A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature

Abstract

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.

Forecasting Property Price Indices in Hong Kong Based on Grey Models

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Study on Transition of Primary Energy Structure and Carbon Emission Reduction Targets in China Based on Markov Chain Model and GM (1, 1)

“…And the modeling process of the simple linear regression model can refer to the relevant literature [24]. [25][26][27][28]. A Markov chain can be expressed as X = X( ), = 0, 1, 2, .…”

A Dynamic Adaptive Failure Detection Algorithm based on Grey System Theory

“…. Grey dynamic prediction model establishment: The major process of the grey prediction is the establishment of GM(1.1) prediction model [5][6][7], namely, to establish a first-order differential equation for the array 1 t ( ) .…”

“…(1) (1) (1) t can be obtained by equation (3)(4)(5)(6)(7)(8)(9).Assume that (0) t is the proper prediction array, then:…”

“…AGO. Obtain the n+1 prediction value by (3)(4)(5)(6)(7)(8)(9). After getting the n+1th actual heartbeat message, make an improvement of the GM 1.1…”