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Faults and Failures in Smart Buildings: A New Tool for Diagnosis

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Towards Energy Smart Homes

Abstract

Building systems are equipped with building automation system, supervisory controllers, and a lot of sensors which make them complex systems. In this context, technical malfunctions can have a huge impact on building operation and occupant’s comfort. To make a resilient building management system, it is important to identify the severity, cause, and type of each fault using fault diagnosis techniques. The first step for a building diagnostic framework is the designing of tests. To make a test, data are required from different sensors. Due to a battery problem, sensors yield gaps. The delay between two data sent depends on the measured value and the type of sensor and the question that arises is from which delay, a sensor becomes faulty? This is a challenge. In a building system, there is no universal test, but there are contextual tests with limited validity. These local contexts are measured with potentially faulty sensors, and the problem is how to conclude about a test that can be valid or not knowing that validity can only be tested with possibly faulty sensors? This is also a complex problem to solve.

A test is characterized by thresholds i.e. the behavioral constraints which are either satisfied or unsatisfied. Uncertainty is related to the validity constraints. Indeed, it is difficult to set a threshold for the level of validity from which we can conclude if a test is valid or not. In this work, the proposed methodology of diagnosis comprises the diagnosis from the first principle because it allows us to determine the minimum diagnoses with explanation at component level. Moreover, the diagnostic results are calculated from a set of tests, each one is defined by its level of validity and the problem is how to conclude in terms of diagnosis and how to take into account the level of validity in the diagnosis?

The objective of this work is to highlight these challenges as well as to provide a strategy about how to solve them. A real application has been studied for validation: a platform at the University of Southern Denmark.

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References

  1. B. Akinci, J. Garrett, Ö. Akin, Identification of functional requirements and possible approaches for self-configuring intelligent building systems. Final Report Submitted to Steven Bushby, NIST, 100, 20899–8631 (2011)

    Google Scholar 

  2. M. Amayri, A. Arora, S. Ploix, S. Bandhyopadyay, Q.-D. Ngo, V.R. Badarla, Estimating occupancy in heterogeneous sensor environment. Energy Build. 129, 46–58 (2016)

    Article  Google Scholar 

  3. K.K. Andersen, T.A. Reddy, The error in variables (EIV) regression approach as a means of identifying unbiased physical parameter estimates: application to chiller performance data. HVAC&R Res. 8(3), 295–309 (2002)

    Article  Google Scholar 

  4. M.M. Ardehali, T.F. Smith, Literature review to identify existing case studies of controls related energy inefficiencies in buildings. Prepared for the National Building Controls Information Program. Technical Report: ME-TFS-01-007. Iowa City, Iowa. Department of Mechanical and Industrial Engineering, the University of Iowa (2002)

    Google Scholar 

  5. A. Atkinson, Human error in the management of building projects. Construct. Manage. Econ. 16(3), 339–349 (1998)

    Article  Google Scholar 

  6. M. Bonvini, M.A. Piette, M. Wetter, J. Granderson, M.D. Sohn, Bridging the gap between simulation and the real world an application to FDD, in Proceedings of 2014 ACEE Summer Study (2014)

    Google Scholar 

  7. J.D. Bynum, D.E. Claridge, J.M. Curtin, Development and testing of an automated building commissioning analysis tool (ABCAT). Energy Build. 55, 607–617 (2012)

    Article  Google Scholar 

  8. D.J. Campbell, Task complexity: a review and analysis. Acad. Manage. Rev. 13(1), 40–52 (1988)

    Article  Google Scholar 

  9. N.S. Castro, Performance evaluation of a reciprocating chiller using experimental data and model predictions for fault detection and diagnosis/discussion. ASHRAE Trans. 108, 889 (2002)

    Google Scholar 

  10. D. Chester, D. Lamb, P. Dhurjati, Rule-based computer alarm analysis in chemical process plants, in Proceedings of the Seventh Annual Conference on Computer Technology (1984), pp. 22–29

    Google Scholar 

  11. J.A. Clarke, J. Cockroft, S. Conner, J.W. Hand, N.J. Kelly, R. Moore, T. O’Brien, P. Strachan, Simulation-assisted control in building energy management systems. Energy Build. 34(9), 933–940 (2002)

    Article  Google Scholar 

  12. A.L. Dexter, D. Ngo, Fault diagnosis in air-conditioning systems: a multi-step fuzzy model-based approach. HVAC&R Res. 7(1), 83–102 (2001)

    Article  Google Scholar 

  13. H. Doukas, K.D. Patlitzianas, K. Iatropoulos, J. Psarras, Intelligent building energy management system using rule sets. Build. Environ. 42(10), 3562–3569 (2007)

    Article  Google Scholar 

  14. Z. Du, X. Jin, L. Wu, Fault detection and diagnosis based on improved PCA with JAA method in VAV systems. Build. Environ. 42(9), 3221–3232 (2007)

    Article  Google Scholar 

  15. Z. Du, X. Jin, Y. Yang, Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network. Appl. Energy 86(9), 1624–1631 (2009)

    Article  Google Scholar 

  16. Z. Du, B. Fan, X. Jin, J. Chi, Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis. Build. Environ. 73, 1–11 (2014)

    Article  Google Scholar 

  17. D. Estrin, L. Girod, G. Pottie, M. Srivastava, Instrumenting the world with wireless sensor networks, in 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 01CH37221) (2001), pp. 2033–2036

    Google Scholar 

  18. H. Friedman, E. Crowe, E. Sibley, M. Effinger, The Building Performance Tracking Handbook (California Commissioning Collaborative, Berkeley, 2011)

    Google Scholar 

  19. C. Ghiaus, Fault diagnosis of air conditioning systems based on qualitative bond graph. Energy Build. 30(3), 221–232 (1999)

    Article  Google Scholar 

  20. A. Giantomassi, F. Ferracuti, S. Iarlori, G. Ippoliti, S. Longhi, Signal based fault detection and diagnosis for rotating electrical machines: issues and solutions, in Complex System Modelling and Control Through Intelligent Soft Computations (Springer, New York, 2015), pp. 275–309

    Google Scholar 

  21. A.S. Glass, P. Gruber, M. Roos, J. Todtli, Qualitative model-based fault detection in air-handling units. IEEE Control Syst. 15(4), 11–22 (1995)

    Article  Google Scholar 

  22. Health and Safety Commission, ACSNI study group on human factors (1993)

    Google Scholar 

  23. E.J. Henley, Application of expert systems to fault diagnosis (1984)

    Google Scholar 

  24. A. Hyvärinen, E. Oja, Simple neuron models for independent component analysis. Int. J. Neural Syst. 7(06), 671–687 (1996)

    Article  Google Scholar 

  25. B.D. Ilozor, M.I. Okoroh, C.E. Egbu et al., Understanding residential house defects in Australia from the state of Victoria. Build. Environ. 39(3), 327–337 (2004)

    Article  Google Scholar 

  26. Y. Jia, T.A. Reddy, Characteristic physical parameter approach to modeling chillers suitable for fault detection, diagnosis and evaluation. J. Solar Energy Eng. 125(3), 258–265 (2003)

    Article  Google Scholar 

  27. S. Katipamula, M.R. Brambley, Methods for fault detection, diagnostics, and prognostics for building systems-a review, part I. HVAC&R Res. 11(1), 3–25 (2005)

    Article  Google Scholar 

  28. S. Katipamula, R.G. Pratt, D.P. Chassin, Z.T. Taylor, K. Gowri, M.R. Brambley, Automated fault detection and diagnostics for outdoor-air ventilation systems and economizers: methodology and results from field testing. Trans.-Am. Soc. Heat. Refrig. Air Condition. Eng. 105, 555–567 (1999)

    Google Scholar 

  29. S. Katipamula, M.R. Brambley, N. Bauman, R.G. Pratt, Enhancing building operations through automated diagnostics: field test results (2003)

    Google Scholar 

  30. W.-Y. Lee, J.M. House, N.-H. Kyong, Subsystem level fault diagnosis of a building’s air-handling unit using general regression neural networks. Appl. Energy 77(2), 153–170 (2004)

    Article  Google Scholar 

  31. N.G. Leveson, Software engineering: stretching the limits of complexity. Commun. ACM 40(2), 129–132 (1997)

    Article  Google Scholar 

  32. S. Li, J. Wen, A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform. Energy Build. 68, 63–71 (2014)

    Article  Google Scholar 

  33. X. Li, D. Huang, Z. Sun, A routing protocol for balancing energy consumption in heterogeneous wireless sensor networks, in International Conference on Mobile Ad-Hoc and Sensor Networks (Springer, New York, 2007), pp. 79–88

    Google Scholar 

  34. M.-D. Ma, D.S.-H. Wong, S.-S. Jang, S.-T. Tseng, Fault detection based on statistical multivariate analysis and microarray visualization. IEEE Trans. Ind. Inf. 6(1), 18–24 (2010)

    Article  Google Scholar 

  35. P. May-Ostendorp, G.P. Henze, C.D. Corbin, B. Rajagopalan, C. Felsmann, Model-predictive control of mixed-mode buildings with rule extraction. Build. Environ. 46(2), 428–437 (2011)

    Article  Google Scholar 

  36. J.D. McKeen, T. Guimaraes, J.C. Wetherbe, The relationship between user participation and user satisfaction: an investigation of four contingency factors. MIS Quart. 18(4), 427–451 (1994)

    Article  Google Scholar 

  37. M.H. Meyer, K.F. Curley, An applied framework for classifying the complexity of knowledge-based systems. Mis Quart. 15, 455–472 (1991)

    Article  Google Scholar 

  38. J. Mickens, M. Szummer, D. Narayanan, Snitch: interactive decision trees for troubleshooting misconfigurations, in Proceedings of the 2007 Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (2007)

    Google Scholar 

  39. P.-D. Moroşan, R. Bourdais, D. Dumur, J. Buisson, Building temperature regulation using a distributed model predictive control. Energy Build. 42(9), 1445–1452 (2010)

    Article  Google Scholar 

  40. S.A. Mostafa, M.S. Ahmad, M.A. Mohammed, O.I. Obaid, Implementing an expert diagnostic assistance system for car failure and malfunction. Int. J. Comput. Sci. Iss. 9(2), 1 (2012)

    Google Scholar 

  41. H. Najeh, Diagnostic du système bâtiment: nouveaux défis. PhD thesis (2019)

    Google Scholar 

  42. H. Najeh, M.P. Singh, S. Ploix, K. Chabir, M.N. Abdelkrim, Automatic thresholding for sensor data gap detection using statistical approach, in Sustainability in Energy and Buildings (Springer, New York, 2020), pp. 455–467

    Google Scholar 

  43. K. Niida, S. Kobayashi, T. Umeda, J. Itoh, A. Ichikawa, Some expert system experiments in process engineering. Chem. Eng. Res. Des. 64, 372–380 (1986)

    Google Scholar 

  44. L.K. Norford, J.A. Wright, R.A. Buswell, D. Luo, C.J. Klaassen, A. Suby, Demonstration of fault detection and diagnosis methods for air-handling units. HVAC&R Res. 8(1), 41–71 (2002)

    Article  Google Scholar 

  45. S. Ploix, M. Désinde, S. Touaf, Automatic design of detection tests in complex dynamic systems. IFAC Proc. Vol. 38(1), 478–483 (2005)

    Article  Google Scholar 

  46. P.M.A. Ribbers, K.-C. Schoo, Program management and complexity of ERP implementations. Eng. Manage. J. 14(2), 45–52 (2002)

    Article  Google Scholar 

  47. S. Roels, P. Bacher, G. Bauwens, H. Madsen, M.J. Jiménez, Characterising the actual thermal performance of buildings: current results of common exercises performed in the framework of the IEA EBC annex 58-project. Energy Proc. 78, 3282–3287 (2015)

    Article  Google Scholar 

  48. R.S. Rollings, M.P. Rollings, Pavement failures: oversights, omissions and wishful thinking. J. Perform. Construct. Fac. 5(4), 271–286 (1991)

    Article  Google Scholar 

  49. K.W. Roth, D. Westphalen, M.Y. Feng, P. Llana, L. Quartararo, Energy impact of commercial building controls and performance diagnostics: market characterization, energy impact of building faults and energy savings potential. Prepared by TAIX LLC for the US Department of Energy. November. 412 pp (Table 2–1) (2005)

    Google Scholar 

  50. J. Schein, S.T. Bushby, N.S. Castro, J.M. House. A rule-based fault detection method for air handling units. Energy Build. 38(12), 1485–1492 (2006)

    Article  Google Scholar 

  51. G. Simkin, J. Ingham, Digital buildings: using sensors to monitor the performance of concrete buildings during the Christchurch earthquake rebuild, in Concrete Innovation Conference (2014)

    Google Scholar 

  52. M. Singh, Improving Building Operational Performance with Reactive Management Embedding Diagnosis Capabilities. PhD Thesis, University of Grenoble Alpes (2017)

    Google Scholar 

  53. M. Singh, N.T. Kien, H. Najeh, S. Ploix, A. Caucheteux, Advancing building fault diagnosis using the concept of contextual and heterogeneous test. Energies 12(13), 2510 (2019)

    Google Scholar 

  54. M. Singh, M. Jradi, H.R. Shaker, Monitoring and evaluation of building ventilation system fans operation using performance curves. Energy Built Environ. 1(3), 307–318 (2020)

    Article  Google Scholar 

  55. H.R. Shaker, N. Mohamed, S. Lazarova-Molnar, Fault detection and diagnosis for smart buildings: state of the art, trends and challenges, in 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC) (2016), pp. 1–7

    Google Scholar 

  56. J. Stein, M.M. Hydeman, Development and testing of the characteristic curve fan model, in ASHRAE Transactions, vol. 110 (2004), pp. 347–356

    Google Scholar 

  57. S. Treado, Y. Chen, Saving building energy through advanced control strategies. Energies 6(9), 4769–4785 (2013)

    Article  Google Scholar 

  58. USDOE, Commercial energy end-use splits, by fuel type (Quadrillion Btu), in Building Energy Data Book (CTCN, Copenhagen, 2010)

    Google Scholar 

  59. S. Wang, Y. Chen, Fault-tolerant control for outdoor ventilation air flow rate in buildings based on neural network. Build. Environ. 37(7), 691–704 (2002)

    Article  Google Scholar 

  60. T.M. Williams, The need for new paradigms for complex projects. Int. J. Project Manage. 17(5), 269–273 (1999)

    Article  Google Scholar 

  61. R.E. Wood, Task complexity: definition of the construct. Organ. Behav. Hum. Decis. Process. 37(1), 60–82 (1986)

    Article  Google Scholar 

  62. B. Yu, D.H.C. Van Paassen, S. Riahy, General modeling for model-based FDD on building HVAC system. Simul. Pract. Theor. 9(6–8), 387–397 (2002)

    Article  MATH  Google Scholar 

  63. D. Yu, H. Li, M. Yang, A virtual supply airflow rate meter for rooftop air-conditioning units. Build. Environ. 46(6), 1292–1302 (2011)

    Article  MathSciNet  Google Scholar 

  64. Y. Zhang, C. Bingham, M. Gallimore et al., Fault detection and diagnosis based on extensions of PCA. Adv. Milit. Technol. 8(2), 27–41 (2013)

    Google Scholar 

  65. Y. Zhu, X. Jin, Z. Du, Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal. Energy Build. 44, 7–16 (2012)

    Article  Google Scholar 

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Acknowledgements

This work is supported by the French National Research Agency in the framework of the “Investissements d’avenir” Eco SESA program (ANR-15-IDEX-02) and by the ADEME in the framework of the COMEPOS project.

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Authors and Affiliations

  1. G-SCOP lab, Grenoble Institute of Technology, University of Grenoble Alps, CNRS UMR5272, Grenoble, France

    Houda Najeh & Stephane Ploix

  2. Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany

    Mahendra Pratap Singh

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  1. Houda Najeh
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  2. Mahendra Pratap Singh
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  3. Stephane Ploix
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Correspondence to Houda Najeh .

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Editors and Affiliations

  1. G-SCOP lab, Grenoble Institute of Technology University of Grenoble Alps, Grenoble, France

    Stephane Ploix

  2. CNRS, Grenoble, France

    Manar Amayri

  3. CIISE, Concordia University, Montreal, QC, Canada

    Nizar Bouguila

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Najeh, H., Singh, M.P., Ploix, S. (2021). Faults and Failures in Smart Buildings: A New Tool for Diagnosis. In: Ploix, S., Amayri, M., Bouguila, N. (eds) Towards Energy Smart Homes. Springer, Cham. https://doi.org/10.1007/978-3-030-76477-7_14

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  • DOI: https://doi.org/10.1007/978-3-030-76477-7_14

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