Blade health monitoring of gas turbine using online crack detection

2017 ◽  
Author(s):  
Bai Liu ◽  
Longli Tang ◽  
Tong Liu ◽  
Zijian Liu ◽  
Kening Xu
Keyword(s):  
Gas Turbine ◽  
Health Monitoring ◽  
Crack Detection

Online Health Monitoring System Using Dynamic Structural Responses

2017 ◽  
Author(s):  
Amit K. Paspulati ◽  
Krishna C. Veluru ◽  
Kashinath Akki
Keyword(s):  
Gas Turbine ◽  
Monitoring System ◽  
Health Monitoring ◽  
Crack Detection ◽  
Field Measurements ◽  
Fatigue Loading ◽  
Flexible Pipe ◽  
Engine Operation ◽  
Health Monitoring System ◽  
Long Duration

This paper discusses the methodology for detection of impending failures in gas turbine components. Gas turbine components when exposed to fatigue loading are prone to crack initiation and propagation (structural failure) during operation. Cracks when left undetected over the long duration of continuous engine operation may lead to failure of gas turbine components and unplanned shutdown. This paper presents the crack detection methodology proposed to be only used in older engines maintained by the service fleet. This is done by capturing the changes in physical characteristics of cracked gas turbine structures during engine operation. Monitoring the change in vibration characteristics is being proposed as an input in developing the health monitoring system. In verifying the proposed technique, static component such as flexible pipe is analytically evaluated and validated with field measurements.

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.

Health Monitoring and Degradation Prognostics in Gas Turbine Engines Using Dynamic Neural Networks

2015 ◽  
Author(s):  
A. Vatani ◽  
K. Khorasani ◽  
N. Meskin
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Network Architecture ◽  
Information Criterion ◽  
Turbine Engines ◽  
Prediction Errors ◽  
Gas Turbine Engines ◽  
Dynamic Neural Networks ◽  
Turbine Engine

In this paper two artificially intelligent methodologies are proposed and developed for degradation prognosis and health monitoring of gas turbine engines. Our objective is to predict the degradation trends by studying their effects on the engine measurable parameters, such as the temperature, at critical points of the gas turbine engine. The first prognostic scheme is based on a recurrent neural network (RNN) architecture. This architecture enables ONE to learn the engine degradations from the available measurable data. The second prognostic scheme is based on a nonlinear auto-regressive with exogenous input (NARX) neural network architecture. It is shown that this network can be trained with fewer data points and the prediction errors are lower as compared to the RNN architecture. To manage prognostic and prediction uncertainties upper and lower threshold bounds are defined and obtained. Various scenarios and case studies are presented to illustrate and demonstrate the effectiveness of our proposed neural network-based prognostic approaches. To evaluate and compare the prediction results between our two proposed neural network schemes, a metric known as the normalized Akaike information criterion (NAIC) is utilized. A smaller NAIC shows a better, a more accurate and a more effective prediction outcome. The NAIC values are obtained for each case and the networks are compared relatively with one another.

Smart Combustors: Just Around the Corner

2005 ◽  
Author(s):  
Ben T. Zinn
Keyword(s):  
Control System ◽  
Control Systems ◽  
Gas Turbine ◽  
Active Control ◽  
Health Monitoring ◽  
State Of The Art ◽  
The State ◽  
Research Needs ◽  
Combustion Instabilities ◽  
Lean Blowout

This paper reviews the state of the art of active control systems (ACS) for gas turbine combustors. Specifically, it discusses the manner in which ACS can improve the performance of combustors, the architecture of such ACS, and the designs and promising performance of ACS that have been developed to control combustion instabilities, lean blowout and pattern factor. The paper closes with a discussion of research needs, with emphasis on the integration of utilized engine ACS, health monitoring and prognostication systems into a single control system that could survive in the harsh combustor environment.

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