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针对航空发动机磨损故障诊断技术智能化、精确化的发展要求,以传统油液监测技术为基础,结合人工免疫系统(AIS)具有的自适应特性、学习记忆特性及识别特性等优点,提出了一种航空发动机磨损故障的智能诊断方法。该方法首先利用人工免疫理论的反面选择原理生成检测器,优化后的检测器生成算法提高了初始检测器的代表性及覆盖性;然后利用故障样本训练出成熟的检测器,使航空发动机典型的磨损状态信息存储在检测器中,实现对故障模式的有效学习和记忆;最后通过检测器的激活发现航空发动机的磨损故障。对油样数据的实例分析结果表明,该方法对航空发动机磨损故障具有较强的识别能力,对磨损状态有很好的监测效果。
Aiming at the requirement of intelligent and precise development of the wear fault diagnosis technology of aeroengines, based on the traditional oil monitoring technology, combined with the adaptive characteristics of the artificial immune system (AIS), learning and memory characteristics and recognition characteristics, Intelligent Diagnosis of Aero Engine Wear Faults. Firstly, the detector is generated by the negative selection principle of artificial immune theory. The optimized detector generation algorithm improves the representativeness and coverage of the initial detector. Then, the fault detector is used to train a mature detector to make the aero-engine typical The wear status information is stored in the detector to realize effective learning and memory of the failure mode. Finally, the wear failure of the aeroengine is found through the activation of the detector. The results of the example analysis of the oil sample data show that this method has a strong ability of recognizing the aeroengine wear fault and has a good monitoring effect on the wear state.