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Volumn 221, Issue 1, 2007, Pages 29-41

Ensemble feature selection for diagnosing multiple faults in rotating machinery

Author keywords

ensemble classification; fault diagnosis; feature selection; genetic algorithms optimization; multiple faults; rotating machinery

Indexed keywords


EID: 84993746880     PISSN: 1748006X     EISSN: 17480078     Source Type: Journal    
DOI: 10.1243/1748006XJRR38     Document Type: Article
Times cited : (6)

References (12)
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    • Tsymbal, A.1    Puuronen, S.2    Skrypnyk, I.3
  • 2
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    • Tsymbal, A.1    Pechenizkiy, M.2    Cunningham, P.3
  • 3
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    • A feature selection approach based on principal component analysis and synaptic weights for mechanical fault diagnosis
    • Ed. Kolowrocki Taylor and Francis London
    • Kilundu B. Dehombreux P. A feature selection approach based on principal component analysis and synaptic weights for mechanical fault diagnosis Advances in safety and reliability (Ed. Ed. Kolowrocki), 2005 (Taylor and Francis, London).
    • (2005) Advances in safety and reliability
    • Kilundu, B.1    Dehombreux, P.2
  • 4
    • 0003912827 scopus 로고
    • Adaptation in natural and artificial systems: an introductory analysis with application to biology
    • 4th edition MIT Press Cambridge, MA
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    • (1975) Control and artificial intelligence
    • Holland, J.H.1
  • 9
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    • Statistical data mining with informational complexity and genetic algorithm. In
    • Ed. Bozdogan H. CRC Press London
    • Bozdogan H. Statistical data mining with informational complexity and genetic algorithm. In Statistical data mining and knowledge discovery, (Ed. Ed. Bozdogan H.) (CRC Press, London).
    • Statistical data mining and knowledge discovery
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  • 10
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    • Selecting features for nuclear transients classification by means of genetic algorithms
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    • Zio E. Baraldi P. Pedroni N. Selecting features for nuclear transients classification by means of genetic algorithms IEEE Trans. Nuclear Sci., 2006. Accepted for Publication.
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    • Zio, E.1    Baraldi, P.2    Pedroni, N.3
  • 12
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    • Feature selection based on a modified fuzzy c-means algorithm with supervision
    • Marcelloni F. Feature selection based on a modified fuzzy c-means algorithm with supervision Inf. Sci., 2003, 151, 201–256.
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    • Marcelloni, F.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.