| Citation: | CAO Huiling, KAN Yuxiang, XUE Penget al. Exploration of engine VSV regulation law using support vector regression[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(7): 1371-1377. doi: 10.13700/j.bh.1001-5965.2017.0523(in Chinese)
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The engine variable stator vane (VSV) regulation law is extremely complex, and through mining quick access recorder (QAR) data, the VSV regulation law is studied. Firstly, the support vector regre-ssion (SVR) model based on particle swarm optimization (PSO) is established through the QAR data of PW4077D engine health condition to explore the regulation law of VSV. Then, the PSO-SVR model is validated by the subsequent flight data, and the verification results are compared with the traditional PSO-BP neural network model. Finally, the PSO-SVR model is applied to engine fault diagnosis. The results show that the regression prediction accuracy of the PSO-SVR model is better than that of the PSO-BP neural network model, and it can accurately reflect the VSV regulation rule. It can be used in the condition monitoring and fault dia-gnosis of engine, and can also provide reference for the design of VSV control system.
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