【作者】 徐百灵；

【导师】 刘群；

【作者基本信息】 哈尔滨工程大学 ， 计算机应用技术， 2005， 硕士

【摘要】 设备故障诊断技术是近40年来发展起来的适应工程实际需要而形成的一门综合学科,是各学科交叉发展的必然,由此获取的经济效益十分显著。数据融合作为许多传统学科和新兴工程领域相结合而产生的一个新的前沿学科,超越了在军事领域的应用,己经在很多领域都得到了广泛的应用。设备故障诊断中可利用的信息很多,只有充分利用有用的信息来对设备的故障进行诊断才能提高故障诊断的精度和可靠性,因此故障诊断实质上是一个多信息融合的过程。 本文提出了基于人工神经网络信息融合的故障诊断,分析了单子神经网络进行故障珍断的特点,提出了集成神经网络的故障诊断模型,研究了集成神经网络的建模方法、组建原则和实现策略,并结合诊断实例进行了仿真分析,结果表明利用集成神经网络信息融合进行故障诊断是一种有效的方法。 在设备故障诊断的过程中,如何减少误诊率,提高可靠性的研究具有重大的意义。传统的诊断系统未考虑网络在各种扰动或故障作用情况下的输出误差,使得系统的容错性受到很大限制。因此神经网络本身的可靠性显得越来越重要,迫切需要建立分析和设计高可靠性的神经网络的理论和方法。 本文介绍了容错神经网络理论的初步框架,把容错神经网络应用到故障诊断中并进行了仿真,实验结果表明此种容错神经网络在网络无故障时能够保持性能不变,在网络出现故障时,也具有良好的性能。此方法极大的提高了设备故障诊断的容错性。更多还原

【Abstract】 Fault diagnosis is a new multi-subject-crossed technique and it meets the needs of project reality .It has been developed rapidly during the past 40 years, and it has brought huge benefit.Information fusion is a subject formed recently. It has been researched and applied in many fields. There is much available information in fault diagnosis. Only when the available information is used, can the precision and credibility be improved. So fault diagnosis is a process of information fusion.The fault diagnosis model is put forward in this paper. Based on the analysis of single neural network characteristic, the model of integrated neural network is put forward. At the same time, the way to establish model, the principle to compose and the strategy to realize are given in this paper. The result based on the simulation of the example show it is a effective way by using integrated neural network to deal with the characteristic information.How to reduce the misdiagnosing rate and improve dependability has great meanings in the process of the fault diagnosis. Traditional diagnosis system don’t consider network output error under the fault situation,so the systematic fault-tolerance is limited greatly. So the dependability of the neural network itself seems more and more important, and it needs to analyse and design the theory and method of the neural network of high dependability urgently.In this paper we introduce the frame of fault-tolerant neural network and do simulation on fault-tolerant neural network. The result based on the simulation of the example show fault-tolerant neural network can keep performance well when the network runs well. When the network breaks down , the system have good performance too.The system improves the fault-tolerance of fault diagnosis greatly.更多还原