Wind Turbine Drivetrain Expert Fault Detection System: Multivariate Empirical Mode Decomposition based Multi-sensor Fusion with Bayesian Learning Classification | Zendy

R. Maheswari | Zendy; R. Umamaheswari | Zendy

Open Access

Wind Turbine Drivetrain Expert Fault Detection System: Multivariate Empirical Mode Decomposition based Multi-sensor Fusion with Bayesian Learning Classification

Author(s) -

R. Maheswari,

R. Umamaheswari

Publication year - 2020

Publication title -

intelligent automation and soft computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.271

H-Index - 27

eISSN - 2326-005X

pISSN - 1079-8587

DOI - 10.32604/iasc.2020.013924

Subject(s) - computer science , drivetrain , fault detection and isolation , artificial intelligence , turbine , hilbert–huang transform , naive bayes classifier , wind power , instantaneous phase , pattern recognition (psychology) , condition monitoring , accelerometer , support vector machine , torque , white noise , engineering , computer vision , physics , filter (signal processing) , actuator , thermodynamics , operating system , mechanical engineering , telecommunications , electrical engineering

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