A New Algorithm Based on Negative Selection and Idiotypic Networks for Generating Parsimonious Detector Sets for Industrial Fault Detection Applications

Authors:
Eduard Plett;Sanjoy Das
Affiliations:
Department of Engineering Technology, Kansas State University, Salina, USA;Department of Electrical and Computer Engineering, Kansas State University, Manhattan, USA
Venue:
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Year:
2009

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Abstract

Artificial Immune System (AIS) algorithms have been used for well over a decade to detect anomalies in computer security and data collection applications. They have also been used for real-time industrial fault detection applications, where they typically outperform traditional limit-checking algorithms in terms of anomaly detection ability. However, large computing overhead and large memory requirements make traditional AIS algorithms unsuitable for many applications. In this paper we propose a new AIS algorithm called Parsimonious Detector Set Algorithm which promises to greatly limit the effect of both constraints and make AIS algorithms suitable for a wider range of applications.