Journal of Shanghai Jiao Tong University (Science) ›› 2018, Vol. 23 ›› Issue (5): 691-695.doi: 10.1007/s12204-018-1964-3

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Discretization Algorithm Based on Particle Swarm Optimization and Its Application in Attributes Reduction for Fault Data

ZHENG Bo (郑波), LI Yanfeng (李彦锋), FU Guozhong (付国忠)   

  1. (1. Aviation Engineering institution, Civil Aviation Flight University of China, Guanghan 618307, Sichuan, China; 2. Center for System Reliability and Safety, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)
  • 出版日期:2018-10-01 发布日期:2018-10-07
  • 通讯作者: LI Yanfeng (李彦锋) E-mail:yanfengli@uestc.edu.cn

Discretization Algorithm Based on Particle Swarm Optimization and Its Application in Attributes Reduction for Fault Data

ZHENG Bo (郑波), LI Yanfeng (李彦锋), FU Guozhong (付国忠)   

  1. (1. Aviation Engineering institution, Civil Aviation Flight University of China, Guanghan 618307, Sichuan, China; 2. Center for System Reliability and Safety, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)
  • Online:2018-10-01 Published:2018-10-07
  • Contact: LI Yanfeng (李彦锋) E-mail:yanfengli@uestc.edu.cn

摘要： In order to increase the fault diagnosis efficiency and make the fault data mining be realized, the decision table containing numerical attributes must be discretized for further calculations. The discernibility matrix-based reduction method depends on whether the numerical attributes can be properly discretized or not. So a discretization algorithm based on particle swarm optimization (PSO) is proposed. Moreover, hybrid weights are adopted in the process of particles evolution. Comparative calculations for certain equipment are completed to demonstrate the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm has better performance than other popular algorithms such as class-attribute interdependence maximization (CAIM) discretization method and entropy-based discretization method.

关键词: attributes discretization, fault data reduction, discernibility matrix, particle swarm optimization (PSO), hybrid weight

Abstract: In order to increase the fault diagnosis efficiency and make the fault data mining be realized, the decision table containing numerical attributes must be discretized for further calculations. The discernibility matrix-based reduction method depends on whether the numerical attributes can be properly discretized or not. So a discretization algorithm based on particle swarm optimization (PSO) is proposed. Moreover, hybrid weights are adopted in the process of particles evolution. Comparative calculations for certain equipment are completed to demonstrate the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm has better performance than other popular algorithms such as class-attribute interdependence maximization (CAIM) discretization method and entropy-based discretization method.

Key words: attributes discretization, fault data reduction, discernibility matrix, particle swarm optimization (PSO), hybrid weight

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