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XIEChun-li XIAHong LIUYong-kuo 《船舶与海洋工程学报》2005,4(1):30-33
The work condition of nuclear power plant (NPP) is very bad, which makes it has faults easily. In order to diagnose the faults real time, the fusion diagnosis system is built. The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosis into three levels, which are data fusion level, feature level and decision level. The feature level uses three parallel neural networks whose structures are the same. The purpose of using neural networks is mainly to get basic probability assignment (BPA) of D-S evidence theory, and the neural networks in feature level are used for local diagnosis, D-S evidence theory is adopted to integrate the local diagnosis results in decision level. The reactor coolant system is the study object and we choose 2# steam generator Utubes break of the reactor coolant system as a diagnostic example, The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirement of complicated system, and verify that the fusion fault diagnosis system can realize the fault diagnosis of NPP on line timely. 相似文献
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LIUYong-kuo XIAHong XIEChun-li 《船舶与海洋工程学报》2005,4(1):34-38
The fuzzy logic and neural networks are combined in this paper, setting up the fuzzy neural network (FNN) ; meanwhile, the distinct differences and connections between the fuzzy logic and neural network are compared. Furthermore, the algorithm and structure of the FNN are introduced. In order to diagnose the faults of nuclear power plant, the FNN is applied to the nuclear power plant, and the intelligence fault diagnostic system of the nuclear power plant is built based on the FNN . The fault symptoms and the possibility of the inverted U-tube break accident of steam generator are discussed. In order to test the system‘ s validity, the inverted U-tube break accident of steam generator is used as an example and many simulation experiments are performed. The test result shows that the FNN can identify the fault. 相似文献
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在总结核动力一回路设备的故障诊断技术的基础上,依据“分解-综合”的诊断思想,提出基于神经网络和专家系统的综合智能诊断系统的模型和多源信息综合诊断模型,探索将不同的诊断技术结合起来形成新的智能诊断系统。 相似文献
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基于模糊信息融合的船舶动力装置综合故障诊断方法研究 总被引:1,自引:1,他引:0
在模糊集理论的基础上,将决策级信息融合技术应用于故障诊断系统中,提出了一种基于系统模糊综合评价融合结构下的综合故障诊断方法.该方法以模糊逻辑运算和全局决策融合来自多传感器的局部判决来获取诊断对象的综合诊断结果,并对船舶主动力系统的运行故障进行诊断研究,结果表明,该方法准确有效,为船舶动力装置故障的智能化诊断提供了有益的借鉴. 相似文献
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船舶动力装置工作过程中会产生大量多域故障信号,通过收集、挖掘隐藏的关联信号,可以解决船舶动力装置在故障诊断中面临的诊断时长问题.文章采用K-均值聚类算法(K-means)对数据进行聚类,聚类结果输入BP神经网络进行模型训练,并在此基础上,设计了主成分分析法(PCA)对模型进行优化.结果 显示,2种算法都能有效降低网络诊... 相似文献
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A marine propulsion system is a very complicated system composed of many mechanical components.As a result,the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft.It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis.For this reason,a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems.To monitor the gear conditions,the bispectrum analysis was first employed to detect gear faults.The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique,which could be regarded as an index actualizing forepart gear faults diagnosis.Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox.The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum,and the ANN classification method has achieved high detection accuracy.Hence,the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases,and thus have application importance. 相似文献
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基于神经网络的柴油机遥控系统故障智能诊断研究 总被引:1,自引:0,他引:1
为了克服传统模拟电路故障诊断方法的不足,通过对船舶柴油机遥控系统工作原理的分析,提出采用BP神经网络诊断船舶主机遥控系统的智能诊断方法。介绍BP神经网络结构确定方法及其数值优化技术,并以具体电路模块为例探讨神经网络在船舶柴油机遥控系统故障诊断中的应用。通过Matlab仿真可以发现基于BP神经网络的电路故障诊断方法具有自适应性好、训练时间短、准确性高等特点。 相似文献
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研究柴燃联合动力装置状态监测系统的组成及基本体系结构,针对其测点多、数据采集量大的特点,引入一种新的数据存储优化策略,在保证数据存储的安全性和有效性的同时,节约存储空间,为动力装置的故障诊断和技术状态评估提供依据。 相似文献
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结构系统的可靠性评估是结构设计的一个重要研究内容,而极限状态函数的建立是进行可靠性评估的基础.但是,大型结构系统的极限状态函数极为复杂,响应面法用简单的多项式进行模拟的精度较低,导致误差较大.文章提出用神经网络替代多项式来拟合复杂的极限状态函数,形成所谓的神经网络响应面.然后,基于塑性极限理论,文中提出了不依赖于失效模式的极限状态函数表达形式及采用ICP对该极限状态函数进行计算的方法.最后,依照拟合得到的神经网络响应面,给出了大型结构系统失效概率的方法.通过两个算例计算并和其它方法进行比较,表明该方法的计算精度较高,而计算时间大大降低. 相似文献
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针对电力系统多异构设备故障诊断工程化不高的问题,提出一种具体的故障诊断实现框架.采用通信方式和数据结构独立配置的方法,实现电力系统多种协议通信的无缝连接,解决了底层异构设备数据在顶层设备中的接收、分析和存储难以统一的问题;在故障诊断框架下,设计并实现模糊专家系统和其他故障数据处理综合诊断;研究了基于xml的系统配置信息描述,能够实现电力系统中各种设备、参数和故障判断准则等信息的标准化;同时提出了一种故障诊断算法,实现系统各设备的故障定位.诊断实例结果表明,系统推理效率高,可信度好,能够满足电力系统故障诊断要求. 相似文献
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将Petri网理论用于船舶电力系统故障诊断中,提出了一种改进的Petri网故障诊断模型。在基本Petri网诊断模型的基础上引入模糊推理规则形成模糊Petri网,说明了该方法的模型构建、推理过程及解析方法的表示。利用该方法对船舶电力系统进行故障诊断使推理过程简洁、诊断快速、诊断结果也更科学有效。 相似文献
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传统的设计螺旋方法由于割裂了学科间耦合,得到的设计往往是满足设计要求的设计而不是最优的设计.多学科设计优化(MDO)方法正是一种能得到最优设计的新设计方法,MDO目前被广泛应用于复杂工程系统的设计中.文中对多学科设计优化的理论进行了简要综述,在此基础上为了演示MDO方法应用于舰船设计,采用iSIGHT对国外的CGX巡洋舰概念设计的13个模块进行了集成,建立了MDO模型并优化,得到了较好的优化解.在此基础上采用神经网络重新建立了该MDO模型,将优化结果和原MDO模型优化结果进行了比较. 相似文献