연구동향 분석
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회전설비 진단을 위한 효율적인 차원 조합 기술 및 경향 분석 시스템 개발 = Development of Efficient Dimension Combination Technology and Trend Analysis System for Rotating Machinery Diagnosis

하정민 2020년

논문상세정보
인용/피인용
' 회전설비 진단을 위한 효율적인 차원 조합 기술 및 경향 분석 시스템 개발 = Development of Efficient Dimension Combination Technology and Trend Analysis System for Rotating Machinery Diagnosis' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 기계학습
  • 머신 러닝
  • 설비진단
  • 유전 알고리즘
  • 차원 축소
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
2,174 0

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' 회전설비 진단을 위한 효율적인 차원 조합 기술 및 경향 분석 시스템 개발 = Development of Efficient Dimension Combination Technology and Trend Analysis System for Rotating Machinery Diagnosis' 의 참고문헌

  • ¡°Vibration feature extraction techniques for fault diagnosis of rotating machinery : a literature survey¡±
    [2003]
  • ¡°Support vector machine in machineCondition monitoring and fault diagnosis¡±
    21 ( 6 ) : 2560 - 2574 [2007]
  • ¡°A GA-based feature selection and parameters optimization for support vector machines¡±
    31 ( 2 ) : 231 ? 240 [2006]
  • Vibration-based damage detection in an aircraft wing scaled model using principal component analysis and pattern recognition
    313 ( 3-5 ) : 560 - 566 [2008]
  • The intelligent industry of the future : A survey on emerging trends , research challenges and opportunities in Industry 4.0
    9 ( 3 ) : 287 - 298 [2017]
  • Support-vector networks
    20 ( 3 ) : 273 - 297 . [1995]
  • Service innovation and smart analystics for Indusrty 4.0 and big data environment
    16 : 3 - 8 . [2014]
  • Rank and responseCombination fromConfusion matrix data
    2 ( 2 ) : 113 - 120 [2001]
  • Mechanical vibration ? Measurement and evaluation of machine vibration - Part 1
    [2016]
  • Machine learning approaches for failure type detection and predictive maintenance
    19 [2015]
  • Learning with Genetic Algorithms : An Overview
    3 ( 95 ) : 121 - 138 [1988]
  • Information-theoretic algorithm for feature selection
    22 ( 7 ) : 799 - 811 [2001]
  • Genetic programming for simultaneous feature selection and classifier design .
    36 : 106-117 . [2006]
  • Genetic algorithms for feature selection in machine condition monitoring with vibration signals
    147 ( 3 ) : 205 - 212 [2000]
  • Genetic Algorithms as a Tool for Feature Selection in Machine Learning
    92 : 200 ? 203 [1992]
  • Genetic Algorithms as Strategy for feature selection
    6 ( 5 ) : 267 - 281 [1992]
  • Genetic Algorithm and Machine Learning
    3 ( 95 ) : 95 - 99 [1988]
  • Gear fault detection using artificial neural networks and support vector machines with genetic algorithms ,
    18 ( 3 ) : 625 - 644 [2004]
  • Enhanced DET-based fault signature analysis for reliable diagnosis of single and multiple-combined bearing defects .
    [2015]
  • Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors
    501 : 714 ? 715 [2016]
  • Detecting gear tooth cracks using cepstral analysis in gearbox of helicopters
    5 ( 2 ) : 139 - 145 [2013]
  • Comparison of algorithms that select features for pattern classifiers
    33 ( 1 ) : 25-41 . [2000]
  • B. K. 2019 . Feature-Based Trend Monitoring of Vibration Signals according to Severity of Gear Tooth Breakage .
    29 ( 2 ) : 199 - 205
  • B. K. 2018 . Degradation Trend Estimation and Prognostics for Low Speed Gear Lifetime
    19 ( 8 ) : 1099 - 1105
  • B. K. 2017 . Vibration Signal Analysis of Gearbox Fault According to Feature . Transaction of Korean Society for Noise and Vibration Engineering
    27 ( 4 ) :419 - 424
  • B. K. 2017 . Feature Analysis for Fault Diagnosis according to Gearbox Failure
    27 ( 3 ) : 312 - 317
  • Artificial neural network based fault diagnostics of rolling element bearings using time-domain features
    17 ( 2 ) : 317-328 . [2003]
  • Applicaion of nonlinear feature extraction and support vector machines for fault diagnosis of induction motor
    33 ( 1 ) : 241 ? 250 [2007]
  • A history of Cepstrum analysis its application to mechanical problems
    97 : 3 - 19 [2016]
  • 29. Lindasay. I. S. 2002. A Tutorial on Principal Components Analysis.
  • 1. Jeong H. D. and Lee S. C. 2017. Industrial Artificial Intelligence. Transaction of Korean Society for Noise and Vibration Engineering. 27(6) : 3 - 7.