Forecasting gas turbine Exhaust Gas Temperatures using Support Vector Machine Experts and Genetic Algorithm

Abstract

One aspect of modern commercial aircraft engine maintenance involves monitoring recorded engine parameters. When these parameters exceed their respective threshold tolerances, appropriate maintenance actions are taken. Reducing these unscheduled maintenance actions would allow maintainers to more effectively plan their maintenance schedules which help in the reduction of costs. One way of accomplishing this is to learn the behavior of the statistics of parameters and the ability to reliably forecast their future values. The parameter studied in this paper is the Exhaust Gas Temperature. A hybrid algorithm called Support Vector Machine Experts and Genetic Algorithm is applied to the task of forecasting a statistic of EGT. The algorithm involves first partitioning the input space according to similar training examples through a procedure that uses a Self Organizing Map. A SVM is regressed onto each partition and the optimal parameters for SVM are found using Genetic Algorithm. This algorithm is shown to have an improved mean absolute error when forecasting EGT values over a single support vector regression machine. The study was conducted for both engines of two Boeing 737 commercial aircrafts.978-1-4244-8092-0/10/$26.00 ©2010 IEEE

Selection of instances in Condition Based Monitoring: the case of aircraft engines

“…The followings works shows different approaches to aircraft engines: [8] in 1993 refers to the advantages of the application of neural networks in monitoring an aircraft engine's condition; [9] in 2005 describes a model which would be able as to predict the residual life of an aircraft engine, using the Principal Components Analyses (PCA) technique in oil data; in 2007 two works distinguished themselvesone about the diagnosis of engine failures [10], the other about advanced estimation in engines [11]; in 2009 more studies appeared about the parameters of aircraft engines and their extreme values [12] as well as forms of classifying their faults from the perspective of diagnosis [13]; the following year [14] studied one important aircraft engine parameter, Exhaust Gas Temperature (EGT). They used three different tools: Self Organizing Map, Vector Machine Experts and Genetic Algorithm.…”

Aero-engine exhaust gas temperature prediction based on adaptive disturbance quantum-behaved particle swarm optimization

“…Cay et al 21 adopted artificial neural network (ANN) modeling to predict the brake specific fuel consumption, effective power and average effective pressure and exhaust gas temperature of the methanol engine. Yukitomo and Syrmos 22 proposed a hybrid algorithm called SVM experts and GA (genetic algorithm) to forecast a statistic of EGT, these statistic of parameters and their future values would help maintainers to more effectively plan their maintenance schedules. Zhao et al 23 proposed a GM(1,1) markov chain-based approach to forecast exhaust gas temperature, and the historical monitoring data of exhaust gas temperature from CFM56 aero-engine of China southern is used to verify the forecast performance of the GM(1,1) by taking the advantage of GM(1,1) markov chain model.…”

Modelling of Fuel Flow in Climb Phase Through Multiple Linear Regression Based on the Data Collected by Quick Access Recorder

“…They estimated the performance of future flights by comparing the EGT values of two engines. Yukimoto and Syrmos [8] estimated the EGT value by using Support Vector Machine Expert Method and genetic algorithm methods. Anastassios [9] investigated the monitoring and diagnostic methods about gas turbine engines.…”