Multiple-model based sensor fault diagnosis using hybrid kalman filter approach for nonlinear gas turbine engines
Author | Pourbabaee, B. |
Author | Meskin, Nader |
Author | Khorasani, K. |
Available date | 2022-04-14T08:45:45Z |
Publication Date | 2013 |
Publication Name | Proceedings of the American Control Conference |
Resource | Scopus |
Abstract | In this paper, an efficient sensor fault detection and isolation (FDI) strategy is proposed based on multiple-model (MM) approach. The scheme is composed of hybrid kalman filters (HKF) by integrating a nonlinear gas turbine engine model that represents the operational engine model with a number of piecewise linear (PWL) models to estimate sensor outputs. The proposed FDI scheme is capable of detecting and isolating permanent sensor bias faults during the entire operational regime of the engine by interpolating the PWL models using a Bayesian approach. Another important aspect of our proposed FDI strategy is its effectiveness within the engine life cycle by periodically updating the model to the degraded health parameters, that one estimated by means of an off-line trend monitoring system that is based on post flight data. The simulation results demonstrate the effectiveness of our proposed online sensor fault diagnosis scheme as well as the robustness of our technique with respect to the engine health parameters degradations. 2013 AACC American Automatic Control Council. |
Sponsor | Qatar National Research Fund |
Language | en |
Publisher | 2013 1st American Control Conference, ACC 2013 |
Subject | Bayesian approaches Filter approach Health parameters Nonlinear gas turbines Piecewise linear models Sensor fault detection and isolations (FDI) Sensor fault diagnosis Trend monitoring Bayesian networks Engines Gas turbines Kalman filters Piecewise linear techniques Sensors |
Type | Conference Paper |
Pagination | 4717-4723 |
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Electrical Engineering [2579 items ]