Application of data fusion method to fault diagnosis of nuclear power plant

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.     

舰船核动力装置非能动余热排出系统运行特性

用RELAP5/MOD3.2安全分析程序对某型舰船核动力装置非能动余热排出系统进行数学建模,并用实际装置的试验结果进行校核验证。重点分析了自然循环工况下蒸汽发生器U型管内冷却剂倒流特性对非能动余热排出系统运行特性的影响。结果表明:舰船核动力装置发生全部电源丧失事故时,蒸汽发生器二次侧非能动余热排出系统能正常投入运行,但蒸汽发生器U型管内冷却剂会发生倒流,降低了一回路主系统的自然循环能力。     

Fault Diagnosing System of Steam Generator for Nuclear Power Plant Based on Fuzzy Neural Networks

All kinds of reasons are analysed in theory and a fault repository combined with local expert experiences is establishedaccording to the structure and the operation characteristic of steam generator in this paper. At the same time, Kohonen algo-rithm is used for fault diagnoses system based on fuzzy neural networks. Fuzzy arithmetic is inducted into neural networks tosolve uncertain diagnosis induced by uncertain knowledge. According to its self-association in the course of default diagnosis. thesystem is provided with non-supervise, self-organizing, self-learning, and has strong cluster ability and fast cluster velocity.     

船用U形管蒸汽发生器模型及其动态仿真应用

针对船用立式U形管自然循环蒸汽发生器,建立了能够描述其稳态及瞬态热工水力特性的数学物理模型,并编制了计算程序。计算和验证结果表明,该数学物理模型准确,可以与其他船用模型一起构建船用系统模型以进行安全分析研究或开发船用核动力仿真机。     

蒸汽发生器水位的模糊-PID复合控制

为了提高蒸汽发生器的模糊水位控制器的稳态性能,结合常规PID控制的优点,设计蒸汽发生器水位的模糊-PID复合控制器。运用Matlab模糊逻辑工具箱与Simulink模块,实现蒸汽发生器的水位控制仿真,对蒸汽发生器大幅度降负荷工况进行仿真计算。结果表明,模糊-PID复合控制既具有模糊控制的良好动态性能,又具有线性控制的良好稳态性能。模糊-PID复合控制特别适用于具有大滞后、多变量、强耦合的非线性系统,是一种比较有发展前景的控制方法。     

Design of robust fuzzy controller for ship course-tracking based on RBF network and backstepping approach

1 Introduction1 Recently, adaptive control approaches based on fuzzy neural network (FNN) have been studied in a lot of papers [1–4]. FNN combines the capability of fuzzy reasoning in handling uncertain information and the capability of artificial neural…     

基于FNN的船舶舵伺服系统故障诊断研究

分析某型船舶舵伺服系统的故障信息,建立了模糊神经网络故障诊断模型.利用模糊逻辑处理数据以便于充分利用经验知识;利用神经网络诊断,避免了复杂故障树诊断系统的"匹配冲突"、"组合爆炸"和"无穷递归"等问题,并采用改进的BP算法训练神经网络,解决了收敛速度和收敛振荡的问题.诊断实例结果表明:该故障诊断系统具有较强的鲁棒性和泛化能力;该算法采用无模型化诊断,容易实现自学习,可不断完善系统性能,具有一定的理论和工程应用价值.     

基于模糊RBF神经网络的辐射源识别

针对模糊识别系统的不足,为了提高辐射源识别系统的识别正确率,构建了基于模糊RBF神经网络的辐射源识别系统,提出了一种等价型模糊RBF神经网络的结构和学习算法,采用五层神经网络结构来实现模糊系统的模糊化和规则推理,神经网络的所有节点和参数对应了模糊系统的隶属函数和推理过程.在仿真实验中,分别采用模糊识别系统、并联型模糊RBF神经网络、结构等价型模糊RBF神经网络进行辐射源识别,给出了三种算法在相同噪声环境下的仿真结果,表明等价型模糊RBF效神经网络有较高的正确识别率,具有更强的抗干扰能力,但运算量相对较大.     

基于模糊神经网络的燃气轮机故障诊断专家系统研究

针对神经网络和传统专家系统在燃气轮机故障诊断过程中各自存在的局限性,提出了一种将模糊神经网络和专家系统相结合的方法.解决了以往专家系统专家知识获取困难和不能描述模糊性知识的缺陷.通过已开发的某型三轴燃气轮机运行模拟器取得典型的故障样本完成了对模糊神经网络的训练工作,最后选取一定数量的测试样本对网络进行了测试,证明了系统的可行性.结果表明,该方法行之有效,在燃气轮机故障诊断领域中有很好的应用价值.     

蒸汽发生器水位神经自校正控制研究

针对传统的核动力蒸汽发生器水位PID控制方法存在的缺点,将神经网络方法与PID控制的结构结合起来,提出核动力蒸汽发生器水位单神经元自校正PID控制方法,在线调整控制器参数。仿真研究表明,该方法可以提高系统的控制品质。     

模糊神经网络控制在调速系统中的应用研究

针对无刷直流电机(BLDCM)调速系统的特点,在分析BLDCM(Brushless DC Motor)的数学模型的基础上建立了控制系统的仿真模型,设计了一种新的模糊神经网络控制器FNNC(Fuzzy Neural Network Controller),并运用遗传算法优化模糊神经网络结构和改进在线学习算法,将其应用到直流电机调速系统中。仿真试验表明,该方法较传统PID控制有更好的控制效果。     

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

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.     

AUV fuzzy neural BDI

The typical BDI （belief desire intention） model of agent is not efficiently computable and the strict logic expression is not easily applicable to the AUV （autonomous underwater vehicle） domain with uncertainties. In this paper, an AUV fuzzy neural BDI model is proposed. The model is a fuzzy neural network composed of five layers ： input （ beliefs and desires）, fuzzification, commitment, fuzzy intention, and defuzzification layer. In the model, the fuzzy commitment rules and neural network are combined to form intentions from beliefs and desires. The model is demonstrated by solving PEG （pursuit-evasion game） , and the simulation result is satisfactory.     

Nuclear power plant fault diagnosis based on genetic-RBF neural network

1 Introduction1 Nuclear power plant is a kind of complicated large-scale system, and the fault mechanism and reason are very complex either. Generally, the fault has no obvious relation with omen. Besides, there are also large quantities of uncertainties.…     

Turbine speed control system based on a fuzzy-PID

The flexibility demand of marine nuclear power plant is very high, the multiple parameters of the marine nuclear power plant with the once-through steam generator are strongly coupled, and the normal PID control of the turbine speed can＇t meet the control demand. This paper introduces a turbine speed Fuzzy-PID controller to coordinately control the steam pressure and thus realize the demand for quick tracking and steady state control over the turbine speed by using the Fuzzy control＇s quick dynamic response and PID control＇s steady state performance. The simulation shows the improvement of the response time and steady state performance of the control system.     

一种基于神经网络的故障诊断新方法研究

针对电子设备故障诊断的问题,在研究分析模糊理论和神经网络的原理和特点的基础上,提出了一种模糊神经网络故障诊断方法,实现两者优势互补,并将全信息理论应用其中,为实现人工神经网络能在人类智能模拟深度上有新突破进行了有意义的探索。     

基于B/S和C/S架构的船舶动力装置远程故障诊断系统

依据“智能船舶”的理念，针对船舶动力装置故障诊断系统，提出一种基于C/S和B/S混合架构的船舶动力装置远程故障诊断系统。依据B/S和C/S架构的优势，开发了基于C/S架构的数据管理平台，实现了船、岸间的数据、信息交互；将基于模糊神经网络的专家诊断模型应用于B/S架构的岸基船舶动力装置故障诊断系统的故障诊断判别，并利用BP算法训练实例对该模型进行了精度验证。结果表明，系统稳定可靠，故障诊断准确性高，为“智能船舶”发展提供了一个良好的解决方案。     

测控装备故障诊断专家系统的设计与实现

通过分析测控装备故障诊断专家系统的目的和任务,将模糊神经网络理论与专家系统相结合用于测控装备故障诊断,建立了故障诊断专家系统的模型。探讨了故障诊断专家系统中知识库的构建及维护、不精确推理等问题。用面向对象分析方法分析故障诊断专家系统,并用软件加以实现。     

基于BP神经网络整定的蒸汽发生器水位PID控制及仿真研究

将神经网络与PID控制结合起来,提出了基于BP神经网络整定的蒸汽发生器水位PID控制方法,采用BP学习算法调整控制器神经网络的连接权值,通过对系统性能的学习实现了控制器参数的在线整定,仿真结果表明,所设计的控制器具有良好的控制性能。     

导管架式海洋平台自适应预测逆控制研究

文章提出了一种新的基于动态刚度阵法的导管架式海洋平台模糊神经网络自适应预测逆控制模型。该模型是通过安放在海洋平台桩腿上的压力传感器实时采集海洋平台所受波浪力,然后应用动态刚度阵法快速而准确地计算出平台顶部的响应,再将此响应作为模糊神经网络预测逆控制器的输入信号,预测出下一时刻平台顶部的控制力来对海洋平台振动响应进行控制;同时,通过反馈的自适应预测逆控制器来减小扰动和前馈控制的误差。为了验证该模型的抗干扰能力,文中还对有随机风载荷扰动情况下的导管架式海洋平台主动控制进行了研究。最后的数值算例结果表明,文中所提出的导管架式海洋平台主动控制模型具有较强的抗干扰能力,能很好地解决控制系统中存在的时滞问题,可有效地控制导管架式海洋平台的振动响应。