Aircraft engine hot-section virtual sensor creation and gas path performance monitoring

2021 ◽  
pp. 095441002110247
Author(s):  
Feng Lu ◽  
Peng Jin ◽  
Jinquan Huang ◽  
Chen Wang ◽  
Haiqin Qin
Keyword(s):  
Kalman Filter ◽  
Performance Monitoring ◽  
Unscented Kalman Filter ◽  
Hypothesis Test ◽  
Complex Structure ◽  
Aircraft Engine ◽  
Performance Estimation ◽  
Physical Parameters ◽  
Aircraft Engines ◽  
Virtual Sensor

Various Kalman filter approaches have been presented for performance estimation of aircraft engines provided that sensor measurement numbers are sufficient and all of them are available. However, it is difficult to collect all physical parameters along the gas path since the complex structure limits sensor installation, especially around the high-pressure turbine. The contribution of this article is to create a virtual sensor in the hot-section based on the component physical characteristics and aerothermodynamic theory and develop a dual hypothesis test strategy combined with a state estimator to track abrupt degradation of the engine component. A novel couple algorithm of the unscented Kalman filter using a virtual sensor is developed that has three primary advantages: (i) The performance degradation of four rotatory components can be estimated without sensor P43. (ii) The accuracy of state estimation using the virtual sensor is equivalent to that of sensor P43, and it adapts to the abrupt change of engine operating condition in the whole flight envelope. (iii) If the sensor can be installed in the future, it can be engaged to its analytical redundancy. Simulations are carried out on typical abrupt degradation datasets of aircraft engines, which demonstrate the superiority of anomaly detection of gas path component performance using virtual sensor measurements, especially hot-section components.

scholarly journals Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

2009 ◽  
Vol 132 (3) ◽  
Author(s):  
Donald L. Simon ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Experimental Simulation ◽  
Tuning Parameter ◽  
Estimation Accuracy ◽  
On Line

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

scholarly journals An Integrated Approach for Aircraft Engine Performance Estimation and Fault Diagnostics

2013 ◽  
Vol 135 (7) ◽  
Author(s):  
Donald L. Simon ◽  
Jeffrey B. Armstrong
Keyword(s):  
Kalman Filter ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Fault Isolation ◽  
Performance Estimation ◽  
Estimation Accuracy ◽  
Fault Diagnostics ◽  
Tuning Parameters ◽  
Performance Deterioration ◽  
Health Parameters

A Kalman filter-based approach for integrated on-line aircraft engine performance estimation and gas path fault diagnostics is presented. This technique is specifically designed for underdetermined estimation problems where there are more unknown system parameters representing deterioration and faults than available sensor measurements. A previously developed methodology is applied to optimally design a Kalman filter to estimate a vector of tuning parameters, appropriately sized to enable estimation. The estimated tuning parameters can then be transformed into a larger vector of health parameters representing system performance deterioration and fault effects. The results of this study show that basing fault isolation decisions solely on the estimated health parameter vector does not provide ideal results. Furthermore, expanding the number of the health parameters to address additional gas path faults causes a decrease in the estimation accuracy of those health parameters representative of turbomachinery performance deterioration. However, improved fault isolation performance is demonstrated through direct analysis of the estimated tuning parameters produced by the Kalman filter. This was found to provide equivalent or superior accuracy compared to the conventional fault isolation approach based on the analysis of sensed engine outputs, while simplifying online implementation requirements. Results from the application of these techniques to an aircraft engine simulation are presented and discussed.

scholarly journals An Integrated Approach for Aircraft Engine Performance Estimation and Fault Diagnostics

2012 ◽  
Author(s):  
Donald L. Simon ◽  
Jeffrey B. Armstrong
Keyword(s):  
Kalman Filter ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Fault Isolation ◽  
Performance Estimation ◽  
Estimation Accuracy ◽  
Fault Diagnostics ◽  
Tuning Parameters ◽  
Performance Deterioration ◽  
Health Parameters

A Kalman filter-based approach for integrated on-line aircraft engine performance estimation and gas path fault diagnostics is presented. This technique is specifically designed for underdetermined estimation problems where there are more unknown system parameters representing deterioration and faults than available sensor measurements. A previously developed methodology is applied to optimally design a Kalman filter to estimate a vector of tuning parameters, appropriately sized to enable estimation. The estimated tuning parameters can then be transformed into a larger vector of health parameters representing system performance deterioration and fault effects. The results of this study show that basing fault isolation decisions solely on the estimated health parameter vector does not provide ideal results. Furthermore, expanding the number of the health parameters to address additional gas path faults causes a decrease in the estimation accuracy of those health parameters representative of turbomachinery performance deterioration. However, improved fault isolation performance is demonstrated through direct analysis of the estimated tuning parameters produced by the Kalman filter. This was found to provide equivalent or superior accuracy compared to the conventional fault isolation approach based on the analysis of sensed engine outputs, while simplifying online implementation requirements. Results from the application of these techniques to an aircraft engine simulation are presented and discussed.

scholarly journals Application of an Optimal Tuner Selection Approach for On-Board Self-Tuning Engine Models

2011 ◽  
Author(s):  
Donald L. Simon ◽  
Jeffrey B. Armstrong ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Closed Loop ◽  
Estimation Error ◽  
Aircraft Engine ◽  
Performance Estimation ◽  
Tuning Parameter ◽  
Parameter Vector ◽  
Engine Operation ◽  
Selection Approach ◽  
On Line

An enhanced design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented in this paper. It specifically addresses the underdetermined estimation problem, in which there are more unknown parameters than available sensor measurements. This work builds upon an existing technique for systematically selecting a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. While the existing technique was optimized for open-loop engine operation at a fixed design point, in this paper an alternative formulation is presented that enables the technique to be optimized for an engine operating under closed-loop control throughout the flight envelope. The theoretical Kalman filter mean squared estimation error at a steady-state closed-loop operating point is derived, and the tuner selection approach applied to minimize this error is discussed. A technique for constructing a globally optimal tuning parameter vector, which enables full-envelope application of the technology, is also presented, along with design steps for adjusting the dynamic response of the Kalman filter state estimates. Results from the application of the technique to linear and nonlinear aircraft engine simulations are presented and compared to the conventional approach of tuner selection. The new methodology is shown to yield a significant improvement in on-line Kalman filter estimation accuracy.

Application of Improved Extended Kalman Filter for Adaptive Model of Aircraft Engine

2014 ◽  
Vol 709 ◽  
pp. 180-185
Author(s):  
Gu Ting Zhou ◽  
San Mai Su
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Extended Kalman Filter ◽  
Performance Monitoring ◽  
Aircraft Engine ◽  
Filter Method ◽  
Adaptive Model ◽  
Nominal Model ◽  
Simulation Results ◽  
Kalman Filter Method

Adaptive model is the basis of engine fault diagnosis, performance monitoring, engine control, etc. This paper presents an improved kalman filter method which uses engine measurable parameters deviation to estimate the degradation parameters to correct the nominal model, and the acquisition and application of multiple kalman filter gain matrices in the whole flight envelope is analyzed. Simulation is carried out taking a civil engine as simulation object, the simulation results show that the method used in this paper to establish unmeasured parameters adaptive model can get the engine parameters better.

scholarly journals Application of an Optimal Tuner Selection Approach for On-Board Self-Tuning Engine Models

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Donald L. Simon ◽  
Jeffrey B. Armstrong ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Closed Loop ◽  
Estimation Error ◽  
Aircraft Engine ◽  
Performance Estimation ◽  
Tuning Parameter ◽  
Parameter Vector ◽  
Engine Operation ◽  
Selection Approach ◽  
On Line

An enhanced design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented in this paper. It specifically addresses the under-determined estimation problem, in which there are more unknown parameters than available sensor measurements. This work builds upon an existing technique for systematically selecting a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. While the existing technique was optimized for open-loop engine operation at a fixed design point, in this paper an alternative formulation is presented that enables the technique to be optimized for an engine operating under closed-loop control throughout the flight envelope. The theoretical Kalman filter mean squared estimation error at a steady-state closed-loop operating point is derived, and the tuner selection approach applied to minimize this error is discussed. A technique for constructing a globally optimal tuning parameter vector, which enables full-envelope application of the technology, is also presented, along with design steps for adjusting the dynamic response of the Kalman filter state estimates. Results from the application of the technique to linear and nonlinear aircraft engine simulations are presented and compared to the conventional approach of tuner selection. The new methodology is shown to yield a significant improvement in on-line Kalman filter estimation accuracy.

scholarly journals A Generalized Likelihood Ratio Test for Adaptive Gas Turbine Performance Monitoring

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
S. Borguet ◽  
O. Léonard
Keyword(s):  
Kalman Filter ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Performance Monitoring ◽  
Aircraft Engine ◽  
Generalized Likelihood Ratio Test ◽  
Ratio Test ◽  
Secondary System ◽  
Generalized Likelihood Ratio ◽  
Auxiliary Component

Kalman filters are widely used in the turbine engine community for health monitoring purposes. This algorithm has proven its capability to track gradual deterioration with good accuracy. On the other hand, its response to rapid deterioration is a long delay in recognizing the fault and/or a spread of the estimated fault on several components. The main reason for this deficiency lies in the transition model of the parameters that is blended in the Kalman filter and assumes a smooth evolution of the engine condition. This contribution reports the development of an adaptive diagnosis tool that combines a Kalman filter and a secondary system that monitors the residuals. This auxiliary component implements a generalized likelihood ratio test in order to detect and estimate an abrupt fault. The enhancement in terms of accuracy and reactivity brought by this adaptive Kalman filter is highlighted for a variety of simulated fault cases that may be encountered on a commercial aircraft engine.

scholarly journals Application of a Constant Gain Extended Kalman Filter for In-Flight Estimation of Aircraft Engine Performance Parameters

2005 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon ◽  
Jonathan S. Litt
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Accurate Estimation ◽  
Aircraft Engines ◽  
Performance Parameters ◽  
Efficient Manner ◽  
Engine Operation ◽  
Engine Model

An approach based on the Constant Gain Extended Kalman Filter (CGEKF) technique is investigated for the in-flight estimation of non-measurable performance parameters of aircraft engines. Performance parameters, such as thrust and stall margins, provide crucial information for operating an aircraft engine in a safe and efficient manner, but they can not be directly measured during flight. A technique to accurately estimate these parameters is, therefore, essential for further enhancement of engine operation. In this paper, a CGEKF is developed by combining an on-board engine model and a single Kalman gain matrix. In order to make the on-board engine model adaptive to the real engine’s performance variations due to degradation or anomalies, the CGEKF is designed with the ability to adjust its performance through the adjustment of artificial parameters called “tuning parameters.” With this design approach, the CGEKF can maintain accurate estimation performance when it is applied to aircraft engines at off-nominal conditions. The performance of the CGEKF is evaluated in a simulation environment using numerous component degradation and fault scenarios at multiple operating conditions.

scholarly journals Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

2009 ◽  
Author(s):  
Donald L. Simon ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Experimental Simulation ◽  
Tuning Parameter ◽  
Estimation Accuracy ◽  
On Line

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

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