Novelty Detection in Time Series Data using Ideas from Immunology (1995)
| Venue: | In Proceedings of The International Conference on Intelligent Systems |
| Citations: | 76 - 15 self |
BibTeX
@INPROCEEDINGS{Dasgupta95noveltydetection,
author = {Dipankar Dasgupta and Stephanie Forrest},
title = {Novelty Detection in Time Series Data using Ideas from Immunology},
booktitle = {In Proceedings of The International Conference on Intelligent Systems},
year = {1995}
}
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Abstract
Detecting anomalies in time series data is a problem of great practical interest in many manufacturing and signal processing applications. This paper presents a novelty detection algorithm inspired by the negative-selection mechanism of the immune system, which discriminates between self and other. Here self is defined to be normal data patterns and non-self is any deviation exceeding an allowable variation. An example application, simulated cutting dynamics in a milling operation, is presented, and the performance of the algorithm in detecting the tool breakage is reported. 1 INTRODUCTION The normal behavior of a system is often characterized by a series of observations over time. The problem of detecting novelties or anomalies can be viewed as finding non permitted deviations of a characteristic property in the system of interest. The detection of novelty is an important task in many diagnostic and monitoring systems. In safety-critical applications, it is essential to detect the o...
