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Abstract

Rule extraction is viewed as an important pretreatment step for machine learning and data mining. In allusion to the shortcomings of the rough set method for rule extraction in systems with incomplete fault diagnosis, in this paper we propose a novel fusion approach based on rough set theory and particle swarm optimization for rule extraction (RSTPSORE) in order to improve the diagnostic robustness and accuracy. In the proposed method, first, an iterative linear subsection interpolation completion method is used to achieve completion of the fault diagnosis process. Then rough set theory is used to find a subset which can preserve the meaning of the attributes. Particle swarm optimization is used to discover the best rule within the flying subset space. To verify the efficiency of the proposed method, experiments are carried out on the collected data in a centralized control substation incomplete fault diagnosis system. The results indicate that the proposed method can provide an efficient solution to finding a minimal subset.

Keywords

Rule extraction rough set theory particle swarm optimization incomplete fault information centralized control substation fusion approach

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References

Kudo

Sklansky

. Comparison of algorithms that select features for pattern classifiers. Pattern Recogn 2000; 33(1): 25–41.

Tsumoto

. Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model. Inform Sci 2004; 162(2): 65–80.

Wroblewski

. Finding minimal reducts using genetic algorithms. In: Proceedings of second annual joint conference on information sciences, Wrightsville Beach, NC, 28 September–1 October 1995, pp.186–189.

Zhang

Xiong

Liu

. Fault diagnosis based on optimized node entropy using lifting wavelet packet transform and genetic algorithms. Proc IMechE, Part I: J Systems and Control Engineering 2010; 224(5): 557–573

Kuo

Chao

Chiu

. Application of particle swarm optimization to association rule mining. Appl Soft Comput 2011; 11(1): 326–336.

Omkar

Raghavendra Karanth

. Rule extraction for classification of acoustic emission signals using ant colony optimization. Eng Applic Artif Intell 2008; 21(8): 1381–1388.

Yang

. A particle swarm optimization-based time-scaling method for quasi-time-optimal control of rigid spacecraft along specified paths. Proc IMechE, Part I: J Systems and Control Engineering 2008; 222(1): 1–9.

Chai

Ding

. Intelligent work-situation fault diagnosis and fault-tolerant system for the shaft-furnace roasting process. Proc IMechE, Part I: J Systems and Control Engineering 2007; 221(6): 843–855.

Zhai

Khoo

Fok

. Feature extraction using rough set theory and genetic algorithms–an application for the simplification of product quality evaluation. Comput Ind Eng 2002; 43(4): 661–676.

10.

Kamrani

Rong

Gonzalez

. A genetic algorithm methodology for data mining and intelligent knowledge acquisition. Comput Ind Eng 2001; 40(4): 361–377.

11.

Chen

Miao

Wang

. A rough set approach to feature selection based on ant colony optimization. Pattern Recogn Lett 2010; 31(3): 226–233.

12.

Bae

Yeh

Chung

. Feature selection method with intelligent dynamic swarm and rough set. Expert Syst Applic 2010; 37(10): 7026–7032.

13.

Kryszkiewicz

. Properties of incomplete information systems in the framework of rough sets. In: Rough sets in knowledge discovery. Heidelberg: Physica, 1998, vol. 1, pp.422–450.

14.

Pawlak

. Rough sets. Int J Comput Inform Sci 1982; 11(5): 341–356.

15.

Inuiguchi

. Attribute reduction in variable precision rough set model. Int J Uncertainty Fuzziness Knowledge-Based Syst 2006; 14(4): 461–479.

16.

Geng

Zhu

. Rough set-based heuristic hybrid recognizer and its application in fault diagnosis. Expert Syst Applic 2009; 36(2): 2711–2718.

17.

Kennedy

Eberhart

. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. Piscataway, NJ: IEEE Press, 1995, pp.1942–1948.