Abstract
Failure of refrigerated cabinets costs millions annually to supermarkets, and a large market exists for systems which can predict such failures. Previous work, now moving towards deployment, has used neural networks to predict volumes of alarms from refrigeration system controllers, and also to predict likely refrigerant gas loss. Here, we use in-cabinet temperature data, aiming to predict faults from the pattern of temperature over time. We argue that artificial immune systems (AIS) are particularly appropriate for this, and report a series of preliminary experiments which investigate parameter and strategy choices. We also investigate a ‘differential’ encoding scheme designed to highlight essential elements of in-cabinet temperature patterns. The results prove feasibility for AIS in this application, with good self-detection rates, and a promising fault-detection rate. The best configuration of those examined seems to be that which uses the novel differential encoding with r-bits matching.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Taylor, D., Corne, D., Taylor, D., Harkness, J.: Predicting Alarms in Supermarket Refrigeration Systems Using Evolved Neural Networks and Evolved Rulesets. In: Proceedings of the World Congress on Computational Intelligence (WCCI-2002), IEEE Press, Los Alamitos (2002)
Taylor, D., Corne, D.: Refrigerant Leak Prediction in Supermarkets Using Evolved Neural Networks. In: Proceedings of the 4th Asia Pacific Conference on Simulated Evolution and Learning SEAL (2002)
de Castro, L.N., Timmis, J.: Artificial Immune Systems: A Novel Paradigm to Pattern Recognition. In: Artificial Neural Networks in Pattern Recognition, University of Paisley (2002)
Dasgupta, D., Attoh-Okine, N.: Immunity-Based Systems: A Survey. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, IEEE Press, Los Alamitos (1997)
Janeway, C.A.: How the Immune System Recognizes Invaders. Scientific American, 41–47 (1993)
Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-Nonself Discrimination in a Computer. In: Proceedings of the IEEE Symposium on Research in Security and Privacy, IEEE Press, Los Alamitos (1994)
Forrest, S., Hofmeyr, S.A., Somayaji, A., Longstaff, T.A.: A Sense of Self for Unix Processes. In: Proceedings of the IEEE Symposium on Research in Security and Privacy, IEEE Press, Los Alamitos (1996)
Kephart, J.O.: A Biologically Inspired Immune System for Computers. In: Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems (1994)
Dasgupta, D.: Using Immunological Principles in Anomaly Detection. In: Proceedings of Artificial Neural Networks in Engineering, ANNIE (1996)
Dasgupta, D., Forrest, S.: Tool Breakage Detection in Milling Operations using a Negative-Selection Algorithm. Technical Report CS95-5, Computer Science, University of New Mexico (1995)
Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data Using Ideas from Immunology. In: Proceedings of The 5th International Conference on Intelligent Systems (1996)
Singh, S.: Anomaly Detection Using Negative Selection Based on the R-Contiguous Matching Rule. In: Proceedings of ICARIS 2002, University of Kent at Canterbury Printing Unit (2002)
Gonzales, F., Dasgupta, D.: An Imunogenic Technique to Detect Anomalies in Network Traffic. In: Proceedings of GECCO 2002, Morgan Kaufmann Publishers, San Francisco (2002)
Gonzales, F., Dasgupta, D.: Neuro-immune and Self-Organising Map Approaches to Anomaly Detection: A Comparison. In: Proceedings of ICARIS 2002, University of Kent at Canterbury Printing Unit (2002)
Timmis, J.: aiVIS - Artificial Immune Network Visualisation. In: EuroGraphics UK 2001 Conference Proceedings (2001)
Kohonen, T.: Self Organising Maps Second Edition. Springer, Heidelberg (1997)
Ayara, M., Timmis, J., de Lemos, R., de Castro, L.N., Duncan, R.: Negative Selection: How to Generate Detectors. In: Proceedings of ICARIS 2002. University of Kent at Canterbury Printing Unit (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Taylor, D.W., Corne, D.W. (2003). An Investigation of the Negative Selection Algorithm for Fault Detection in Refrigeration Systems. In: Timmis, J., Bentley, P.J., Hart, E. (eds) Artificial Immune Systems. ICARIS 2003. Lecture Notes in Computer Science, vol 2787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45192-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-45192-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40766-9
Online ISBN: 978-3-540-45192-1
eBook Packages: Springer Book Archive