://doi.org/10.15377/2409-5818.2019.06.3
PDF

References

United Nations Environment Programme. Buildings and climate change: summary for decision-makers, New York - USA 2009.

Layke J, Mackres E, Liu S, Aden N, Becqué R, Graham P, et al. Accelerating building efficiency: eight actions for urban leaders, World Resources Institute 2016. https://www.wri.org/ publication/accelerating-building-efficiency-actionscityleaders.

Dexter AL, Ngo D. Fault diagnosis in air-conditioning system: a multi-step fuzzy model-based approach, International Journal of Heating, Ventilation, Air-Conditioning and Refrigerating Research 2001. https://doi.org/10.1080/10789669.2001.10391431

Ngo D, Dexter AL. A robust model-based approach to diagnosis faults in air-handling units. ASHRAE Trans 2001; 105(1).

Yu Y, Woradechjumroen D, Yu D. A review of fault detection and diagnosis methodologies on air-handling units. Energy and Buildings 2014; 82: 550-562. https://doi.org/10.1016/j.enbuild.2014.06.042

Rasmussen J. Diagnostic reasoning in action. IEEE Trans Syst Man Cybern 1993; 23: 981-92. https://doi.org/10.1109/21.247883

Struss P, Malik A, Sachenbacher M. Qualitative modeling is the key to automated diagnosis. IFAC proceedings Volumes 1996; 6365-70. https://doi.org/10.1016/S1474-6670(17)58702-9

Himmelblau DM. Fault detection and diagnosis in chemical and petrochemical processes. American Institute of Chemical Engineers 1978.

Hakahara N. Building optimization. definition and concept. Laboratory of heating and ventilation 1993; 42-6.

Isermann R. Process fault detection based on modeling and estimation methods. A survey. Great Britain Pergamon Press 1984; 387-404. https://doi.org/10.1016/0005-1098(84)90098-0

Granderson J, Singla R. Chatacterization and survey of automated fault detection and diagnostic tools, Lawrence Berkeley National Laboratory. Energy Technology Area 2017.

Shi Z, O’Brien W. Development and implementation of automated fault detection and diagnosis for building systems: a review. Automation in Construction 2019; 104: 2015-29. https://doi.org/10.1016/j.autcon.2019.04.002

Liu M. Improving building energy system performance by continuous commissioning. Energy Eng 1999; 96: 46-56. https://doi.org/10.1080/01998595.1999.10530472

Katipamula S, Brambley M. Review article: methods for fault detection, diagnostics, and prognostics for building systems - a review. part II, HVAC&R 2005; 11. https://doi.org/10.1080/10789669.2005.10391133

Rogers AP, Guo F, Rasmussen BP. A review of fault detection and diagnosis methods for residential air conditioning system. Building and Environment 2019; 161. https://doi.org/10.1016/j.buildenv.2019.106236

IEA Annex 25, Real time simulation of HVAC system for building optimization, fault detection and diagnosis, ed. Hyvarien J, Karki S. Techical Research Centre of Finland 1996.

IEA Annex 34, Demonstrating automated fault detection and diagnosis methods in real buildings, ed. Dexter A. Technical Research Centre of Finland 2001.

Shoureshi R, McLaughlin K. Microprocessor-based failure detection of heat pumps. IFAC Proceedings Volumes 1985; 155-160. https://doi.org/10.1016/B978-0-08-033473-8.50031-0

Usoro PB, Schick IC, Negahdaripour S. HVAC system fault detection and diagnosis. American Control Conference 1985; 606-612.

Liddament MW. Technical synthesis report: real time simulation of HVAC systems for building optimisation, fault detection and diagnostics. ESSU 1999.

Jagpal R. Computer aided evaluation of HVAC system performance: technical synthesis report. International Energy Agency 2006.

Katipamula S, Brambley M. Review article: methods for fault detection, diagnostics, and prognostics for building systems - a review. part I. HVAC&R 2005; 11. https://doi.org/10.1080/10789669.2005.10391123

Ding SX. Model-based fault diagnosis techniques: design schemes, algorithms, and tools, Springer 2008.

Isermann R. Fault-diagnosis applications: model-based condition monitoring: actuators, drives, machinery, plants, sensors, and fault-tolerant systems, Springer 2011. https://doi.org/10.1007/978-3-642-12767-0_12

De Kleer J, Williams BC. Diagnosing multiple faults. Artif Intell 1987; 32: 97-130. https://doi.org/10.1016/0004-3702(87)90063-4

Venkatasubramanian V, Rengaswamy R. A review of process fault detection and diagnosis part I: quantitative model-based methods. Comput Chem Eng 2003; 27: 293- 311. https://doi.org/10.1016/S0098-1354(02)00160-6

Isermann R. Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer 2006. https://doi.org/10.1007/3-540-30368-5

Isermann R. Fault diagnosis of machines via parameter estimation and knowledge processing-tutorial paper. Automatica 1993; 29: 815-835. https://doi.org/10.1016/0005-1098(93)90088-B

Gunay B, Shen W, Yang C. Characterization of a building's operation using automation data: a review and case study. Build Environ 2017; 118: 196-210. https://doi.org/10.1016/j.buildenv.2017.03.035

Brambley M, Haves P, Torcellini P, Hansen D. Advanced sensors and controls for building applications: market assessment and potential. R & D Pathways 2005. https://doi.org/10.2172/859997

Capehart BL, Brambley MR. Automated diagnostics and analytics for buildings, 1st ed. Fairmont Press 2014.

Du Z, Fan B, Chi J, Jin X. Sensor fault detection and its efficiency analysis in air handling unit using the combined neural networks. Energ Buildings 2014; 72: 157-66. https://doi.org/10.1016/j.enbuild.2013.12.038

Dey M, Rana SP, Dudley S. Smart building creation in large scale HVAC environments through automated fault detection and diagnosis. Future Generation Computer System 2018. https://doi.org/10.1016/j.future.2018.02.019

Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction, 2nd ed. Springer: New York 2008.

Kim W, Katipamula S. A review of fault detection and diagnostics methods for building systems. Sci Technol Built Environ 2017; 24: 1-19. https://doi.org/10.1080/23744731.2017.1318008

Venkatasubramanian V, Rengaswamy R, Kavuri SN. A review of process fault detection and diagnosis: part II: qualitative models and search strategies. Comput Chem Eng 2003; 27: 313-26. https://doi.org/10.1016/S0098-1354(02)00161-8

O’Neill Z, Pang X, Shashanka M, Haves P, Bailey T. Modelbased real-time whole building energy performance monitoring and diagnostics. J Build Perform Simul 2014; 7: 83-99. https://doi.org/10.1080/19401493.2013.777118

Ham Y, Golparvar-Fard M. EPAR: energy performance augmented reality models for identification of building energy performance deviations between actual measurements and simulation results. Energ Buildings 2013; 63: 15-28. https://doi.org/10.1016/j.enbuild.2013.02.054

Shi Z, O’Brien W, Gunay HB. Building zone fault detection with Kalman filter based methods, ESim 2016 - Building Simulation to Support Building Sustainability 2016.

Rossi TM, Braun JE. A statistical, rule-based fault detection and diagnostic method for vapor compression air conditioners. HVAC&R 1997; 3: 19-37. https://doi.org/10.1080/10789669.1997.10391359

Sulaiman NA, Othman MF, Abdullah H. Fuzzy logic control and fault detection in centralized chilled water system. IEEE Symposium Series on Computational Intelligence 2015: 8-13. https://doi.org/10.1109/SSCI.2015.265

Žàčekovà E, Vàňa Z, Cigler J. Towards the real-life implementation of MPC for an office building: Identification issues. Applied Energy 2014; 135: 53-62. https://doi.org/10.1016/j.apenergy.2014.08.004

Afraz A, Janabi-Sharifi F. Supervisory model predictive controller (MPC) for residential HVAC systems: implementation and experimentation on archetype sustainable house in Toronto. Energy and Buildings 2017; 154: 268-82. https://doi.org/10.1016/j.enbuild.2017.08.060

Fornera L, Glass AS, Grumber P, Todtli J. Qualitative fault detection based on logical programming applied to a variable air volume air handling unit. Control Engineering Practice 1996; 1(4): 105-16. https://doi.org/10.1016/0967-0661(95)00213-9

Glass AS, Gruber P, Todtli J. Qualitative approaches to fault detection and diagnosis. Building Services Engineering research and Technology 1996; 17(3): 24-27.

House JM, Lee WY, Dong RS. Classification technique for fault detection and diagnosis of and air-handling unit. ASHRAE Trans 1999; 105.

Norford LK, Wright JA, Buswell RA, Luo D, Klaasses CJ, et al, Demonstration of fault detection and diagnosis methods for air-handling units. HVAC&R Research 2001; 8(1): 41-71. https://doi.org/10.1080/10789669.2002.10391289

House JM, Vaezi-Nejad H, Whitcomb JM. An expert rule set for fault detection in air-handling units. ASHRAE Trans 2006.

Schein J, Bushby ST, Castro NS, House JM. A rule fault detection method for air handling units. Energy and Buildings 2006; 38. https://doi.org/10.1016/j.enbuild.2006.04.014

Sterling R, Provan G, Febres J, O’Sullivan D, et al., Modelbased fault detection and diagnosis of air handling units: a comparison of methodologies. Energy and Buildings 2014; 62. https://doi.org/10.1016/j.egypro.2014.12.432

Liang Y, Meng Q, Chang S. Fault diagnosis and consumption analysis for variavle air valume air conditioning system: a case study. Heating, Ventilation and Air Conditioning International Symposium 2017. https://doi.org/10.1016/j.proeng.2017.10.021

Deshmukh S, Samouhos S, Glicksman L, Norford L. Fault detection in commercial building VAV AHU: A case study of an academic building. Energy and Buildings 2019; 163-73. https://doi.org/10.1016/j.enbuild.2019.06.051

Shiozaki J, Miyasaka F. A fault diagnosis tool for HVAC system using qualitative reasoning algorithm. Building Simulation 1999.

Morisot O, Marchio D. Fault detection and diagnosis on HVAC variable air volume system using artificial neural network. Building Simulation 1999.

Lee WY, House JM, Kyong NH. Subsystem level fault diagnosis of a building’s air-handling unit using general regression neural networks. Applied Energy 2004; 77. https://doi.org/10.1016/S0306-2619(03)00107-7

Du Z, Jin X. Multiple faults diagnosis for sensors in air handling unit using Fisher discriminant analysis. Energy Conversion and Management 2008; 49. https://doi.org/10.1016/j.enconman.2008.06.032

Gruber P, Kaldorf S. Performance audit tool PAT: an expert system for the detection and diagnosis of building underperformance. ASHRAE Trans 2001.

Shi Z, O’Brien W, Gunay HB. Development of a distributed building fault detection, diagnostic, and evaluation system. ASHRAE Trans 2018; 124: 23-37.

Sun L, Li Y, Jia H, Ying Y. Research on fault detection method for air handling units system. Energy and Buildings 2019; 163-73.

Simulink, “Simulation and Model-Based Design”. [Online]. Available: http://www.mathworks.com.

MatLab, “Simulation and Model-Based Design”. [Online]. Available: http://www.mathworks.com.

PROLOG, “Prolog for the real world”. [Online]. Available: http://www.swi-prolog.org.

TRNSYS, “The transient energy system simulation tool.” [Online]. Available: http://www.trnsys.com.

EnergyPlus, “Energy Simulation Software for Buildings.” [Online]. Available: http://www.energyplus.net.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2019 F. Guarino, V. Filomena, L. Maffei, S. Sibilio, A. Rosato