CAJ | 학술논문

提出了一种航空发动机喘振故障检测的神经网络免疫识别模型.该模型利用人工免疫系统的反面选择原理来构建神经网络检测器,通过训练将失速压力信号的模式特征存储在分布的检测器中.检测器用于捕获信号的失速模式特征,当检测器与特征样本匹配时则激活该检测器,根据检测器的激活情况来发现失速点.对某型涡喷发动机压力测量信号的分析结果表明,该方法对由失速气团造成的压力信号突变具有较强的分辨力,可以用于发动机喘振的早期检测.
제출료일충항공발동궤천진고장검측적신경망락면역식별모형.해모형이용인공면역계통적반면선택원리래구건신경망락검측기,통과훈련장실속압력신호적모식특정존저재분포적검측기중.검측기용우포획신호적실속모식특정,당검측기여특정양본필배시칙격활해검측기,근거검측기적격활정황래발현실속점.대모형와분발동궤압력측량신호적분석결과표명,해방법대유실속기단조성적압력신호돌변구유교강적분변력,가이용우발동궤천진적조기검측.
An immune recognition model based on neural network for aeroengine surge detection was proposed.In this model, neural network-based detectors were constructed based on negative selection principle of an artificial immune system.Through neural network training, the pattern features of stall pressure signal were stored in distributed neural network-based detectors.These detectors were used to capture the stall pattern features.When a detector was matched up with a feature sample, the detector was activated.A stall point could be found out through the relevant activated detectors.When applied to a certain type of turbo engine, the result showed that the proposed model has high resolution for locations of pressure signal singularities, which are caused by stall air mass in the compressor; this method can be used in the early detection of surge signal.