Multidomain Features-Based GA Optimized Artificial Immune System for Bearing Fault Detection

This paper proposes a novel multidomain features-based genetic algorithm (GA) optimized artificial immune system (AIS) framework for fault detection in real systems. Different from native real-valued negative selection algorithm (RNSA) that operates in original data space, this algorithm utilizes fe...

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Veröffentlicht in:	IEEE transactions on systems, man, and cybernetics. Systems Jg. 50; H. 1; S. 348 - 359
Hauptverfasser:	Abid, Anam, Khan, Muhammad Tahir, Khan, Muhammad Salman
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Anomaly detection Artificial immune system (AIS) Artificial intelligence Clustering deep learning Detectors Fault detection Fault diagnosis Feature extraction feature selection genetic algorithm (GA) Genetic algorithms Immune system Machine learning negative selection algorithm (NSA) Neural networks Signal processing Training Transformations
ISSN:	2168-2216, 2168-2232
Online-Zugang:	Volltext
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