因果图向故障树转换的研究

因果图模型可以表示整个复杂系统中各因素见的复杂因果关系,故障树是故障分析和安全可靠性分析的常用模型,如果因果图模型能够转换为故障树模型,将扩大因果图的应用范围,方便故障树建模。因此研究因果图向故障树的转换显得十分重要。文章在分析故障树与因果图概念和表示符号的基础上,提出了因果图转换为故障树的算法。通过因果图向故障树转换可以得到任何事件为顶事件的故障树模型,然后可以采用成熟的FTA方法进行分析,这样因果图模型中的一些问题也可以采用故障树的方法来解决。在假设因果图中的连接事件表示必然因果关系条件下,因果图可以转换为含有可能的因果关系的故障树。研究表明通过微因果树化,因果图可以转换为故障树,所以因果图模型具有广泛的适用性和应用前景。     

故障树向因果图转换的研究

故障树和因果图都采用图形表示因果关系,都主要用于故障诊断和故障分析。由于二者的相似点较多,因此研究故障树向因果图的转换显得十分重要。这样可以进一步扩大因果图的应用范围。文章在分析故障树与因果图概念和表示符号的基础上,探讨了故障树与因果图之间的相互转换,提出了故障树转换为因果图的方法。研究结果表明：含有“与门”、“或门”、“禁门”和“表决门”的故障树可以转换为因果图,这样对于这类故障树处理的故障诊断问题都可以采用因果图来解决。     

因果图理论用于煤矿机械设备故障分析的研究

因果图理论是一种基于概率论的图形化的知识表达推理方法，文中对因果图理论在故障分析和安全评估中的应用进行了研究。将这种新方法运用于煤矿机械设备的故障分析，并与故障树分析法进行了比较和分析。研究结果表明因果图方法比故障树分析法效果好。     

控制系统的SDG模型描述及故障传播分析

讨论了控制系统的符号有向图(SDG)模型描述和故障在控制系统中的传播方式及分析方法.该方法按控制回路信息流向的正反推理,分析初始和稳态响应的故障传递规律,通过基本单元的组合可以扩展到各类控制系统的SDG描述和故障传播分析.以锅炉水位控制系统为例,验证了该方法的有效性.     

基于独立源分析的过程监测方法

多元统计过程控制要求观测数据服从正态分布,而实际的5-业过程数据大都不满足正态分布条件.独立源分析（ICA）近几年才发展起来的一种新的统计方法,可以克服对数据分布的依赖性．对此,以ICA算法为核心,引入一种新型的过程监测方法,应用ICA提取独立源,利用I^2图,Ic^2图和SPE图进行故障检测．最后以3水箱系统为例进行了实验研究,取得了很好的效果．     

Fault detection and diagnosis in synchronous motors using hidden Markov model-based semi-nonparametric approach

Early detection and diagnosis of faults in industrial machines would reduce the maintenance cost and also increase the overall equipment effectiveness by increasing the availability of the machinery systems. In this paper, a semi-nonparametric approach based on hidden Markov model is introduced for fault detection and diagnosis in synchronous motors. In this approach, after training the hidden Markov model classifiers (parametric stage), two matrices named probabilistic transition frequency profile and average probabilistic emission are computed based on the hidden Markov models for each signature (nonparametric stage) using probabilistic inference. These matrices are later used in forming a similarity scoring function, which is the basis of the classification in this approach. Moreover, a preprocessing method, named squeezing and stretching is proposed which rectifies the difficulty of dealing with various operating speeds in the classification process. Finally, the experimental results are provided and compared. Further investigations are carried out, providing sensitivity analysis on the length of signatures, the number of hidden state values, as well as statistical performance evaluation and comparison with conventional hidden Markov model-based fault diagnosis approach. Results indicate that implementation of the proposed preprocessing, which unifies the signatures from various operating speeds, increases the classification accuracy by nearly 21% and moreover utilization of the proposed semi-nonparametric approach improves the accuracy further by nearly 6%.     

Reconstruction-based contribution for process monitoring

This paper presents a new method to perform fault diagnosis for data-correlation based process monitoring. As an alternative to the traditional contribution plot method, a reconstruction-based contribution for fault diagnosis is proposed based on monitored indices, SPE, T² and a combined index φ. Analysis of the diagnosability of the traditional contributions and the reconstruction-based contributions is performed. The lack of diagnosability of traditional contributions is analyzed for the case of single sensor faults with large fault magnitudes, whereas for the same case the proposed reconstruction-based contributions guarantee correct diagnosis. Monte Carlo simulation results are provided for the case of modest fault magnitudes by randomly assigning fault sensors and fault magnitudes.     

Local polynomial estimation in partial linear regression models under dependence

A regression model whose regression function is the sum of a linear and a nonparametric component is presented. The design is random and the response and explanatory variables satisfy mixing conditions. A new local polynomial type estimator for the nonparametric component of the model is proposed and its asymptotic normality is obtained. Specifically, this estimator works on a prewhitening transformation of the dependent variable, and the results show that it is asymptotically more efficient than the conventional estimator (which works on the original dependent variable) when the errors of the model are autocorrelated. A simulation study and an application to a real data set give promising results.     

8031单片机在自动剪板机上的应用

介绍了自动剪板机系统的结构组成和工作原理，叙述了8031单片微机实现其生产过程控制的硬件与软件的设计方法。     

Super connectivity of Kronecker products of graphs

Let G₁ and G₂ be two connected graphs. The Kronecker product G₁×G₂ has vertex set V(G₁×G₂)=V(G₁)×V(G₂) and the edge set . In this paper, we show that if G is a bipartite graph with κ(G)=δ(G), then G×Kn(n?3) is super-κ.     

Smooth functions and local extreme values

The problem of specifying a smooth and simple function that approximates noisy data is considered. A new automatic method is described that is based on solving a constrained optimisation problem. The target functional to be minimised is the sum of the squared residuals penalised by the curve length of the approximation. Multiresolution and monotonicity constraints provide a good approximation to the data with a small number of local extreme values. The new method can also be applied to density estimation.     

Robust estimation of dimension reduction space

Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions. Two recently proposed methods, minimum average variance estimation and outer product of gradients, can be and are made robust in such a way that preserves all advantages of the original approach. Their extension based on the local one-step M-estimators is sufficiently robust to outliers and data from heavy-tailed distributions, it is relatively easy to implement, and surprisingly, it performs as well as the original methods when applied to normally distributed data.