基于空间故障树理论的系统故障定位方法研究

为了系统发生不同类型故障后快速定位可能引起该故障的系统元件,通过分析系统结构和元件故障概率分布,以及系统在不同工作环境中发生各类型故障的统计数量,提出基于空间故障树(Space Fault Tree,SFT)理论的系统故障定位方法.该方法使用SFT概念得到系统内部结构及元件的故障概率矩阵P(X_i),分析元件X_i故障对于所在割集S_j及系统T故障的贡献度,结合系统故障次数统计矩阵Γ(m_q),最终得到元件X_(1~I)与故障m_(1~Q)的相关度矩阵.这个矩阵可反映出对于任意系统故障m_q与故障元件X_(1~I)的相关性排序、对应的割集、及保证结论正确的可能性,还可优化系统故障分类.实例研究表明:方法可确定各故障的至因故障元件,并根据可能性进行排序,排序靠前的元件组合正是系统的割集,这从侧面也说明了方法正确性.     

多参数结构特征二阶灵敏度

提出了一种有效计算多参数结构特征值与特征向量二阶灵敏度矩阵--Hessian矩阵的方法.将特征值和特征向量二阶摄动法转变为多参数形式,推导出二阶摄动灵敏度矩阵,由此得到特征值和特征向量的二阶估计式.该法解决了无法用直接求导法计算特征值和特征向量二阶灵敏度矩阵的问题.数值算例说明了该算法的应用和计算精度.     

飞机故障模糊诊断系统结构设计及仿真

建立了某型飞机典型故障诊断的数学模型与仿真模型 ,针对最大隶属度原则的缺陷 ,改进了该原则 ,此诊断方法可以按飞机系统功能分解推广到对任一分系统 (如液压系统 ,燃料系统 ,冷气系统 ,着陆装置系统等 )的故障诊断 .     

基于模糊集和COPRAS的改进FMEA方法

本文针对传统故障模式及影响分析(FMEA)方法在复杂不确定环境下存在的缺陷,提出了一种基于模糊集理论和COPRAS(Complex Proportional Assessment)的改进FMEA方法。该方法首先应用模糊集理论并结合专家知识建立评价故障模式的模糊语言术语集,然后由FMEA专家小组对各种故障模式根据其风险因子进行模糊评价,最后利用COPRAS方法并综合考虑风险因子的相对权重确定各故障模式的风险顺序,从而识别出关键故障模式。通过将改进FMEA方法应用到企业仓储管理中,并与传统FMEA方法结果进行比较,验证了该方法的有效性和准确性。     

非对称阻尼系统特征对一阶导数与二阶导数的计算

提出了一种计算非对称阻尼系统特征对一阶、二阶导数的方法.该方法利用阻尼系统的特征向量计算特征对的导数,避免了状态空间中特征向量的使用,节省了计算量,且不要求系统所有特征值的互异性.最后以两个非对称阻尼系统进行数值试验,数值结果表明提出的方法是有效的.     

基于动态神经网络的非线性系统鲁棒故障检测

针对一类具有不确定性扰动的非线性系统,将设计的系统线性观测器产生的误差信号作为残差,采用一种具有高斯型激励函数的动态神经网络(DNN)对残差信号进行分析处理,得到了系统的鲁棒故障检测方法.文中分析了该方法的稳定性和故障检测的鲁棒性,并通过算例验证了该方法的有效性.     

阻尼系统重特征对导数的计算

提出了一种计算阻尼系统重特征值及其特征向量导数的方法．该方法利用n维空间的特征向量计算特征对的导数,避免了状态空间中特征向量的使用,从而节省了计算量,提高了计算效率．最后以一个5自由度的非比例阻尼系统对所提方法进行了数值试验,数值结果表明方法是有效的．     

一类二次特征值反问题的中心对称解及其最佳逼近

1引言给定n阶实矩阵M,C和K,二次特征值问题:求数λ和非零向量x使得Q(λ)x=0, (1．1)其中Q(λ)=λ2M λC K称为二次束．数λ和相应的非零向量x分别称为二次束Q(λ)的特征值和特征向量．Tisseur和Meerbergen概述了二次特征值问题的各种应用、数学理论和数值方法．在工程技术,特别是结构动力模型修正技术领域经常遇到与二次特征值问题相反的问题(称之为二次特征值反问题)．对阻尼结构进行动力分析时,应用有限元方法可得到系统的质量矩阵M,阻尼矩阵C和刚度矩阵K,从而可求得二次特征值问题的特征值(频率)和特征向量(振型)．但是有限元模型毕竟是实际结构系统的离散化,并且     

被检测系统的可靠性和最优检测策略

研究被检测系统的一个模型,假定系统有4种运行状态(正常工作、异常工作、正常故障和异常故障)．系统故障时不需检测,系统工作时必须经过检测才能知道它是正常还是异常．系统开始工作后,每隔一段随机时间对它检测一次,直到系统故障或检测出系统处于异常状态为止．利用概率分析和随机模型的密度演化方法,导出了系统的一些新的可靠性指标和最优检测策略．     

求n阶实方阵A的全部模最大的特征值及其相应特征向量的幂法

本文给出并论证了 ,当 n阶实方阵 A具有 i ( 1≤ i≤ n)个 (即任意多个 )模最大的特征值时 ,用幂法求出这些模最大的特征值及其相应特征向量的方法 .该方法是对幂法理论的进一步完善     

Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets

Fault detection of rotating machinery by the complex and non-stationary vibration signals with noise is very difficult, especially at the early stages. Also, many failure mechanisms and various adverse operating conditions in rotating machinery involve significant nonlinear dynamical properties. As a novel method, phase space reconstruction is used to study the effect of faults on the chaotic behavior, for the first time. Strange attractors in reconstructed phase space proof the existence of chaotic behavior. To quantify the chaotic vibration for fault diagnosis, a set of new features are extracted. These features include the largest Lyapunov exponent; approximate entropy and correlation dimension which acquire more fault characteristic information. The variations of these features for different healthy/faulty conditions are very good for fault diagnosis and identification. For the first time, a new chaotic feature space is introduced for fault detection, which is made from chaotic behavior features. In this space, different conditions of rotating machinery are separated very well. To obtain more generalized results, the features are introduced into a neural network to identify different faults in rotating machinery. The effectiveness of the new features based on chaotic vibrations is demonstrated by the experimental data sets. The proposed approach can reliably recognize different fault types and have more accurate results. Also, the performance of the new procedure is robust to the variation of load values and shows good generalization capability for various load values.     

基于故障树和混沌粒子群算法的锻压机床故障诊断方法

锻压机床由于生产效率高和材料利用率高的特点,被广泛应用于各领域.然而,锻压机床发生故障时,其故障种类繁多、故障数据量大,所以对锻压机床故障源的快速、准确诊断较困难.针对该问题,文章提出一种将故障树分析法和混沌粒子群算法相融合的方法,对锻压机床的故障源进行故障诊断.该方法是先通过故障树分析法对锻压机床的故障进行分析从而得到故障模式及其故障概率,然后由得到的故障模式和已知的故障维修经验分析归纳出故障模式的学习样本,再根据得到的故障概率运用混沌粒子群算法的遍历性快速、准确地诊断出锻压机床发生故障的精确位置.文章提出的方法以锻压机床的伺服系统为例进行了故障诊断实验,将该实验结果与遗传算法、粒子群算法进行对比.实验结果表明,文章的算法在锻压机床伺服系统的故障诊断中准确度更高、速度更快.     

Pattern classification by using improved wavelet Compressed Zernike Moments

In this paper, an improved Feature Extraction Method (FEM), which selects discriminative feature sets able to lead to high classification rates in pattern recognition tasks, is presented. The resulted features are the wavelet coefficients of an improved compressed signal, consisting of the Zernike moments amplitudes. By applying a straightforward methodology, it is aimed to construct optimal feature vectors in the sense of vector dimensionality and information content for classification purposes. The resulting surrogate feature vector is of lower dimensionality than the original Zernike moment feature vector and thus more appropriate for pattern recognition tasks.Appropriate validation tests have been arranged, in order to investigate the performance of the proposed algorithm by measuring the discriminative power of the new feature vectors despite the information loss.     

基于奇偶校验的Petri网控制器的故障检测

提出基于奇偶校验的方法对Petri网控制器进行故障检测.设计出满足包含标识向量和Parikh向量的线性约束的Petri网控制器;建立一个包含一定数量库所的附加Petri网控制器以满足奇偶校验的编码要求;分别针对库所故障和变迁故障,选用不同的奇偶校验参数进行故障检测,并通过实例详细阐明了故障检测的过程.     

基于特征参数分布的机载电子设备故障诊断与预测

基于特征参数趋势进化的故障预测是一种有效的方法,引入了一种考虑特征参数概率分布的新型判据进行多故障模式诊断与预测.基于过程神经网络建立了高精度预测模型,根据模型和部件使用记录进行趋势预测.基于方法对机载电子设备进行案例研究,结果表明,方法的判定结果更加符合多故障模式并存、故障严重程度不同的实际情况,而具有较高拟和、泛化预测精度的PNN模型是一种有效的趋势预测方法.     

基于Matlab灰色神经网络的电力变压器故障诊断研究

针对电力变压器故障的特点及传统诊断方法在变压器故障诊断中的局限性,提出了基于灰色神经网络的变压器故障诊断方法.首先将典型油中气体浓度样本集作为参考序列,挖掘出样本集中的故障信息,然后利用灰色神经网络进行变压器的故障诊断.通过大量的实例,并将诊断结果与IEC三比值法和改良三比值法的诊断结果相比较,表明基于matlab灰色神经网络的诊断方法具有更高的精确度.     

A boosting method with asymmetric mislabeling probabilities which depend on covariates

A new boosting method for a kind of noisy data is developed, where the probability of mislabeling depends on the label of a case. The mechanism of the model is based on a simple idea and gives natural interpretation as a mislabel model. The boosting algorithm is derived from an extension of the exponential loss function, which provides the AdaBoost algorithm. A connection between the proposed method and an asymmetric mislabel model is shown. It is also shown that the loss function proposed constructs a classifier which attains the minimum error rate for a true label. Numerical experiments illustrate how well the proposed method performs in comparison to existing methods.     

Multi-class support vector machine optimized by inter-cluster distance and self-adaptive deferential evolution

Support vector machine (SVM) is a popular tool for machine learning task. It has been successfully applied in many fields, but the parameter optimization for SVM is an ongoing research issue. In this paper, to tune the parameters of SVM, one form of inter-cluster distance in the feature space is calculated for all the SVM classifiers of multi-class problems. Inter-cluster distance in the feature space shows the degree the classes are separated. A larger inter-cluster distance value implies a pair of more separated classes. For each classifier, the optimal kernel parameter which results in the largest inter-cluster distance is found. Then, a new continuous search interval of kernel parameter which covers the optimal kernel parameter of each class pair is determined. Self-adaptive differential evolution algorithm is used to search the optimal parameter combination in the continuous intervals of kernel parameter and penalty parameter. At last, the proposed method is applied to several real word datasets as well as fault diagnosis for rolling element bearings. The results show that it is both effective and computationally efficient for parameter optimization of multi-class SVM.     

输电系统的部分可观Petri网故障诊断器设计

采用部分可观Petri网的故障诊断方法来解决变电站输电系统中不可观事件和不可观运行状态的故障诊断问题.首先,将系统可观测序列分解为长度为1的基础观测序列,应用线性不等式矩阵计算与基础观测序列相符的点火序列集;然后,基于整数线性规划问题,利用向前向后函数拓宽诊断区间,同时应用参数K限定故障诊断序列长度,通过分析系统可观事件和系统部分可观状态,给出故障诊断结果.最后,构造变电站输电系统的部分可观Petri网模型,应用提出的故障诊断算法对输电系统进行诊断,诊断结果准确给出了故障发生与否及故障发生位置.算法适用于在线故障诊断,计算复杂性线性相关于观序列长度.     

Modeling,analysis and performance evaluation for fault diagnosis and Fault Tolerant Control in bottle-filling plant modeled using Hybrid Petri nets

In this paper, an approach to achieve fault diagnosis and Fault Tolerant Control in a typical bottle-filling plant using event based techniques is discussed. For this purpose, the plant is modeled using Hybrid Petri nets which enable study and analysis with regard to the working of the plant. Once effective modeling is done based on two different case studies considered, new algorithms are proposed to achieve fault diagnosis and Fault Tolerant Control on the models developed. Finally, performance measures with regard to the models proposed are evaluated to check the correctness of the models developed. Both analytical and numerical results are obtained which are highly useful to understand plant behavior.