A hybrid approach of rough set theory and genetic algorithm for fault diagnosis

Huang, Cheng-Lung; Li, T.S.; Peng, Ting-Kuo

doi:10.1007/s00170-004-2142-3

Cited by 18 publications

(7 citation statements)

References 23 publications

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“…al. [9] put it that decision table is characterized by disjoint sets of condition attributes (C  Q) and decision (action) attributes (D  Q). In this regard Q = C  D and C ∩ D = Ø.…”

Section: Rough Set Data Mining In Dealing With Inconsistent Numericalmentioning

confidence: 99%

“…When some conditions are satisfied, deterministic DS uniquely describes the decisions (actions) to be made. In a non-deterministic DS, decisions are not uniquely determined by the conditions [9]. Formally, it is defined that:…”

Section: Terms Of Values Of Attributes From P)mentioning

confidence: 99%

“…rules 1, 2, 3 and rules 16,17,18,19, altogether totaling 7 rules, are inconsistent (false). The positive region of the CD-decision algorithm consists of only consistent decision rules 4,5,6,7,8,9,10,11,12,13,14,15,20 and 21 (i.e. card POS(C,D) = 14) and, hence, the degree of dependency is k = 14/21= 0.67.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Inconsistent Decision System: Rough Set Data Mining Strategy to Extract Decision Algorithm of a Numerical Distance Relay - Tutorial

Othman¹,

Aris²

2012

Advances in Data Mining Knowledge Discovery and Applications

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Section: Rough Set Data Mining In Dealing With Inconsistent Numericalmentioning

confidence: 99%

Section: Terms Of Values Of Attributes From P)mentioning

confidence: 99%

See 1 more Smart Citation

Inconsistent Decision System: Rough Set Data Mining Strategy to Extract Decision Algorithm of a Numerical Distance Relay - Tutorial

Othman¹,

Aris²

2012

Advances in Data Mining Knowledge Discovery and Applications

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“…Inada and Terano [85] presented a useful and effective method "QC Chart Mining" to extract the systematic patterns from quality control charts in order to manage the clinical test data. Huang et al [86] presented an integrated diagnostic support system which uses hybrid rough set theory and genetic algorithm. The proposed approach has been applied at a mother board manufacturing company to discover decision rules for EMI faults.…”

Section: Clustering In Manufacturingmentioning

confidence: 99%

Data mining in manufacturing: a review based on the kind of knowledge

2008

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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied.

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“…Lo, et al, [8] proposed a fault diagnosis method based on genetic algorithms (GA's) and qualitative bond graphs (QBG's) for an in-house designed and built floating disc experimental setup. Huang, et al, [9] proposed an integrated intelligent system that builds a fault diagnosis inference model based on the advantage of rough set theory and genetic algorithms (GAs). This integrated system successfully integrated the rough set theory for handling uncertainty with a robust search engine, GA. Fei and Zhang [10] proposed support vector machine with genetic algorithm (SVMG) to apply to fault diagnosis of a power transformer, in which genetic algorithm (GA) is used to select appropriate free parameters of SVM.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Intelligent Method for Fault Diagnosis of the Complex System

Chu¹,

Niu²

2016

IJHIT

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In allusion to the low correctness and efficiency of fault diagnosis for the complex industrial system, rough set theory, particle swarm optimization and back propagation (BP) neural network are introduced to propose a hybrid intelligent fault diagnosis(RPBPNN) method in this paper. In the proposed RPBPNN method, rough set theory as a new mathematical tool is used to process inexact and uncertain knowledge in order to obtain the minimum fault characteristic set for simplifying the structure and improving learning efficiency of BPNN. The particle swarm optimization (PSO) algorithm with the global optimization ability is directly used to train the weights of BP neural network in order to establish the optimized BP neural network model. Then the minimum fault characteristic set is used to train the optimized BP neural network model in order to obtain the optimal BP neural network model for realizing the fault diagnosis. Finally, the proposed RPBPNN method is applied to an actual application case for verifying the effectiveness. The experimental results show that PSO algorithm can search for the optimal values of BPNN parameters and the proposed RPBPNN method can accurately eliminate false and improve the diagnostic accuracy. So the proposed RPBPNN method takes on better generalization performance and prediction accuracy in the real industrial application system.

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A hybrid approach of rough set theory and genetic algorithm for fault diagnosis

Cited by 18 publications

References 23 publications

Inconsistent Decision System: Rough Set Data Mining Strategy to Extract Decision Algorithm of a Numerical Distance Relay - Tutorial

Inconsistent Decision System: Rough Set Data Mining Strategy to Extract Decision Algorithm of a Numerical Distance Relay - Tutorial

Data mining in manufacturing: a review based on the kind of knowledge

A Novel Hybrid Intelligent Method for Fault Diagnosis of the Complex System

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