A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature
Performance degradation forecast technology for quantitatively assessing degradation states of aeroengine using exhaust gas temperature is an important technology in the aeroengine health management. In this paper, a GM (1, 1) Markov chain-based approach is introduced to forecast exhaust gas tempera...
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2014-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2014/832851 |
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doaj-262ae4b58a874d88a4eb05192908be6e2020-11-24T21:59:03ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472014-01-01201410.1155/2014/832851832851A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas TemperatureNing-bo Zhao0Jia-long Yang1Shu-ying Li2Yue-wu Sun3College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, ChinaPerformance degradation forecast technology for quantitatively assessing degradation states of aeroengine using exhaust gas temperature is an important technology in the aeroengine health management. In this paper, a GM (1, 1) Markov chain-based approach is introduced to forecast exhaust gas temperature by taking the advantages of GM (1, 1) model in time series and the advantages of Markov chain model in dealing with highly nonlinear and stochastic data caused by uncertain factors. In this approach, firstly, the GM (1, 1) model is used to forecast the trend by using limited data samples. Then, Markov chain model is integrated into GM (1, 1) model in order to enhance the forecast performance, which can solve the influence of random fluctuation data on forecasting accuracy and achieving an accurate estimate of the nonlinear forecast. As an example, the historical monitoring data of exhaust gas temperature from CFM56 aeroengine of China Southern is used to verify the forecast performance of the GM (1, 1) Markov chain model. The results show that the GM (1, 1) Markov chain model is able to forecast exhaust gas temperature accurately, which can effectively reflect the random fluctuation characteristics of exhaust gas temperature changes over time.http://dx.doi.org/10.1155/2014/832851 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ning-bo Zhao Jia-long Yang Shu-ying Li Yue-wu Sun |
spellingShingle |
Ning-bo Zhao Jia-long Yang Shu-ying Li Yue-wu Sun A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature Mathematical Problems in Engineering |
author_facet |
Ning-bo Zhao Jia-long Yang Shu-ying Li Yue-wu Sun |
author_sort |
Ning-bo Zhao |
title |
A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature |
title_short |
A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature |
title_full |
A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature |
title_fullStr |
A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature |
title_full_unstemmed |
A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature |
title_sort |
gm (1, 1) markov chain-based aeroengine performance degradation forecast approach using exhaust gas temperature |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2014-01-01 |
description |
Performance degradation forecast technology for quantitatively assessing degradation states of aeroengine using exhaust gas temperature is an important technology in the aeroengine health management. In this paper, a GM (1, 1) Markov chain-based approach is introduced to forecast exhaust gas temperature by taking the advantages of GM (1, 1) model in time series and the advantages of Markov chain model in dealing with highly nonlinear and stochastic data caused by uncertain factors. In this approach, firstly, the GM (1, 1) model is used to forecast the trend by using limited data samples. Then, Markov chain model is integrated into GM (1, 1) model in order to enhance the forecast performance, which can solve the influence of random fluctuation data on forecasting accuracy and achieving an accurate estimate of the nonlinear forecast. As an example, the historical monitoring data of exhaust gas temperature from CFM56 aeroengine of China Southern is used to verify the forecast performance of the GM (1, 1) Markov chain model. The results show that the GM (1, 1) Markov chain model is able to forecast exhaust gas temperature accurately, which can effectively reflect the random fluctuation characteristics of exhaust gas temperature changes over time. |
url |
http://dx.doi.org/10.1155/2014/832851 |
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