改进BA优化的MKSVDD航空发动机工作状态识别

Aero-engine working condition recognition based on MKSVDD optimized by improved BA

为了提高航空发动机工作状态识别准确率和效率,避免人工识别中存在的误判和耗时耗力问题,提出了基于混沌脉冲蝙蝠算法(CRBA)优化的多核支持向量数据描述(CRBA-MKSVDD)智能识别方法。研究了多核支持向量数据描述(MKSVDD)改进策略,引入混沌脉冲发射率提高了蝙蝠算法(BA)的收敛速度和收敛精度,得到了CRBA;通过CRBA优化MKSVDD的惩罚因子和核参数,同时对飞参数据进行了特征提取;基于特征飞参数据训练了CRBA-MKSVDD分类器,并对某型发动机一个飞行架次的工作状态进行了识别。结果表明,该方法识别准确率达到97.547 9%,可用于与发动机工作状态的相关研究和应用。

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.