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Volumn , Issue , 2009, Pages 1228-1235

Particle and Kalman Filtering for fault diagnosis in DC motors

Author keywords

[No Author keywords available]

Indexed keywords

FAULT DIAGNOSIS; GAUSSIAN MEASUREMENTS; INDUSTRIAL ROBOTICS; INDUSTRIAL SYSTEMS; KALMAN-FILTERING; LINEAR MODEL; MEASUREMENT NOISE; NON-LINEAR MODEL; PARTICLE FILTER; SIMULATION EXPERIMENTS;

EID: 72149119990     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/VPPC.2009.5289708     Document Type: Conference Paper
Times cited : (13)

References (23)
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  • 2
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  • 3
    • 0034297534 scopus 로고    scopus 로고
    • Application of model-based fault detection to a brushless DC motor
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    • (2000) IEEE Transactions on Industrial Electronics , vol.47 , Issue.5 , pp. 1015-1020
    • Moseler, O.1    Isermann, R.2
  • 4
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    • Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems
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  • 5
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    • (2002) Proc. 13th Intl. Workshop on Principles Diagnosis
    • Dearden, R.1    Clancy, D.2
  • 9
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    • Fuzzy model validation using the local statistical approach
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    • Rigatos, G.1    Zhang, Q.2
  • 10
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    • Particle and kalman filtering for state estimation and control of DC motors
    • Elsevier
    • G.G. Rigatos, Particle and Kalman filtering for state estimation and control of DC motors, ISA Transactions, Elsevier, vol. 48, no. 1, pp. 62-72, 2009.
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  • 12
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.