| Citation: | HE Dawei, PENG Jingbo, HU Jinhai, et al. Aero-engine working condition recognition based on MKSVDD optimized by improved BA[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(10): 2238-2246. doi: 10.13700/j.bh.1001-5965.2017.0756(in Chinese)
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In order to ameliorate the accuracy and efficiency of aero-engine working condition identification, and to avoid the misjudgment and time-consuming problems in manual identification of aero-engine working condition, an intelligent recognition method, multi-kernel support vector data description based on chaotic rate bat algorithm (CRBA-MKSVDD), is proposed. The improved strategy of multi-kernel support vector data description (MKSVDD) is researched. The chaotic rate method is introduced to improve the convergence speed and convergence accuracy of the bat algorithm (BA), and the chaotic rate bat algorithm (CRBA) is obtained with this method. The penalty factor and kernel parameter of MKSVDD are optimized by CRBA and the characteristics of the flight parameters have been extracted. The CRBA-MKSVDD classifiers are trained based on the characteristics of flight parameters, and the working condition of a certain type of aero-engine in one sortie is identified by the proposed method. The results show that the accuracy of aero-engine working condition identified by the proposed method is 97.547 9%, which means that the method can be used in the research and application related to aero-engine working condition.
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