Fault diagnosis in gas turbine engines using fuzzy logic

2004 ◽  
Author(s):  
D. Gayme ◽  
S. Menon ◽  
C. Ball ◽  
D. Mukavetz ◽  
E. Nwadiogbu
Keyword(s):  
Fuzzy Logic ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Turbine Engines ◽  
Gas Turbine Engines

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.

scholarly journals Інтелектуальний регулятор запасу газодинамічної стійкості компресора авіаційного ГТД

2021 ◽  
pp. 48-52
Author(s):  
Сергій Васильович Єнчев ◽  
Сергій Олегович Таку
Keyword(s):  
Fuzzy Logic ◽  
Nonlinear Systems ◽  
Fuzzy Control ◽  
Control Systems ◽  
Gas Turbine ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Turbine Engine ◽  
Harmonic Linearization ◽  
Gas Dynamic

The gas-dynamic stability of compressors of aircraft gas turbine engines is one of the most important conditions that determine their reliability and level of flight safety. Unstable operation of the compressor in the engine system (surge) leads to loss of thrust accompanied by an increase in gas temperature in front of the turbine and increased vibration because of large amplitudes of pressure pulsations and mass flow through the engine path. To improve the parameters of ACS aviation gas turbine engines are increasingly using regulators built using fuzzy logic algorithms. The implementation of fuzzy control algorithms differs from classical algorithms, which are based on the concept of feedback and reproduce a given functional dependence or differential equation. These functions are related to the qualitative assessment of system behavior, analysis of the current changing situation, and the selection of the most appropriate for the situation supervision of the gas turbine engine. This concept is called advanced management. ACS gas turbine engines with fuzzy regulators are nonlinear systems in which stable self-oscillations are possible. Approximate methods are used to solve the problems of analysis of periodic oscillations in nonlinear systems. Among them, the most developed in theoretical and methodological aspects is the method of harmonic linearization. The scientific problem is solved in the work – methods of synthesis of intelligent control system with the fuzzy regulator as a separate subsystem based on the method of harmonic linearization and design on its basis of fuzzy ACS reserve of gas-dynamic stability of aviation gas turbine engine. Based on the analysis of the principles of construction of fuzzy control systems, it is shown that the use of fuzzy logic provides a new approach to the design of control systems for aviation gas turbine engines in contrast to traditional control methods. It is shown that the fuzzy controller, as the only essentially nonlinear element when using numerical integration methods, can be harmonically linearized. Harmonic linearization allows using the oscillation index to assess the quality in the separate channels of fuzzy ACS aviation gas turbine engines. A fuzzy expert system has been developed for optimal adjustment of the functions of belonging of typical fuzzy regulators according to quality criteria to transients.

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 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.

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
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