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Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter

Department of Robot Engineering, Kyungil University, Gyeongsan 38428, Korea
School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(16), 3329;
Received: 30 May 2019 / Revised: 26 July 2019 / Accepted: 8 August 2019 / Published: 13 August 2019
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
PDF [2335 KB, uploaded 13 August 2019]


Early detection and diagnosis of wind turbine faults is critical for applying a possible maintenance and control strategy to avoid catastrophic incidents. This paper presents a novel method to estimate the parameter of faults in a wind turbine. In this work, the estimation of fault parameters is reformulated as the state estimation problem by augmenting the parameters as an additional state. The novelty of the proposed method lies in the use of an adaptive fuzzy fading algorithm for the adaptive Kalman filter so that the convergence property during the estimation of fault parameter can be improved. The performance of the proposed method is evaluated through a set of numerical simulations with both linear and non-linear models. View Full-Text
Keywords: fault; fuzzy; kalman filter; parameter estimation fault; fuzzy; kalman filter; parameter estimation

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Kim, D.; Lee, D. Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter. Appl. Sci. 2019, 9, 3329.

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