An Anomaly Entection Algorithm Inspired by the Immune Syste

Entecting anomaly in a system or a process behavior is very important in many real-world applications such as manufacturing, monitoring, signal processing etc. This chapter presents an anomaly Entection algorithm inspired by the negative-selection mechanism of the immune system, which discriminates betweensellandother.Here self is Enfined to benormal data patternsand non-self is any Enviation exceeding an allowable variation. Experiments with this anomaly Entection algorithm are reported for two data sets - time series data, generated using the Mackey-Glass equation and a simulated signal.Compared to existing methods, this method has the advantage of not requiring prior knowledge about all possible failure moEns of the monitored system. Results are reported to display the performance of the Entection algorithm

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