A highly accurate method for forecasting the compressor geometric variable system based on the data-driven method
Author:
Xia Cunjiang,
Zhan YuyouORCID,
Tan Yan,
Gou Yi,
Wu Wenqing
Abstract
To make the puzzle of aero-engines complete, understanding the law of the compressor geometric variable system is a vital part. Modeling all aspects of aero-engines quickly has been a continuous area of research since the advent of artificial intelligence (AI). However, diagnosing or predicting faults is an ancient adage, and it is vital to explore key system forecast research, particularly since traditional forecasting techniques do not account for future information of non-target parameters. In this article, based on the feasibility of forecasting the compressor geometric variable system, an enhanced ConvNeXt model utilizing the Sliding Window Algorithm mechanism is proposed. And this method takes into account the future information of non-target parameters. With the novel strategy, the issue of the forecast’s error increasing with forecast length is alleviated. As a result, in a particular condition, the error we obtained only accounts for 20.07% of that of the standard forecast approach. Additionally, it is verified that this method can be applied to various aero-engines. Finally, experiments on several aero-engine states involving the transition state and the steady state are conducted to strengthen the plausibility and credibility of our theories. It should be noted that the foundation of each experiment is data from actual flights.
Funder
the Key R & D plan of Sichuan Provincial Department of science and Technology
the Key R & D plan of Tibet Science and Technology Department
CAAC education and training program
the Fundamental Research Funds for the Central Universities
Publisher
Public Library of Science (PLoS)
Subject
Multidisciplinary
Reference22 articles.
1. Digital twin–driven aero-engine intelligent predictive maintenance;Minglan Xiong;The International Journal of Advanced Manufacturing Technology 114
2. Axial compressor stall phenomena;E. M. Greitzer,1980
3. Numerical optimization of a stator vane setting in multistage axial-flow compressors;J. Sun;Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 21
4. Numerical optimization for stator vane settings of multi-stage compressors based on neural networks and genetic algorithms;Bo Li;Aerospace Science and Technology