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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature
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A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature

机译：基于GM（1,1）马尔可夫链的航空发动机性能降解预测方法使用废气温度

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摘要

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.
机译：使用废气温度定量评估航空发动机降解状态的性能降解预测技术是航空发动机健康管理中的重要技术。在本文中，引入了GM（1,1）马尔可夫链的方法，以通过在时间序列中的优势和Markov链模型在处理高度非线性方面的优势来预测废气温度由不确定因素引起的随机数据。在这种方法中，首先，GM（1,1）模型用于通过使用有限的数据样本来预测趋势。然后，马尔可夫链模型集成到GM（1,1）模型中，以提高预测性能，可以解决随机波动数据对预测准确性的影响和实现非线性预测的准确估计。作为一个例子，中国南方CFM56航空发动机的废气温度的历史监测数据用于验证GM（1,1）马尔可夫链模型的预测性能。结果表明，GM（1,1）马尔可夫链模型能够精确地预测废气温度，这可以有效地反映随着时间的推移随着时间的推移随着时间的推移的随机波动特性。

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