Fault Diagnosis Feature Set Optimization Algorithm Based on Immunity

Jin Ying Huang; Hong Xia Pan; Hai Rui Wang; Yue Li

doi:10.4028/www.scientific.net/AMR.875-877.2062

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 875-877Fault Diagnosis Feature Set Optimization Algorithm...

Fault Diagnosis Feature Set Optimization Algorithm Based on Immunity

Abstract:

For reducing the dimensions of feature set and the amount of calculation in fault diagnosis, discrete immunity feature set optimization algorithm is put forward in this paper. Vibration signals are tested from laboratory gearbox under four specific conditions. Twenty-five kinds of feature parameters of those signals are extracted as the initial feature set. Distance-based objective function is established for the algorithm. By discrete binary encoding, immunity cloning, mutation and selection operating, the feature set is optimized. The optimal set includes eleven kinds of feature. Compared with the initial feature set, the application of optimal feature set in gearbox fault diagnosis has reduced computation and improved the diagnostic accuracy and efficiency greatly.

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Periodical:

Advanced Materials Research (Volumes 875-877)

Pages:

2062-2066

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.875-877.2062

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Online since:

February 2014

Authors:

Jin Ying Huang, Hong Xia Pan, Hai Rui Wang, Yue Li

Keywords:

Fault Diagnosis, Feature Set Optimization, Immunity

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References

[1] Liao zhang-zhen, etc. Artificial Immune System of Basic Theory and Its Application. Automation and Instrumentation, 2008, (1): 5-8.

Google Scholar

[2] A Watkins, L Boggess. A new classifier based on resource limited artificial immune systems, (Proceedings of Congress on Evolutionary Computation, USA 2002).

DOI: 10.1109/cec.2002.1004472

Google Scholar

[3] Gang Shi, Yuanwei Jing. Research of Improved Immune Clonal Algorithms and its Applications (proceedings of International Conference on Computational Intelligence for Measurement Systems and Applications, China 2009).

DOI: 10.1109/cimsa.2009.5069950

Google Scholar

[4] De Castro L N, Von Zuben F J. Learning and Optimization Using the Clonal Selection Principle. [J]. IEEE Trans on Evolutionary Computation, Special Issue on Artificial Immune Systems, Vol. 6 (2001), pp.239-251.

DOI: 10.1109/tevc.2002.1011539

Google Scholar

[5] Hong Lu, etc. Research of Improved Multi-modal Immune Optimization Algorithm. Journal of System Simulation. 2008, 20, (13): 3440-3444.

Google Scholar

[6] Luo Yin-sheng. Li Ren-hou, Zhang Wei-xi. Multimodal Functions Parallel Optimization Algorithm Based on Immune Mechanism. Journal of System Simulation, 2005, 17(2): 319-322.

Google Scholar