基于人工神经网络的某型雷达故障诊断的设计与实现

发布时间：2018-05-02 10:30

本文选题：模拟电路 + 故障诊断　；参考：《中国石油大学(华东)》2014年硕士论文

【摘要】：随着计算机技术与控制技术的发展与应用,军事装备的自动化程度也越来越高。故障原因、故障过程和故障现象也变得错综复杂,研究如何运用现代诊断技术从大规模容差电路中准确地诊断出存在故障元件,是实际工程迫切需要解决的课题,也是模拟电路故障诊断理论和方法走向实际应用的关键步骤之一。本文介绍了近年来故障诊断技术的发展现状和基本理论,对故障诊断方法进行了简要评述,介绍了故障诊断技术的理论基础,阐述了人工神经网络在故障诊断领域广泛应用的必然性。介绍了人工神经网络的基础理论,重点研究了基于BP神经网络的改进算法,并将专家系统和BP神经网络理论相结合应用到某型雷达的故障诊断之中。最后详细介绍了某型雷达故障诊断系统的设计与实现,利用Visual FoxPro 8.0语言和控制主机等硬件电路建立一个简单的某型雷达故障诊断系统。结果表明,本文提出的专家系统与BP神经网络结合应用于某型雷达故障诊断的方法,是可行的,有很好的应用前景。
[Abstract]:With the development and application of computer technology and control technology, the degree of military equipment automation is becoming higher and higher. The causes, processes and phenomena of faults have become complicated. It is an urgent task for practical engineering to study how to use modern diagnosis technology to accurately diagnose the existing fault elements from large scale tolerance circuits. It is also one of the key steps in the application of analog circuit fault diagnosis theory and method. In this paper, the development and basic theory of fault diagnosis technology in recent years are introduced, the methods of fault diagnosis are briefly reviewed, and the theoretical basis of fault diagnosis technology is introduced. The necessity of wide application of artificial neural network in fault diagnosis is expounded. This paper introduces the basic theory of artificial neural network, focuses on the improved algorithm based on BP neural network, and applies the expert system and BP neural network theory to the fault diagnosis of a certain type of radar. Finally, the design and implementation of a radar fault diagnosis system is introduced in detail. A simple radar fault diagnosis system is established by using Visual FoxPro 8.0 language and control host circuit. The results show that the proposed expert system combined with BP neural network is feasible and has a good application prospect in a certain type of radar fault diagnosis.
【学位授予单位】：中国石油大学(华东)
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN957;TP183

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