Proceedings Article10.1109/ICSMC.2006.385120
Clonal Optimization of Negative Selection Algorithm with Applications in Motor Fault Detection
Xiao-Zhi Gao,Seppo J. Ovaska,Xiaolei Wang,Mo-Yuen Chow +3 more
- 01 Oct 2006
- Vol. 6, pp 5118-5123
21
TL;DR: Taking advantage of the clonal optimization strategy, the NSA detectors can be optimized for anomaly detection and a new motor fault detection scheme using the authors' NSA is discussed.
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Abstract: In this paper, we employ the clonal optimization method to optimize the detectors in the negative selection algorithm (NSA). Taking advantage of the clonal optimization strategy, the NSA detectors can be optimized for anomaly detection. A new motor fault detection scheme using our NSA is also discussed. We demonstrate the efficiency of the proposed approach with an example of bearings fault detection.
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Citations
Study on the application of the hierarchical fuzzy neural network in the fault diagnosis of the asynchronous motor
Yun Zhang,Nan Xu,Quanxi Ji +2 more
- 23 Sep 2010
TL;DR: The experimental results show that this diagnosis method can effectively classify the single fault samples and the multi fault samples of the motor and this not only can raise the accurateness rate of the diagnosis, but it also possesses a good applicable value in engineering.
2
A Detector Generation Algorithm Based on Negative Selection
Qian Wang,Xiao-kai Feng +1 more
- 18 Oct 2008
TL;DR: An algorithm named VRGA for detector generation is proposed and variable matching threshold r (r-variable) is introduced in VRGA to effectively increase the detector coverage and increase the diversity of detectors.
2
Negative Selection Algorithm Research and Applications in the Last Decade: A Review
TL;DR: The Negative Selection Algorithm (NSA) is one of the important methods in the field of Immunological Computation (or Artificial Immune Systems) as discussed by the authors , and some progress has been made which turns this algorithm ( NSA) into an efficient approach to solve problems in different domain.
Abnormality degree detection method using negative potential field group detectors
TL;DR: The problem of abnormal degree detection without fault sample is studied with a new detection method called negative potential field group detectors (NPFG-detectors), which achieves the quantitative expression of abnormality degree and provides the better detection results compared with other methods.
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Dipankar Dasgupta,Senhua Yu,Fernando Nino +2 more
- 01 Mar 2011
TL;DR: A survey of the major works in the AIS field explores up-to-date advances in applied AIS during the last few years and reveals that recent research is centered on four major AIS algorithms: negative selection algorithms; artificial immune networks; clonal selection algorithm; Danger Theory and dendritic cell algorithms.
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TL;DR: This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response and derives two versions of the algorithm, derived primarily to perform machine learning and pattern recognition tasks.
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