Propulsion Health Management System Development for Affordable and Reliable Operation of Space Exploration Systems

Melcher, Kevin J.; Maul, William A.; Garg, Sanjay

doi:10.2514/6.2007-6237

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The hybrid prediction method can not only fully absorb the advantages of the respective methods, but also make up for their respective shortcomings. Therefore, it is an inevitable trend to predict the development of technology Advances in Computer Science Research, volume 86 by combining various different prediction methods and further improving the comprehensive performance of prediction [16][17][18].…”

Section: (3)prediction Based On the Datamentioning

confidence: 99%

Developments of EHM Under the Conditions of Big Data Technology

Cai¹,

Liu²,

Wang³

et al. 2018

Proceedings of the 2018 International Symposium on Communication Engineering &Amp; Computer Science (CECS 2018)

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This paper explores the role of big data technology development in the promotion of aero engine health management (EHM) technology. Firstly, the research and development history of EHM is elaborated in detail, and then the key technologies for EHM research are analyzed. Finally, according to the existing difficulties and technical problems, the method of combining computer science engineering and aerospace science and technology is proposed to improve Chinese aviation EHM technology.

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Section: (3)prediction Based On the Datamentioning

confidence: 99%

Developments of EHM Under the Conditions of Big Data Technology

Cai¹,

Liu²,

Wang³

et al. 2018

Proceedings of the 2018 International Symposium on Communication Engineering &Amp; Computer Science (CECS 2018)

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“…Prognostic and health management for aeroengine are the main concerns for many researchers and users in order to provide more useful information for the safe operation [1,2]. Performance degradation forecast technology for assessing degradation states quantitatively based on gas path performance parameters is one of the most important technologies, which can improve the safety, reliability, and maintenance of aeroengine [3,4]. Therefore, how to improve the forecast precision by the advanced condition monitoring techniques to extract hidden, unknown, and the useful information from large amounts of monitoring data is emphasized on in the study of performance degradation forecast.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Yang

et al. 2014

Mathematical Problems in Engineering

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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.

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