Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective

Sun, Junjie; Yan, Zichen; Ying, Han; Lee, Seonjoo; Yang, Caiqiong

doi:10.1016/j.ress.2023.109404

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…A complete life cycle of DT technology contains five parts: design, experiment, manufacturing, operation and maintenance, and recycle [4] . Many researchers [5,6,7] followed and developed this concept in the field of performance monitoring. Minghui HU [5] et al established LM2500 + performance model by fusing the mechanism model and measured data.…”

Section: Introductionmentioning

confidence: 99%

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Design and application of the gas turbine performance monitoring system based on digital twins

Zhang,

Wang

2024

Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024)

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The performance monitoring of gas turbine is an important global issue that is related to the stable and safe operation of gas turbine equipment. With the development of gas turbines, the complexity of its structure and the harsh conditions of high temperature and high pressure have brought many difficulties to measure. To a certain extent, it is impossible to measure even at the exit of the burning room or the high -voltage turbine inlet, which leads to how to use these available parameters to ensure the health state of the gas turbine. The emergence of digital twin technology provides a feasible way for the health monitoring of gas turbine, which has set off a wave in the field of gas turbine's health monitoring. Digital twin is a numerical copy based on physical data. It can truly reflect effective information and have the ability to self -correct and predict the future. This article focuses on the logical framework of the performance monitoring operation of the gas turbine, builds the calculation module and optimization function of the functional component of the gas turbine, and verified it through experiments. The results show that the system can be used for the construction of the digital twin model of the gas turbine, which has certain engineering significance for future performance monitoring.

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Section: Introductionmentioning

confidence: 99%

“…Zaccaria [6] et al applied DT technology to health monitoring and diagnostics of aero-engines. In recent, Jianzhong Sun [7] et al proposed a semi-supervised deep learning method for constructing DT model from the perspective of data-driven and successfully validated this method on turbofan engines in real world.…”

Section: Introductionmentioning

confidence: 99%