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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Main Authors:	Ning-bo Zhao, Jia-long Yang, Shu-ying Li, Yue-wu Sun
Format:	Article
Language:	English
Published:	Hindawi Limited 2014-01-01
Series:	Mathematical Problems in Engineering
Online Access:	http://dx.doi.org/10.1155/2014/832851

id	doaj-262ae4b58a874d88a4eb05192908be6e
record_format	Article
spelling	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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A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature

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