On Real-Time Markov Simulation for Gas Turbine Engine Condition Monitoring

1998 ◽  
Vol 31 (4) ◽  
pp. 161-165
Author(s):  
G.G. Kulikov ◽  
T.V. Breikin ◽  
V.Y. Arkov ◽  
P.J. Fleming
Keyword(s):  
Real Time ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Gas Turbine Engine ◽  
Turbine Engine ◽  
Engine Condition

Development of Condition Monitoring Test Cell Using Micro Gas Turbine Engine

2009 ◽  
Author(s):  
Seonghee Kho ◽  
Jayoung Ki ◽  
Miyoung Park ◽  
Changduk Kong ◽  
Kyungjae Lee
Keyword(s):  
Performance Analysis ◽  
Real Time ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Gas Turbine Engine ◽  
Operating Conditions ◽  
Test Cell ◽  
Turbine Engine ◽  
Time Performance ◽  
Engine Condition

This study is aim to be programmed the simulation which is available for real-time performance analysis so that is to be developed gas turbine engine’s condition monitoring system with analyzing difference between performance analysis results and measuring data from test cell. In addition, test cell created by this study have been developed to use following applications: to use for learning principals and mechanism of gas turbine engine in school, and to use performance test and its further research for variable operating conditions in associated institutes. The maximum thrust of the micro turbojet engine is 137 N (14 kgf) at 126,000 rpm of rotor rotational speed if the Jet A1 kerosene fuel is used. The air flow rate is measured by the inflow air speed of duct, and the fuel flow is measured by a volumetric fuel flowmeter. Temperatures and pressures are measured at the atmosphere, the compressor inlet and outlet and the turbine outlet. The thrust stand was designed and manufactured to measure accurately the thrust by the load cell. All measuring sensors are connected to a DAQ (Data Acquisition) device, and the logging data are used as function parameters of the program, LabVIEW. The LabVIEW is used to develop the engine condition monitoring program. The proposed program can perform both the reference engine model performance analysis at an input condition and the real-time performance analysis with real-time variables. By comparing two analysis results the engine condition can be monitored. Both engine performance analysis data and monitoring results are displayed by the GUI (Graphic User Interface) platform.

Inverse Generation of Gas Turbine Component Performance Maps From Experimental Test Data

2010 ◽  
Author(s):  
Changduk Kong ◽  
Semyeong Lim ◽  
Seonghwan Oh ◽  
Jihyun Kim
Keyword(s):  
Real Time ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Test Data ◽  
Performance Test ◽  
Monitoring Program ◽  
Engine Performance ◽  
Gas Turbine Engine ◽  
Performance Data ◽  
Turbine Engine

The gas turbine engine performance is greatly relied on its component performance characteristics. Generally, acquisition of component maps is not easy for engine purchasers because it is an intellectual property of gas turbine engine supplier. In the previous work, the maps were inversely generated from engine performance deck data. However this method is limited to obtain the realistic maps from the calculated performance deck data. Present work proposes a novel method to generate more realistic component maps from experimental performance test data. In order to demonstrate the proposed method, firstly the NI data acquisition device with the proposed LabVIEW on-condition monitoring program monitors and collects real-time performance data such as temperature, pressure, thrust, and fuel flow etc. from a micro turbojet engine of the test setup which is specially manufactured for this study. Real-time data obtained from the test results are used for inverse generation of the component maps after processing by some numerical schemes. Realistic component maps can then be generated from those processed data using the proposed extended scaling method at each rotational speed. Verification can be made through comparison between performance analysis results using the performance simulation program including the generated compressor map and on-condition monitoring performance data.

On Gas Turbine Engine and Control System Condition Monitoring

1997 ◽  
Vol 30 (18) ◽  
pp. 67-71 ◽  
Author(s):  
Timofei Breikin ◽  
Valentin Arkov ◽  
Gennady Kulikov ◽  
Visakan Kadirkamanathan ◽  
Vijay Patel
Keyword(s):  
Control System ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Gas Turbine Engine ◽  
Turbine Engine ◽  
And Control

Auto-Tuning of Real-Time Dynamic Gas Turbine Models

2014 ◽  
Author(s):  
V. Panov
Keyword(s):  
Real Time ◽  
Gas Turbine ◽  
Gas Turbine Engine ◽  
Model Adaptation ◽  
Transient Operation ◽  
Turbine Engine ◽  
Time Dynamic ◽  
Performance Tracking ◽  
Auto Tuning ◽  
Health Parameters

Real-time gas turbine engine models are integral part of techniques such as model-based control and diagnostics. The use of model based techniques to diagnose and adaptively manage degradation of engine components is crucial for operational effectiveness of gas turbines. Since the gas turbine model represents “nominal” engine, it must be adapted or tuned to the performance of the real engine as it deviates from nominal baseline. Implementation of a method for auto-tuning of dynamic gas turbine engine models was considered in this paper, and a non-linear physics based component level model was facilitated as an on-line gas turbine model. Real-time nonlinear dynamic model of an industrial twin-shaft gas turbine with tracking filter was deployed onto the dedicated hardware platform and integrated with the engine control system. In presented application the identified gas turbine health parameters were obtained by the performance estimation tool and included in the observer design. The designed observer detects changes in the engine health parameters and generates model tuners. The model tuning process based on Kalman filtering technique was applied to secure robust execution of real-time dynamic models. Proposed auto-tuning methodology provides a tool for model adaptation, capable of addressing abrupt and gradual degradation of engine performance and at the same time offers a means for model compensation of performance deviation caused by engine-to-engine variation. Although most of performance tracking and diagnostic methods are developed for gas turbine operating at steady state, current trend demonstrates increasing interest in diagnostics during transient operation. Devised method estimates dynamic behaviour of gas turbine health parameters enabling in that way performance tracking under transient conditions. Examples of model adaptation during gas turbine engine transient operation are given in the paper.

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