Design of an artificial immune system based on Danger Model for fault detection

This paper presents a methodology that enables fault detection in dynamic systems based on recent immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The fault detection central challenge is de...

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Veröffentlicht in:Expert systems with applications 2010-07, Vol.37 (7), p.5145-5152
Hauptverfasser: Laurentys, C.A., Palhares, R.M., Caminhas, W.M.
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Palhares, R.M.
Caminhas, W.M.
description This paper presents a methodology that enables fault detection in dynamic systems based on recent immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The fault detection central challenge is determining the difference between normal and potential harmful activities at dynamic systems. A promising solution is emerging in the form of Artificial Immune Systems (AIS). The Danger Model (DM) proposes that the immune system reacts not against self or non-self but by threats generated into the organism: the danger signals. DM-based fault detection system proposes a new formulation for a fault detection system. A DM-inspired methodology is applied to a fault detection benchmark provided by DAMADICS to compare its relative performance to others algorithms. The results show that the strategy developed is promising for incipient and abrupt fault detection in dynamic systems.
doi_str_mv 10.1016/j.eswa.2009.12.079
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subjects Artificial immune system
Computational intelligence
Decision support
Fault detection
Fuzzy set
Model development
Neural network
title Design of an artificial immune system based on Danger Model for fault detection
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