Health Estimation of Gas Turbine: A Symbolic Linearization Model Approach

2017 ◽  
Author(s):  
Qingcai Yang ◽  
Yunpeng Cao ◽  
Fang Yu ◽  
Jianwei Du ◽  
Shuying Li
Keyword(s):  
Steady State ◽  
State Space ◽  
Gas Turbine ◽  
Linear Model ◽  
Piecewise Linear ◽  
State Space Model ◽  
Space Model ◽  
Linear State ◽  
Model Approach ◽  
Health Parameters

This paper is mainly concerned with the health estimation of a gas turbine using a symbolic linearization model approach. Health parameters will change with the degradation of gas turbine performance. Monitoring and evaluating these health parameters can assist in the development of predictive control techniques and maintenance schedules. Currently, various health parameter estimation methods have been studied extensively, but there have been less related studies on how to obtain statespace models. In this paper, a symbolic linearization model method is presented to overcome the shortcoming of high time consumption suffered by existing methods. In this method, each component model of the dynamic nonlinear gas turbine model is decomposed into several sub-modules, each of which contains a simple nonlinear equation. By means of symbolic computation, a linear model of the components is derived by linearizing these sub-modules, and then the generalized linear state-space model of the gas turbine is derived from the relationship among the components. In the generalized linear state-space model, the Jacobian matrices are functions of the parameters under a steady-state operating condition. Therefore, it is easy to obtain a linear model that represents the dynamics of the gas turbine under a given operating condition. To estimate the health parameters of a gas turbine, a piecewise linear model is developed using the proposed approach, and this model is verified in a simulation environment. The results show that the developed piecewise linear model can capture the behavior of a gas turbine quite closely. Then, a linearized Kalman filter is designed for estimating the health parameters under steady-state and transient conditions. The results show that the generalized linear model established using the presented method can be used to accurately estimate the health parameters of a gas turbine.

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

Optimal State-Space Control of a Gas Turbine Engine

1992 ◽  
Vol 114 (4) ◽  
pp. 763-767 ◽  
Author(s):  
J. W. Watts ◽  
T. E. Dwan ◽  
C. G. Brockus
Keyword(s):  
State Space ◽  
Gas Turbine ◽  
State Space Model ◽  
Gas Turbine Engine ◽  
The State ◽  
Operating Conditions ◽  
Turbine Engine ◽  
Analog Control ◽  
Linear State ◽  
High Level

An analog fuel control for a gas turbine engine was compared with several state-space derived fuel controls. A single-spool, simple cycle gas turbine engine was modeled using ACSL (high level simulation language based on FORTRAN). The model included an analog fuel control representative of existing commercial fuel controls. The ACSL model was stripped of nonessential states to produce an eight-state linear state-space model of the engine. The A, B, and C matrices, derived from rated operating conditions, were used to obtain feedback control gains by the following methods: (1) state feedback; (2) LQR theory; (3) Bellman method; and (4) polygonal search. An off-load transient followed by an on-load transient was run for each of these fuel controls. The transient curves obtained were used to compare the state-space fuel controls with the analog fuel control. The state-space fuel controls did better than the analog control.

scholarly journals Gaussian linear state-space model for wind fields in the North-East Atlantic

2014 ◽  
Vol 26 (1) ◽  
pp. 29-38 ◽  
Author(s):  
J. Bessac ◽  
P. Ailliot ◽  
V. Monbet
Keyword(s):  
State Space ◽  
State Space Model ◽  
Wind Fields ◽  
East Atlantic ◽  
Space Model ◽  
North East ◽  
The North ◽  
Linear State

A state space model approach for HIV infection dynamics

2012 ◽  
Vol 33 (5) ◽  
pp. 841-849 ◽  
Author(s):  
Jiabin Wang ◽  
Hua Liang ◽  
Rong Chen
Keyword(s):  
Hiv Infection ◽  
State Space ◽  
State Space Model ◽  
Space Model ◽  
Infection Dynamics ◽  
Model Approach

scholarly journals Evaluating somatic cell scores with a Bayesian Gaussian linear state-space model

animal ◽  
2014 ◽  
Vol 8 (3) ◽  
pp. 477-483
Author(s):  
J. Detilleux ◽  
L. Theron ◽  
E. Reding ◽  
C. Bertozzi ◽  
C. Hanzen
Keyword(s):  
Somatic Cell ◽  
State Space ◽  
State Space Model ◽  
Space Model ◽  
Linear State
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