Open Science Research Excellence

Open Science Index

Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29414

Select areas to restrict search in scientific publication database:
Performance Monitoring of the Refrigeration System with Minimum Set of Sensors
This paper describes a methodology for remote performance monitoring of retail refrigeration systems. The proposed framework starts with monitoring of the whole refrigeration circuit which allows detecting deviations from expected behavior caused by various faults and degradations. The subsequent diagnostics methods drill down deeper in the equipment hierarchy to more specifically determine root causes. An important feature of the proposed concept is that it does not require any additional sensors, and thus, the performance monitoring solution can be deployed at a low installation cost. Moreover only a minimum of contextual information is required, which also substantially reduces time and cost of the deployment process.
Digital Object Identifier (DOI):


[1] EPA, Supermarket Energy Use Profile, Environmental Protection Agency, 2007.
[2] Dan W. Taylor, David W. Corne, David L. Taylor, and Jack Harkness, Predicting Alarms in Supermarket Refrigeration Systems Using Evolved Neural Networks and Evolved Rulesets, In the Proceedings of the World Congress on Computational Intelligence (WCCI-2002), IEEE Press, Honolulu, Hawaii, , 2002, pp. 1988-1993.
[3] Dan W. Taylor and David W. Corne, Refrigerant Leak Prediction in Supermarkets Using Evolved Neural Networks, In the Proceedings of the 4th Asia Pacific Conference on Simulated Evolution and Learning (SEAL), Singapore, 2002.
[4] Abtar Singh, Paul Wickberg, Thomas J Mathews, and Neal Starling, System for remote refrigeration monitoring and diagnostics, US Patent 7644591, 2010.
[5] William S. Cleveland, Robust Locally Weighted Regression and Smoothing Scatterplots, Journal of the American Statistical Association, vol. 74, pp. 829-836, 1979.
[6] William S. Cleveland and Susan J. Devlin, Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting, Journal of the American Statistical Association, vol. 83, pp. 596-610, 1988.
[7] Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning, Second Edition: Data Mining, Inference, and Prediction, 2nd ed.: Springer, 2009.
[8] K. Marik, Z. Schindler, and P. Stluka, Decision support tools for advanced energy management, Energy, vol. 33, pp. 858-873, 2008.
[9] Clive R. Loader, Bandwidth selection: classical or plug-in?, The Annals of Statistics, vol. 27, pp. 415-438, 1999.
[10] Geoffrey S. Watson, Smooth regression analysis, Sankhya: The Indian Journal of Statistic, vol. 26, pp. 359-372, 1964.
[11] E. A. Nadaraya, On estimating regression, Theory of Probability and its Applications, vol. 9, pp. 141-142, 1964.
[12] J. Kukal, K. Macek, J. Rojicek, and J. Trojanova, From Symptoms to Faults: Temporal Reasoning Methods, In the Proc. 2009 Int. Conference on Adaptive and Intelligent Systems, Klagenfurt, Austria, 2009.
[13] Danfoss. (2009) Why Compressors Fail - Liquid Slugging.
[Online]. 17/Article/LiquidSlugging/Why Compressors Fail Part 3-web.pdf.
[14] Dan W Taylor and David W Corne, An Investigation of the Negative Selection Algorithm for Fault Detection in Refrigeration Systems, In the Proceeding of Second International Conference on Artificial Immune Systems (ICARIS), Edinburgh, UK, 2003, pp. 34-45.
[15] D. Ruppert and M.P.Wand, Multivariate Locally Weighted Least Square Regression, The Annals of Statistics, vol. 22, pp. 1346-1370, 1994.
Vol:13 No:03 2019Vol:13 No:02 2019Vol:13 No:01 2019
Vol:12 No:12 2018Vol:12 No:11 2018Vol:12 No:10 2018Vol:12 No:09 2018Vol:12 No:08 2018Vol:12 No:07 2018Vol:12 No:06 2018Vol:12 No:05 2018Vol:12 No:04 2018Vol:12 No:03 2018Vol:12 No:02 2018Vol:12 No:01 2018
Vol:11 No:12 2017Vol:11 No:11 2017Vol:11 No:10 2017Vol:11 No:09 2017Vol:11 No:08 2017Vol:11 No:07 2017Vol:11 No:06 2017Vol:11 No:05 2017Vol:11 No:04 2017Vol:11 No:03 2017Vol:11 No:02 2017Vol:11 No:01 2017
Vol:10 No:12 2016Vol:10 No:11 2016Vol:10 No:10 2016Vol:10 No:09 2016Vol:10 No:08 2016Vol:10 No:07 2016Vol:10 No:06 2016Vol:10 No:05 2016Vol:10 No:04 2016Vol:10 No:03 2016Vol:10 No:02 2016Vol:10 No:01 2016
Vol:9 No:12 2015Vol:9 No:11 2015Vol:9 No:10 2015Vol:9 No:09 2015Vol:9 No:08 2015Vol:9 No:07 2015Vol:9 No:06 2015Vol:9 No:05 2015Vol:9 No:04 2015Vol:9 No:03 2015Vol:9 No:02 2015Vol:9 No:01 2015
Vol:8 No:12 2014Vol:8 No:11 2014Vol:8 No:10 2014Vol:8 No:09 2014Vol:8 No:08 2014Vol:8 No:07 2014Vol:8 No:06 2014Vol:8 No:05 2014Vol:8 No:04 2014Vol:8 No:03 2014Vol:8 No:02 2014Vol:8 No:01 2014
Vol:7 No:12 2013Vol:7 No:11 2013Vol:7 No:10 2013Vol:7 No:09 2013Vol:7 No:08 2013Vol:7 No:07 2013Vol:7 No:06 2013Vol:7 No:05 2013Vol:7 No:04 2013Vol:7 No:03 2013Vol:7 No:02 2013Vol:7 No:01 2013
Vol:6 No:12 2012Vol:6 No:11 2012Vol:6 No:10 2012Vol:6 No:09 2012Vol:6 No:08 2012Vol:6 No:07 2012Vol:6 No:06 2012Vol:6 No:05 2012Vol:6 No:04 2012Vol:6 No:03 2012Vol:6 No:02 2012Vol:6 No:01 2012
Vol:5 No:12 2011Vol:5 No:11 2011Vol:5 No:10 2011Vol:5 No:09 2011Vol:5 No:08 2011Vol:5 No:07 2011Vol:5 No:06 2011Vol:5 No:05 2011Vol:5 No:04 2011Vol:5 No:03 2011Vol:5 No:02 2011Vol:5 No:01 2011
Vol:4 No:12 2010Vol:4 No:11 2010Vol:4 No:10 2010Vol:4 No:09 2010Vol:4 No:08 2010Vol:4 No:07 2010Vol:4 No:06 2010Vol:4 No:05 2010Vol:4 No:04 2010Vol:4 No:03 2010Vol:4 No:02 2010Vol:4 No:01 2010
Vol:3 No:12 2009Vol:3 No:11 2009Vol:3 No:10 2009Vol:3 No:09 2009Vol:3 No:08 2009Vol:3 No:07 2009Vol:3 No:06 2009Vol:3 No:05 2009Vol:3 No:04 2009Vol:3 No:03 2009Vol:3 No:02 2009Vol:3 No:01 2009
Vol:2 No:12 2008Vol:2 No:11 2008Vol:2 No:10 2008Vol:2 No:09 2008Vol:2 No:08 2008Vol:2 No:07 2008Vol:2 No:06 2008Vol:2 No:05 2008Vol:2 No:04 2008Vol:2 No:03 2008Vol:2 No:02 2008Vol:2 No:01 2008
Vol:1 No:12 2007Vol:1 No:11 2007Vol:1 No:10 2007Vol:1 No:09 2007Vol:1 No:08 2007Vol:1 No:07 2007Vol:1 No:06 2007Vol:1 No:05 2007Vol:1 No:04 2007Vol:1 No:03 2007Vol:1 No:02 2007Vol:1 No:01 2007