Fault Diagnosis of Gas Turbine Engines From Transient Data

1989 ◽  
Vol 111 (2) ◽  
pp. 237-243 ◽  
Author(s):  
G. L. Merrington
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Sampling Rate ◽  
Diagnostic Information ◽  
Measurement Noise ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Input Output ◽  
Fault Parameters ◽  
Transient Data

The desirability of being able to extract relevant fault diagnostic information from transient gas turbine data records is discussed. A method is outlined for estimating the effects of unmeasured fault parameters from input/output measurements. The resultant sensitivity of the technique depends on the sampling rate and the measurement noise.

Development of Fault Diagnosis and Failure Prediction Techniques for Small Gas Turbine Engines

2001 ◽  
Author(s):  
Craig R. Davison ◽  
A. M. Birk
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Operating Time ◽  
Turbine Engines ◽  
Time To Failure ◽  
Gas Turbine Engines ◽  
Ambient Conditions ◽  
Engine Operation ◽  
Maintenance Cycle ◽  
Prediction Techniques

A computer model of a gas turbine auxiliary power unit was produced to develop techniques for fault diagnosis and prediction of remaining life in small gas turbine engines. Due to the relatively low capital cost of small engines it is important that the techniques have both low capital and operating costs. Failing engine components were identified with fault maps, and an algorithm was developed for predicting the time to failure, based on the engine’s past operation. Simulating daily engine operation over a maintenance cycle tested the techniques for identification and prediction. The simulation included daily variations in ambient conditions, operating time, load, engine speed and operating environment, to determine the amount of degradation per day. The algorithm successfully adapted to the daily changes and corrected the operating point back to standard conditions to predict the time to failure.

Sensor fault diagnosis of gas turbine engines using an integrated scheme based on improved least squares support vector regression

2019 ◽  
Vol 234 (3) ◽  
pp. 607-623
Author(s):  
Yu Hu ◽  
Jietang Zhu ◽  
Zhensheng Sun ◽  
Lijia Gao
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Support Vector Regression ◽  
Gas Turbine ◽  
Turbine Engines ◽  
Support Vector ◽  
Gas Turbine Engines ◽  
Sensor Fault ◽  
Diagnosis System ◽  
Fault Diagnosis System

As the flight envelope is widening continuously and operational capability is improving sequentially, gas turbine engines are faced with new challenges of increased operation and maintenance requirements for efficiency, reliability, and safety. The measures for security and safety and the need for reducing the life cycle cost make it necessary to develop more accurate and efficient monitoring and diagnostic schemes for the health management of gas turbine components. Sensors along the gas path are one of the components in gas turbines that play a crucial role in turbofan engines owing to their safety criticality. Failures in sensor measurements often result in serious problems affecting flight safety and performance. Therefore, this study aims to develop an online diagnosis system for gas path sensor faults in a turbofan engine. The fault diagnosis system is designed and implemented using a genetic algorithm optimized recursive reduced least squares support vector regression algorithm. This method uses a reduction technique and recursion strategy to obtain a better generalization performance and sparseness, and exploits an improved genetic algorithm to choose the optimal model parameters for improving the training precision. The effectiveness of the sensor fault diagnosis system is then validated through typical fault modes of single and dual sensors.

Fault Diagnosis and Prognosis for Fuel Supply System in Gas Turbine Engines

2005 ◽  
Author(s):  
Kyusung Kim ◽  
Onder Uluyol ◽  
Charles Ball
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Supply System ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Fuel Supply ◽  
Diagnosis And Prognosis ◽  
Fuzzy Clustering Method ◽  
Fuel Supply System ◽  
Fault Diagnosis And Prognosis

A fault diagnosis and prognosis method is developed for the fuel supply system in gas turbine engines. The engine startup profiles of the core speed (N2) and the exhaust gas temperature (EGT) collected with high speed sampling rate are extracted and processed into a more compact data set. The fuzzy clustering method is applied to the smaller number of parameters and the fault is detected by differentiating the clusters matching the failures. In this work, the actual flight data collected in the field is used to develop and validate the system, and the results are shown for the test on nine engines that experienced fuel supply system failure. The developed fault diagnosis system detects the failure successfully for all nine cases. For the earliest detection cases, the alarms start to trigger 26 days before the system completely fails and 7 days in advance for the last detection.

Dynamic neural network-based fault diagnosis of gas turbine engines

2014 ◽  
Vol 125 ◽  
pp. 153-165 ◽  
Author(s):  
S. Sina Tayarani-Bathaie ◽  
Z.N. Sadough Vanini ◽  
K. Khorasani
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Dynamic Neural Network

Fault diagnosis and prognosis for fuel supply systems in gas turbine engines

2008 ◽  
Vol 223 (3) ◽  
pp. 757-768 ◽  
Author(s):  
K Kim
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Supply System ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Fuel Supply ◽  
Diagnosis And Prognosis ◽  
Fuzzy Clustering Method ◽  
Fuel Supply System ◽  
Fault Diagnosis And Prognosis

This paper introduces a feature-extraction method to characterize gas turbine engine dynamics. The extracted features are used to develop a fault diagnosis and prognosis method for the fuel supply system in gas turbine engines. The engine start-up profiles of the core speed (N2) and the exhaust gas temperature collected with high-speed sampling rate are obtained and processed into a more compact data set by identifying critical-to-characterization instances. The fuzzy-clustering method is applied to the smaller number of parameters, and the fault is detected by differentiating the clusters matching the failures. In this work, the actual flight data collected in the field was used to develop and validate the system, and the results are shown for the test on nine engines that experienced fuel supply system failure. The developed fault diagnosis system detected the failure successfully in all nine cases. For the earliest detection cases, the alarms start to trigger 26 days before the system completely fails and 7 days in advance for the last detection.

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