Unsupervised feature selection algorithm utilizing multi-objective evolutionary model for fault diagnosis

XIA Hu;ZHUANG Jian;ZHOU Fan;YU De-hong

Journal of Vibration and Shock ›› 2014, Vol. 33 ›› Issue (8) : 61-65.

PDF(1387 KB)
PDF(1387 KB)
Journal of Vibration and Shock ›› 2014, Vol. 33 ›› Issue (8) : 61-65.
论文

Unsupervised feature selection algorithm utilizing multi-objective evolutionary model for fault diagnosis

  • XIA Hu1, ZHUANG Jian1, ZHOU Fan2,YU De-hong1
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Abstract

Feature selection is necessary for high-dimensional features in fault diagnosis since it can improve the efficiency and accuracy. However, traditional feature selection algorithm always has a strong bias towards a single criterion, which is harmful to the quality of feature selection. An unsupervised feature selection algorithm based on multi-objective evolutionary model was proposed to solve this problem. A relevance objective based on entropy measure and a redundancy objective based on correlation coefficient were simultaneously optimized. Both initialization process and mutation operator were also designed by utilizing the distribution of samples in each feature. Besides, an ensemble method was proposed to obtain the importance order. Experiments on five UCI and three valve fault of reciprocating compressor datasets demonstrated better performance of the proposed algorithm.

Key words

feature selection / multi-objective evolutionary algorithm / redundancy measure / fault diagnosis

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XIA Hu;ZHUANG Jian;ZHOU Fan;YU De-hong. Unsupervised feature selection algorithm utilizing multi-objective evolutionary model for fault diagnosis[J]. Journal of Vibration and Shock, 2014, 33(8): 61-65
PDF(1387 KB)

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