Advanced Diagnostic and Prognostic Technologies for Gas Turbine Engine Risk Assessment

2000 ◽  
Author(s):  
Michael J. Roemer ◽  
Gregory J. Kacprzynski
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Management Practices ◽  
Probabilistic Models ◽  
Gas Turbine Engine ◽  
Probabilistic Methods ◽  
Great Opportunity ◽  
Turbine Engine

Real-time, integrated health monitoring of gas turbine engines that can detect, classify, and predict developing engine faults is critical to reducing operating and maintenance costs while optimizing the life of critical engine components. Statistical-based anomaly detection algorithms, fault pattern recognition techniques and advanced probabilistic models for diagnosing structural, performance and vibration related faults and degradation can now be developed for real-time monitoring environments. Integration and implementation of these advanced technologies presents a great opportunity to significantly enhance current engine health monitoring capabilities and risk management practices. This paper describes some novel diagnostic and prognostic technologies for dedicated, real-time sensor analysis, performance anomaly detection and diagnosis, vibration fault detection, and component prognostics. The technologies have been developed for gas turbine engine health monitoring and prediction applications which includes an array of intelligent algorithms for assessing the total ‘health’ of an engine, both mechanically and thermodynamically. This includes the ability to account for uncertainties from engine transient conditions, random measurement fluctuations and modeling errors associated with model-based diagnostic and prognostic procedures. The implementation of probabilistic methods in the diagnostic and prognostic methodology is critical to accommodating for these types of uncertainties.

Life assessment of a high temperature probe designed for performance evaluation and health monitoring of an aero gas turbine engine

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Benny George ◽  
Nagalingam Muthuveerappan
Keyword(s):  
Performance Evaluation ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Gas Turbine Engine ◽  
Life Assessment ◽  
Exhaust Gas ◽  
Cycle Fatigue ◽  
Creep Life ◽  
Turbine Engine ◽  
Engine Components

AbstractTemperature probes of different designs were widely used in aero gas turbine engines for measurement of air and gas temperatures at various locations starting from inlet of fan to exhaust gas from the nozzle. Exhaust Gas Temperature (EGT) downstream of low pressure turbine is one of the key parameters in performance evaluation and digital engine control. The paper presents a holistic approach towards life assessment of a high temperature probe housing thermocouple sensors designed to measure EGT in an aero gas turbine engine. Stress and vibration analysis were carried out from mechanical integrity point of view and the same was evaluated in rig and on the engine. Application of 500 g load concept to clear the probe design was evolved. The design showed strength margin of more than 20% in terms of stress and vibratory loads. Coffin Manson criteria, Larsen Miller Parameter (LMP) were used to assess the Low Cycle Fatigue (LCF) and creep life while Goodman criteria was used to assess High Cycle Fatigue (HCF) margin. LCF and HCF are fatigue related damage from high frequency vibrations of engine components and from ground-air-ground engine cycles (zero-max-zero) respectively and both are of critical importance for ensuring structural integrity of engine components. The life estimation showed LCF life of more than 4000 mission reference cycles, infinite HCF life and well above 2000 h of creep life. This work had become an integral part of the health monitoring, performance evaluation as well as control system of the aero gas turbine engine.

Life assessment of a high temperature probe designed for performance evaluation and health monitoring of an aero gas turbine engine

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Benny George ◽  
Nagalingam Muthuveerappan
Keyword(s):  
Performance Evaluation ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Gas Turbine Engine ◽  
Life Assessment ◽  
Exhaust Gas ◽  
Cycle Fatigue ◽  
Creep Life ◽  
Turbine Engine ◽  
Engine Components

Abstract Temperature probes of different designs were widely used in aero gas turbine engines for measurement of air and gas temperatures at various locations starting from inlet of fan to exhaust gas from the nozzle. Exhaust Gas Temperature (EGT) downstream of low pressure turbine is one of the key parameters in performance evaluation and digital engine control. The paper presents a holistic approach towards life assessment of a high temperature probe housing thermocouple sensors designed to measure EGT in an aero gas turbine engine. Stress and vibration analysis were carried out from mechanical integrity point of view and the same was evaluated in rig and on the engine. Application of 500 g load concept to clear the probe design was evolved. The design showed strength margin of more than 20% in terms of stress and vibratory loads. Coffin Manson criteria, Larsen Miller Parameter (LMP) were used to assess the Low Cycle Fatigue (LCF) and creep life while Goodman criteria was used to assess High Cycle Fatigue (HCF) margin. LCF and HCF are fatigue related damage from high frequency vibrations of engine components and from ground-air-ground engine cycles (zero-max-zero) respectively and both are of critical importance for ensuring structural integrity of engine components. The life estimation showed LCF life of more than 4000 mission reference cycles, infinite HCF life and well above 2000 h of creep life. This work had become an integral part of the health monitoring, performance evaluation as well as control system of the aero gas turbine engine.

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.

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
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