摘要
随着人工神经网络研究的兴起,智能故障诊断技术成为近年来人们研究的一个热点问题。自动刹车是飞机起降时的重要装置,需要定期对其进行故障检测,以免形成飞行安全隐患,是飞机故障诊断的一个重要内容。本文从传统的BP神经网络算法出发,通过算法改进建立了波音737自动刹车故障诊断的神经网络模型,通过提取故障样本对网络进行训练和诊断,仿真结果显示这种方法能较理想地对波音737飞机自动刹车系统的故障进行分析和检测,对于建立波音飞机的故障自动诊断系统具有重要意义。
In recent years, intelligent diagnosis technology has been one of the hotspot research with the artificial neural net. In this paper, we start from the conventional arithmetic of the BP neural net, establish the fault diagnosis neural net model of Boeing 737 auto braking system, training and diagnosing the net by the samples of fault. The results show that this method can analyze and de tect the fault of auto brake system well. Experiment indicated that this method is practical and has significant importance for con struction of Boeing 737 automatic fault diagnosis system.
出处
《微计算机信息》
2012年第8期48-50,共3页
Control & Automation
