基于产生式人工神经网络的汽轮发电机组故障诊断专家系统研究

针对人工神经网络训练样本难以获取的困难,提出１种从专家系统获取训练样本的方法,使得产生式人工神经网络能够有效地逼近传统专家系统的诊断推理行为,系统已被应用于１２５ＭＷ汽轮发电机组的故障诊断。     

基于模糊神经网络的汽轮发电机组振动故障诊断

利用模糊系统和神经网络的优势,构造了一种改进模糊神经网络模型,该模型由四层组成。改进后的模糊神经网络模型大大减少了运算量,提高了收敛速度。采用此算法对于实际汽轮发电机组振动故障实例进行诊断（诊断结果与某改进BP网络进行比较）,证明了该模型是行之有效的,并具有较强的故障识别能力。     

汽轮发电机组不稳定振动故障诊断

据不完全统计,国内已运行的汽轮发电机组转子存在明显热弯曲的约占20%.目前转子的平衡都是在常温下进行的,运行中会有一系列原因使转子热变形而产生新的不平衡.诊断和消除热变形一般比较困难,通常是用轴系平衡的方法予以补偿.我厂二电站2号机在1993年10月巡检中,发现4号轴承振动超过50μm的标准值,达到79μm,判断为发电机转子热不平衡.因当时正处于负荷高峰期,经采用特殊方法开机后,振动得到了控制.大修时找到了故障原因并予以处理,消除了故障.     

汽轮发电机组在线故障诊断的模糊综合决策

文章用模糊综合决策理论建立了某船舶汽轮发电机组在线监测与故障诊断系统。用阶比采样获取振动信号,提取时域的信息状态变化率参数及阶比域的ＡＲ阶比谱值参数为状态特征参数。在综合决策策略中,用模糊隶属度描述和关联描述两类模型来表征信号异常程度。文章建立了模糊综合决策模型并用汽轮发电机维修前综合决策结果验证了的合理性,先进性。运行结果证明系统是可靠和实用的。     

汽轮发电机组状态监测与故障诊断系统

介绍了所研制的汽轮发电机组状态监测与故障诊断系统的结构、功能和特点,同时给出了监测与诊断子系统软件的设计思想与实现方法。系统采用了网络分布式结构,由多机共同完成数据采集、分析、监测与诊断任务,可以实时地进行数据采集与分析、状态监测、故障诊断。该系统已成功地在某电厂运行。     

基于虚拟技术的发电机组故障诊断与在线监测

介绍了汽轮发电机组的具体故障形式以及转子的不同故障对应的转子轴心轨迹，分别计算了汽轮发电机组在3种不同状态下的关联雏数，开发了基于Lab VIEW的汽轮发电机组故障诊断与在线监测系统，综合利用频谱分析技术、小波分析技术以及分形理论，同时监测发电机转子轴心轨迹和轴承温度可提高发电机组故障诊断的命中率。     

汽轮发电机组在线故障诊断的模糊综合决策

用模糊综合决策理论建立了某船舶汽轮发电机组在线监测与故障诊断系统。用阶比采样获取振动信号，提取时域的信息状态变化率参数及阶比域的ＡＲ阶比谱值参数为状态特征参数。在综合决策策略中，用模糊隶属度描述和关联描述两类模型来表征信号异常程度，建立了模糊综合决策模型，并用汽轮发电机故障的综合决策结果验证了模型的合理性和诊断系统的可行性。     

某汽轮发电机组轴系的动平衡处理及研究

以某厂的一台国产２００ＭＷ汽轮发电机组为为例,着重介绍了对国产２００ＭＷ汽轮发电机组轴泵的现场振动分析及动平衡处理的方法、步骤。通过动平衡处理及振动分析,发现了影响该机组轴系泵导振动的各种因素,并提出了改进措施,取得了较好的效果,其结论对类似机组具有一定的启发作用。     

汽轮发电机组振动故障分析与处理

3#气轮发电机组振动较大,影响机组的安全运行。介绍该机组的振动测试数据和分析过程。试验总结轴振测量支架共振和刚度不足故障的判断方法。     

汽轮发电机组状态监测与故障诊断系统的发展和展望

文章对状态监测与故障诊断的定义进行了综合,综述了监测诊断技术的意义以及国内外电站监测诊断技术及应用情况,最后针对目前汽轮机监测诊断系统研发中存在的问题,指出虚拟仪器、远程虚拟仪器技术的应用将成为其发展的方向.     

Quantitative analysis of nonlinear joint motions for young males during walking

Body motions associated with walking exhibit irregular and complex patterns with time. Chaos analysis methods have been developed to clarify nonlinearity of the lower extremity motions. No research has been reported on chaos analysis of the upper extremity joints. The purpose of this study was to investigate chaotic characteristics of movements of the upper body as well as the lower extremity during level walking. Gait experiments were carried out for eighteen young males. Each subject was instructed to walk on a treadmill at his own natural speed. Flexion-extension angles of eleven joints were obtained by using eight video cameras. To evaluate joint characteristics in a quantitative way, the largest Lyapunov exponent (LLE) was calculated from a reconstructed state space created by time series and embedding dimension. The mean LLE ranged from 0.080 to 0.137 for the upper body, and from 0.090 to 0.182 for the lower extremity joints. The mean LLE of the upper extremity joints was statistically different from that of the lower extremity joints (p<0.05). The results obtained can be used as a valuable reference for the normal gait for further studies of abnormal walking.     

往复式压缩机故障诊断方法研究综述

本文叙述了往复式压缩机故障诊断的意义及研究现状,对往复式压缩机常见故障及机理进行了分析,并介绍了国内外一些常见的往复式压缩机状态监测与故障诊断的方法及其原理和特点,最后提出了往复式压缩机的故障诊断方法的难点和发展方向,为从事该方面研究提供借鉴.     

基于DataSocket和小波消噪的齿轮故障远程监测与诊断

介绍利用LabVIEW平台检测齿轮故障信号 ,叙述DataSocket协议和使用DataSocket技术进行远程监控的方法 ,给出在LabVIEW的环境内 ,使用MATLAB脚本节点对齿轮振动信号进行小波消噪和分解 ,提取齿轮故障特征信息 ,实现齿轮故障的远程诊断的方案。     

小波分析在深孔加工刀具故障诊断中的应用

在小波分析的理论基础上 ,讨论小波包在故障信息提取中的应用。小波包频段能量故障特征提取方法 ,克服了传统的信号处理方法不易提取微弱故障信息的不足。并举例说明该法的实用性     

Fault diagnosis of complex system based on nonlinear frequency spectrum fusion

A novel fault detection and diagnosis approach is proposed for nonlinear complex systems by combining nonlinear frequency spectrum characteristics and evidence theory. In order to overcome the problem of calculated amount expansion of generalized frequency response functions, single-dimensional nonlinear output frequency response functions are used to obtain nonlinear frequency spectrum, from which, features of nonlinear frequency spectrum are extracted. The fault diagnosis model of multiple faults is given based on evidence theory. The mass functions of evidences are obtained according to the similarity among different modes. In order to solve the problem of evidence fusion in the situation of evidence confliction, a dynamic parameter conflicting evidence combination method is proposed based on the average credibility. Fault diagnosis of the transmission system of numerical control machine tool is studied through nonlinear frequency spectrum. Simulations indicate that the proposed approach has simple calculation and high recognition rate for faults.     

基于小波变换的柱塞泵故障诊断方法

基于柱塞泵的脉动分析模型,提出了一种基于小波变换的柱塞泵故障诊断方法。以泵出口处的压力信号作 为分析信号,利用小波变换将该信号进行频谱分解,可有效地提取压力信号中所包含的故障信号。仿真与试验结 果均表明:基于小波变换对泵出口压力信号的分析可实现泵的故障诊断。     

齿轮故障诊断技术现状与展望

介绍了齿轮故障理论及诊断技术的现状;对齿轮故障机理研究、齿轮故障简易诊断技术、精密诊断技术、诊断技术最新发展进行了分类阐述,并对齿轮故障诊断技术的未来发展方向提出了看法。     

基于故障树的汽包水位故障诊断方法

锅炉汽包满、缺水事故是长期困扰火力发电厂安全的恶性频发事故之一,对故障的早期征兆和发展趋势进行处理对于事故预报和预警、避免事故发生具有重要意义。在深入研究汽包水位故障的成因机理基础上,采用将广义故障树分析方法与智能诊断系统相结合的思路,给出一种汽包水位故障的在线监测和诊断的方法。通过汽包水位低故障的现场实例应用表明该方法的有效性。     

Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis

Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing.     

Multiple manifolds analysis and its application to fault diagnosis

A novel approach to fault diagnosis is proposed using multiple manifolds analysis (MMA) to extract manifold information from the vibration signals collected from a mechanical system. The basic idea of MMA is to reconstruct a manifold by embedding time series into a high-dimensional phase space. The tangent direction of the neighborhood for each point is then used to approximate its local geometry. The variation of the multiple manifolds representing different states of the mechanical system can be revealed by performing multi-way principal component analysis. The vibration signals acquired from roller bearings are employed to validate the proposed algorithms. Test results show that the proposed MMA-based approach can interpret different machine conditions and is effective to the fault diagnosis, and the MMA-based fault clustering and trend analysis algorithms have outperformed the conventional fault diagnosis methods.