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Robust sensor fault detection and isolation of gas turbine engines


Robust sensor fault detection and isolation of gas turbine engines

Pourbabaee, Bahareh (2016) Robust sensor fault detection and isolation of gas turbine engines. PhD thesis, Concordia University.

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An effective fault detection and isolation (FDI) technology can play a crucial role in improving the system availability, safety and reliability as well as reducing the risks of catastrophic failures. In this thesis, the robust sensor FDI problem of gas turbine engines is investigated and different novel techniques are developed to address the effects of parameter uncertainties, disturbances as well as process and measurement noise on the performance of FDI strategies. The efficiencies of proposed techniques are investigated through extensive simulation studies for the single spool gas turbine engine that is previously developed and validated using the GSP software. The gas turbine engine health degradation is considered in various forms in this thesis. First, it is considered as a part of the engine dynamics that is estimated off-line and updated periodically for the on-board engine model. Second, it is modeled as the time-varying norm-bounded parameter uncertainty that affects all the system state-space matrices and third as an unknown nonlinear dynamic that is approximated by the use of a dynamic recurrent neural network.

In the first part of the thesis, we propose a hybrid Kalman filter (HKF) scheme that consists of a single nonlinear on-board engine model (OBEM) augmented with piecewise linear (PWL) models constituting as the multiple model (MM) based estimators to cover the entire engine operating regime. We have integrated the generalized likelihood ratio (GLR)-based method with our MM-based scheme to estimate the sensor fault severity under various single and concurrent fault scenarios. In order to ensure the reliability of our proposed HKF-based FDI scheme during the engine life cycle, it is assumed that the reference baselines are periodically updated for the OBEM health parameters.

In the second part of the thesis, a novel robust sensor FDI strategy using the MM-based approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple PWL models. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (ARE) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The main objective is to propose a robust filter that satisfies the overall performance requirements and is not affected by system perturbations. The requirements include a quadratically stable filter that ensures bounded estimation error variances having predefined values.

In the third part of the thesis, a novel hybrid approach is proposed to improve the robustness of FDI scheme with respect to different sources of uncertainties. For this purpose, a dynamic recurrent neural network (DRNN) is designed to approximate the gas turbine engine uncertainty due to the health degradations. The proposed DRNN is trained offline by using the extended Kalman filter (EKF) algorithm for an engine with different levels of uncertainty, but with healthy sensors. The convergence of EKF-based DRNN training algorithm is also investigated. Then, the trained DRNN with the fixed parameters and topology is integrated with our online model-based FDI algorithm to approximate the uncertainty terms of the real engine. In this part, the previously proposed HKF and RKF are integrated with the trained DRNN to construct the hybrid FDI structure.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (PhD)
Authors:Pourbabaee, Bahareh
Institution:Concordia University
Degree Name:Ph. D.
Program:Electrical and Computer Engineering
Date:18 December 2016
Thesis Supervisor(s):Khorasani, Khashayar and Meskin, Nader
Keywords:Fault Detection and Isolation, Gas Turbine Engine, Robust Estimation Theory, Kalman Filter, Recurrent Neural Network
ID Code:982072
Deposited On:31 May 2017 18:43
Last Modified:18 Jan 2018 17:54
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