Dynamic model-based feature extraction for fault detection and diagnosis of a supermarket refrigeration system^†

References

• BehFar, A., D. Yuill, and Y. Yu. 2018. Supermarket system characteristics and operating faults (RP-1615). Science and Technology for the Built Environment 24 (10):1104–13. 10.1080/23744731.2018.1479614
   Web of Science ®Google Scholar
• BehFar, A., D. Yuill, and Y. Yu. 2017. Automated fault detection and diagnosis methods for supermarket equipment (RP-1615). Science and Technology for the Built Environment 23 (8):1253–66. 10.1080/23744731.2017.1333352
   Web of Science ®Google Scholar
• BehFar, A., D. Yuill, and Y. Yu. 2019. Automated fault detection and diagnosis methods for supermarkets – Method selection, replication, and applicability. Energy and Buildings 198:520–7. 10.1016/j.enbuild.2019.06.011
   Web of Science ®Google Scholar
• Beshr, M., V. Aute, V. Sharma, O. Abdelaziz, B. Fricke, and R. Radermacher. 2015. A comparative study on the environmental impact of supermarket refrigeration systems using low GWP refrigerants. International Journal of Refrigeration 56:154–64. volume10.1016/j.ijrefrig.2015.03.025
   Web of Science ®Google Scholar
• Bourdhouxhe, J. P., M. Grodent, and J. Lebrun. 1999. HVACTOOLKIT: A toolkit for primary HVAC system energy calculation. Atlanta, GA: ASHRAE.
   Google Scholar
• Dai, B., H. Yang, S. Liu, C. Liu, T. Wu, J. Li, J. Zhao, and V. Nian. 2022. Hybrid solar energy and waste heat driving absorption subcooling supermarket CO2 refrigeration system: Energetic, carbon emission and economic evaluation in China. Solar Energy 247:123–45. volume10.1016/j.solener.2022.10.009
   Web of Science ®Google Scholar
• Eilperin, J., and D. Butler. 2021. “There’s an invisible climate threat seeping from grocery store freezers. Biden wants to change that,” The Washington Post, February 15. https://www.washingtonpost.com/climate-environment/2021/02/15/these-gases-your-grocerys-freezer-are-fueling-climate-change-biden-wants-fix-that/
   Google Scholar
• Hassanpour, H., P. Mhaskar, J. M. House, and T. I. Salsbury. 2020. A hybrid modeling approach integrating first-principles knowledge with statistical methods for fault detection in HVAC systems. Computers & Chemical Engineering 142:107022. 10.1016/j.compchemeng.2020.107022
   Web of Science ®Google Scholar
• Kim, M. H., and C. W. Bullard. 2002. Thermal performance analysis of small hermetic refrigeration and air-conditioning compressor. JSME International Journal of Series B 45 (4):857–64.
   Google Scholar
• Lee, W. Y., J. House, and N. H. Kyong. 2004. Subsystem level fault diagnosis of a building’s air-handling unit using general regression neural networks. Applied Energy 77 (2):153–70. 10.1016/S0306-2619(03)00107-7
   Web of Science ®Google Scholar
• Li, W., H. Yue, S. Valle-Cervantes, and S. Qin. 2000. Recursive PCA for adaptive process monitoring. Journal of Process Control 10 (5):471–86. 10.1016/S0959-1524(00)00022-6
   Web of Science ®Google Scholar
• Li, Y., J. Sun, B. Fricke, P. Im, and T. Kuruganti. 2022. Grey-box fault models and applications for low carbon emission CO2 refrigeration system. International Journal of Refrigeration 141:76–89. 10.1016/j.ijrefrig.2022.06.003
   Google Scholar
• Liu, X., K. Yu, X. Wan, and X. Li. 2021. Performance evaluation of CO2 supermarket refrigeration system with multi-ejector and dedicated mechanical subcooling. Energy Reports 7:5214–27. volume10.1016/j.egyr.2021.08.110
   Web of Science ®Google Scholar
• Makhnatch, P., A. M. Babiloni, J. Rogstam, and R. Khodabandeh. 2017. Retrofit of lower GWP alternative R449A into an existing R404A indirect supermarket refrigeration system. International Journal of Refrigeration 76:184–92. volume10.1016/j.ijrefrig.2017.02.009
   Web of Science ®Google Scholar
• Navigant Consulting. 2009. Energy savings potential and R&D opportunities for commercial refrigeration.
   Google Scholar
• Rengaswamy, R., T. Hagglund, and V. Venkatasubramanian. 2001. A qualitative shape analysis formalism for monitoring control loop performance. Engineering Applications of Artificial Intelligence 14 (1):23–33. 10.1016/S0952-1976(00)00051-8
   Web of Science ®Google Scholar
• Rengaswamy, R., and V. Venkatasubramanian. 2000. A fast training neural network and its updation for incipient fault detection and diagnosis. Computers & Chemical Engineering 24 (2–7):431–7. 10.1016/S0098-1354(00)00434-8
   Web of Science ®Google Scholar
• Salehy, Y., H. M. Hoang, F. Cluzel, Y. Leroy, A. Delahaye, L. Fournaison, and B. Yannou. 2020. Energy performances assessment for sustainable design recommendations: Case study of a supermarket’s refrigeration system. Procedia CIRP 90:328–33. volume10.1016/j.procir.2020.01.102
   Google Scholar
• Scenna, N. J. 2000. Some aspects of fault diagnosis in batch processes. Reliability Engineering & System Safety 70 (1):95–110. 10.1016/S0951-8320(00)00049-1
   Web of Science ®Google Scholar
• Shaw, S. R., L. K. Norford, D. Luo, and S. B. Leeb. 2002. Detection and diagnosis of HVAC faults via electrical load monitoring. HVAC&R Research 8 (1):13–40. 10.1080/10789669.2002.10391288
   Web of Science ®Google Scholar
• Sun, J., P. Im, Y. Bae, J. Munk, T. Kuruganti, and B. Fricke. 2021a. Dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics. Scientific Data 8 (1):144. 10.1038/s41597-021-00927-6
   PubMed Web of Science ®Google Scholar
• Sun, J., P. Im, Y. Bae, J. Munk, T. Kuruganti, and B. Fricke. 2021b. Fault detection of low global warming potential refrigerant supermarket refrigeration system: Experimental investigation. Case Studies in Thermal Engineering 26:101200. 10.1016/j.csite.2021.101200
   Web of Science ®Google Scholar
• Sun, J., T. Kuruganti, J. Munk, J. Dong, and B. Cui. 2021c. Low global warming potential (GWP) refrigerant supermarket refrigeration system modeling and its application. International Journal of Refrigeration 126:195–209. 10.1016/j.ijrefrig.2020.12.037
   Google Scholar
• Sun, J., T. Kuruganti, B. Fricke, S. Xuan, Y. Li, W. Wilkerson, and C. Cunningham. 2022. Automated fault detection and diagnosis deployment internet of things solution for building energy system. Journal of Building Engineering 61:105291. 10.1016/j.jobe.2022.105291
   Web of Science ®Google Scholar
• Sun, Z., J. Li, Y. Liang, H. Sun, S. Liu, L. Yang, C. Wang, and B. Dai. 2020. Performance assessment of CO2 supermarket refrigeration system in different climate zones of China. Energy Conversion and Management 208:112572. volume10.1016/j.enconman.2020.112572
   Web of Science ®Google Scholar
• Tarifa, E., and N. Scenna. 1997. Fault diagnosis, directed graphs, and fuzzy logic. Computers & Chemical Engineering 21 (1–2):S649–S654. 10.1016/S0098-1354(97)87576-X
   Google Scholar
• Tsimpoukis, D., E. Syngounas, E. Bellos, M. Koukou, C. Tzivanidis, S. Anagnostatos, and M. Vrachopoulos. 2023. Thermodynamic and economic analysis of a supermarket transcritical CO2 refrigeration system coupled with solar-fed supercritical CO2 Brayton and organic Rankine cycles. Energy Conversion and Management: X 18:100351. 10.1016/j.ecmx.2023.100351
   Google Scholar
• US EPA. 2008. “Facility type: Supermarkets and grocery stores,” Energy Star Building Upgrade Manual. Office of Air and Radiation. http://www.energystar.gov/sites/default/files/buildings/tools/EPA_BUM_CH11_Supermarkets.pdf
   Google Scholar
• Vedam, H., and V. Venkatasubramanian. 1997. A wavelet theory-based adaptive trend analysis system for process monitoring and diagnosis. American control conference, 309–313.
   Google Scholar
• Venkatasubramanian, V., R. Rengaswamy, K. Yin, and S. N. Kavuri. 2003a. A review of process fault detection and diagnosis Part I: Quantitative model-based methods. Computers & Chemical Engineering 27 (3):293–311. 10.1016/S0098-1354(02)00160-6
   Web of Science ®Google Scholar
• Venkatasubramanian, V., R. Rengaswamy, and S. N. Kavuri. 2003b. A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies. Computers & Chemical Engineering 27 (3):313–26. 10.1016/S0098-1354(02)00161-8
   Web of Science ®Google Scholar
• Venkatasubramanian, V., R. Rengaswamy, S. N. Kavuri, and K. Yin. 2003c. A review of process fault detection and diagnosis part III: Process history based methods. Computers & Chemical Engineering 27 (3):327–46. 10.1016/S0098-1354(02)00162-X
   Web of Science ®Google Scholar
• Versteeg, N. K., and W. Malalasekera. 2007. An introduction to computational fluid dynamics: The finite volume method. Harlow, England: Pearson Education.
   Google Scholar
• Wang, X. Z., B. H. Chen, S. H. Yang, and C. Mcgreavy. 1999. Application of wavelets and neural networks to diagnostic system development. 2. An integrated framework and its application. Computers & Chemical Engineering 23 (7):945–54. 10.1016/S0098-1354(99)00260-4
   Web of Science ®Google Scholar
• Yang, Z., K. B. Rasmussen, A. T. Kieu, and R. Izadi-Zamanabadi. 2011a. Fault detection and isolation for a supermarket refrigeration system – Part one: Kalman-Filter-based methods. IFAC Proceedings Volumes 44 (1):13233–8. 10.3182/20110828-6-IT-1002.03115
   Google Scholar
• Yang, Z., K. B. Rasmussen, A. T. Kieu, and R. Izadi-Zamanabadi. 2011b. Fault detection and isolation for a supermarket refrigeration system – Part two: Unknown-input-observer method and its extension. IFAC Proceedings Volumes 44 (1):4238–43. 10.3182/20110828-6-IT-1002.03120
   Google Scholar
• Yoshida, H., and S. Kumar. 1999. ARX and AFMM model-based on-line real-time data base diagnosis of sudden fault in AHU of VAV system. Energy Conversion and Management 40 (11):1191–206. 10.1016/S0196-8904(99)00022-9
   Web of Science ®Google Scholar
• Yu, Y., D. Woradechjumroen, and D. Yu. 2014. A review of fault detection and diagnosis methodologies on air-handling units. Energy and Buildings 82:550–62. 10.1016/j.enbuild.2014.06.042
   Web of Science ®Google Scholar
• Zhu, Y., X. Jin, and Z. Du. 2012. Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal. Energy and Buildings 44:7–16. 10.1016/j.enbuild.2011.09.043
   Web of Science ®Google Scholar