一种快速DSmT-DS近似推理融合方法

该文对Dempster-Shafer(DS)理论以及Dezert-Smarandache理论(DSmT)进行了深入研究,为了能够在仅需较低计算复杂度的前提下得到更加精确的融合结果,提出一种新的快速DSmT-DS近似推理融合方法。该方法针对超幂集空间仅单子焦元具有信度赋值的情况,将超幂集空间拆分映射成元素为各单子焦元和其补集的二元集合的新的超幂集空间,并求出每个补集的信度赋值;再运用Dezert-Smarandache框架中的第5条比例冲突分配规则(DSmT+PCR5)在新的超幂集空间的二元集合子空间下对多证据源进行融合,得到各单子焦元的融合结果;然后通过归一化处理求得各单子焦元的信度赋值。通过理论分析得出该文方法的融合结果是介于Dezert-Smarandache框架中的第5条比例冲突分配规则(DSmT+PCR5)及Dempster-Shafer(DS)框架下的 Dempster 组合规则之间。该文方法在需要较低计算复杂度的前提下,可以得到优于Dempster组合规则的近似融合结果。最后通过多个角度与已有方法进行对比,验证了该文方法的优越性。     

一种改进的设备故障诊断方法

提出了一种新型故障诊断的粗糙集方法.在粗糙集知识系统中信息熵概念基础上,重新定义了一种信息熵度量方法,并运用信息熵判断系统状态:基于粗糙集优越的约简理论,运用一种改进的区分矩阵方法形成一种综合策略的诊断规则.该方法有效地解决随机误报以及信息丢失和信息不完备情况下仍保持着较好的诊断性能,并降低了计算复杂度,减少了计算开支.     

粗糙集数据挖掘及其在汽轮机故障诊断中的应用

针对当前专家系统知识获取瓶颈的难题,提出了基于粗糙集数据挖掘的汽轮机故障预报及诊断方法。将汽轮机故障历史数据首先进行模糊化及离散化处理,然后构建故障诊断决策表,以决策表作为主要工具,即“知识库”,采用粗糙集数据挖掘方法直接从决策表中提取出潜在的诊断规则,为汽轮机提供有效的故障诊断。提出了基于粗集的分类规则学习和约简算法,实现了基于粗糙集数据挖掘的汽轮机故障预报及诊断系统,其诊断正确率达到了88%。实验表明该方法可行,对汽轮机故障预报及诊断系统的设计具有借鉴意义和深入研究的价值。     

基于模糊故障特征信息的随机集度量信息融合诊断方法

该文给出一种基于模糊故障特征信息随机集度量的信息融合诊断方法。针对信号采集与故障特征提取中的模糊性,首先用模糊隶属度函数分别表示故障档案库中的多种故障样板模式和从不同传感器观测中提取的多类故障特征亦即待检模式,进而基于模糊集的随机集模型,得到样板模式与待检模式的匹配度,即基本概率指派函数(BPA)。然后利用Dempster-Shafer证据组合规则对BPA进行融合,给出诊断结果。该文给出的待检模式是从多个连续观测中提取的,与原有的由单个观测确定待检模式的方式相比,文中提出的特征提取及匹配方法,同时考虑了样板模式和待检模式所具有的模糊性,能够显著降低融合决策中的不确定性,大大提高故障识别的能力。最后通过电机转子故障诊断实例验证方法的有效性。     

一种基于二叉树的测控设备故障诊断方法

针对传统故障树知识规则存储和诊断推理算法不易实现的问题,提出了一种基于二叉树的故障诊断方法。首先,通过对故障树与二叉树转换规则与方法的分析,直接构建出测控设备故障二叉树集;然后,利用二叉树节点左右编码值来定位该节点的方法,建立故障诊断规则库;最后,采用遍历诊断规则库的搜索算法实现对故障定位。在测控设备中的应用表明,该方法能够方便地建立诊断规则库,准确定位设备故障,可有效提高设备故障诊断效率。     

Hierarchical motor diagnosis utilizing structural knowledge and aself-learning neuro-fuzzy scheme

A fault diagnosis system contains a classification system that can distinguish between different faults based on observed symptoms of the process under investigation. Since the fault symptom relationships are not always known beforehand, a system is required which can be learned from experimental or simulated data. A fuzzy-logic-based diagnosis is advantageous. It allows an easy incorporation of a priori known rules and enables the user to understand the inference of the system. In this paper, a new diagnosis scheme is presented and applied to a DC motor. The approach is based on the combination of structural a priori knowledge and measured data in order to create a hierarchical diagnosis system that can be adapted to different motors. Advantages of the system are its transparency and an increased robustness over traditional classification schemes     

Theorems for Fault Collapsing in Combinational Circuits

This paper gives a mathematical approach to fault collapsing based on the stuck-at fault model for combinational circuits. The mathematical structure we work within is a Boolean ring of Boolean functions of several variables. The goal of fault collapsing for a given circuit is to reduce the number of stuck-at faults to be considered in test generation and fault diagnosis. For this purpose we need rules that let us eliminate faults from the considered fault set. In this paper some earlier known rules are proved in the new context, and several new rules are presented and proved. The most important of the new theorems deal with the relationship between stuck-at faults on a fanout stem and the branches. The concept of monotony of Boolean functions appears to be important in most of these new rules. Editor: M. Hsiao Audhild Vaaje received the M.S. degree and the Ph.D. degree in mathematics from University of Oslo in 1971 and 1992, respectively. She is an associate professor of mathematics at Agder University College in Norway, where she has been employed since 1972. She has research interests in mathematics applied to fault detection in digital circuits.     

Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers

This paper presents a novel association rule mining (ARM)-based dissolved gas analysis (DGA) approach to fault diagnosis (FD) of power transformers. In the development of the ARM-based DGA approach, an attribute selection method and a continuous datum attribute discretization method are used for choosing user-interested ARM attributes from a DGA data set, i.e. the items that are employed to extract association rules. The given DGA data set is composed of two parts, i.e. training and test DGA data sets. An ARM algorithm namely Apriori-Total From Partial is proposed for generating an association rule set (ARS) from the training DGA data set. Afterwards, an ARS simplification method and a rule fitness evaluation method are utilized to select useful rules from the ARS and assign a fitness value to each of the useful rules, respectively. Based upon the useful association rules, a transformer FD classifier is developed, in which an optimal rule selection method is employed for selecting the most accurate rule from the classifier for diagnosing a test DGA record. For comparison purposes, five widely used FD methods are also tested with the same training and test data sets in experiments. Results show that the proposed ARM-based DGA approach is capable of generating a number of meaningful association rules, which can also cover the empirical rules defined in industry standards. Moreover, a higher FD accuracy can be achieved with the association rule-based FD classifier, compared with that derived by the other methods.     

基于关联规则的航空电子设备故障诊断

航空电子设备的复杂度、综合化程度越来越高,对故障的检测、故障的定位要求越来越高,基于关联规则的数据挖掘应用到产品故障的诊断中,通过多个故障现象推断出具体的故障点,提高了产品故障诊断的准确性,时效性.为产品故障的预测及故障的解决提供了决策的依据.     

基于无监督聚类和频繁子图挖掘的电力通信网缺陷诊断与自动派单

缺陷诊断一直是电力通信领域研究的难点之一。基于人工规则的缺陷诊断已经无法应对告警数据的海量增长。基于有监督学习的智能方法需要大量的标注数据和较长的系统构建时间,且大多面向指标性数据,实现部署缺乏可行性。面向告警数据,提出一种基于无监督聚类和频繁子图挖掘实现告警归并和缺陷模式发现的自学习算法,设计了一个自动化完成缺陷诊断及处置的架构。该架构具有良好的可扩展性和迭代更新能力,并部署于实际缺陷自动派单系统中。通过真实场景数据集进行实验验证,结果显示出良好的性能表现,实现了对缺陷的及时发现及精准派单维护。     

基于小波分析和神经网络的模拟电路故障诊断方法

提出了一种基于神经网络和小波分析的模拟电路故障诊断的系统方法。该方法通过对电路的可测性测度计算，选择电路的最佳测试节点，然后利用小波分析作为特征提取手段提取电路的故障特征向量，经归一化和主元分析（PCA）处理后。得到最优特征向量，最后输入到神经网络实现电路故障诊断。计算机仿真结果表明该方法具有更好的故障分辨率。     

Analog circuit soft-fault diagnosis based on sensitivity analysis with minimum fault number rule

This paper proposes a new single or multiple soft analog circuit fault diagnosis approach based on the minimum fault number rule. It is based on the consideration that the fact that the probability of a single soft fault is much greater than that of a multiple fault if the related fault modes are independent. In this way, a new diagnostic strategy based on the circuit sensitivity analysis is proposed. The proposed strategy is an optimization-based one, whose objective is to find the minimum value of unaccepted parameter deviations which satisfy all those constraints, and the constraints equations are actually the voltage increment equations in all test nodes and the changing range of each element. The diagnosis process can fulfill the requirement of fault detection and fault isolation. It enables a fast or a real-time diagnosis in practical engineering. A DC circuit example and an AC circuit example are presented to demonstrate the effectiveness of the proposed approach.     

Improved diagnosis using enhanced fault dominance

Defect diagnosis can benefit from fault dominance relations to reduce the set of defect candidate sites. This paper presents new fault dominance collapsing operators that further reduce the set of candidates considered during the initial phase of diagnosis. In contrast to existing dominance-based methods which operate on pairs of faults, the proposed method operates on sets of faults. Fault-related entities are generated to guide the diagnosis process. The proposed collapsing operators can be used to accelerate effect-cause diagnosis. Experimental results demonstrate that the proposed method achieves a higher collapsing ratio than existing methods.     

A hierarchical test generation methodology for digital circuits

A new hierarchical modeling and test generation technique for digital circuits is presented. First, a high-level circuit model and a bus fault model are introduced—these generalize the classical gate-level circuit model and the single-stuck-line (SSL) fault model. Faults are represented by vectors allowing many faults to be implicitly tested in parallel. This is illustrated in detail for the special case of array circuits using a new high-level representation, called the modified pseudo-sequential model, which allows simultaneous test generation for faults on individual lines of a multiline bus. A test generation algorithm called VPODEM is then developed to generate tests for bus faults in high-level models of arbitrary combinational circuits. VPODEM reduces to standard PODEM if gate-level circuit and fault models are used. This method can be used to generate tests for general circuits in a hierarchical fashion, with both high- and low-level fault types, yielding 100 percent SSL fault coverage with significantly fewer test patterns and less test generation effort than conventional one-level approaches. Experimental results are presented for representative circuits to compare VPODEM to standard PODEM and to random test generation techniques, demonstrating the advantages of the proposed hierarchical approach.     

基于信息融合技术的模拟电路故障诊断

论述了基于多类电量测试信息模糊融合的模拟电路故障诊断方法的基本原理,提出了分别基于K故障节点诊断法和最小标准差法的元件故障隶属函数构造方法,以及基于可测点电压与不同测试频率下电路增益的模糊信息融合诊断算法.分别利用此两类测试信息及K故障诊断法和最小标准差法,对电路进行初步诊断,再运用模糊变换及故障定位规则,得到融合的故障诊断结果.模拟实验结果表明,所提方法大大提高了故障定位的准确率.     

Multi-valued logic mapping of resistive short and open delay-fault testing in deep sub-micron technologies

In advanced technologies an increasing proportion of defects manifest themselves as small delay faults. Most of today’s advanced delay-fault algorithms are able to propagate those delay faults which create logic or glitch faults. An algorithm is proposed for circuit fault diagnosis in deep sub-micron technology to propagate the actual timing faults as well as those delay faults that eventually create logic faults to the primary outputs. Unlike the backtrack algorithm that predicts the fault site by tracing the syndrome at a faulty output back into the circuit, this approach propagates the fault from the fault site by mapping a nine-valued voltage model on top of a five-valued voltage model. In such a forward approach, accuracy is greatly increased since all composite syndromes at all faulty outputs are considered simultaneously. As a result, the proposed approach is applicable even when the delay size is relatively small. Experimental results show that the number of fault candidates produced by this approach is considerable.     

A new method for constructing fuzzy evidence based on the non-consonant random set

Natural-language information is often mathematically expressed by fuzzy sets. With the random set theory as a bridge, this kind of information can be transformed into fuzzy evidence in Dempster-Shafer (DS) theory. Then Dempster’s combination rule or other combination rules of evi- dence can be used perfectly for fusing natural-language and other information. However, this traditional transformation involves the use of α -cutsets to construct the focal elements which have to be repre- sented as consonant set...     

WCI 01 Fuzzy Rules Generation using Conditional Clustering

This work describes the Fuzzy-CCM (Fuzzy Conditional Clustering based Modeling) method, which generates fuzzy rules automatically using the conditional Fuzzy C-Means algorithm, and proposes the use of a new strategy for attributes combination on the context definition step, using heuristic search based on the criteria of best performance. This strategy allows the reduction of the number of contexts considered and avoids the generation of rule bases that do not present performance improvements with relation to the already known ones. The main goal of the Fuzzy-CCM method is to provide new manners to deal with the issue of interpretability of rule bases. The generated rules have a format different from the usual that combines linguistic variables and groups in the antecedent. The experiments were performed using data sets with discrete and continuous output values, in order to evaluate the proposed approach and to compare the results with those obtained with the Wang-Mendel and Fuzzy C-Means methods. The advantages of the method, the benefits of the proposed strategy and the results obtained in the experiments are discussed     

A NEURAL NETWORK BASED FAULT FUZZY DIAGNOSTIC SYSTEM

A fault fuzzy diagnostic system(FFDS) based on neural network and fuzzy logic hybrid is proposed. FFDS consists of two modes: a fuzzy inference mode and a rule learning mode. The fuzzy inference rules are stored in the memory layer. The excitation levels of the memory neurons reflect the matching degrees between the input vectors and the prototype rules. In the rule learning mode, the rules can be produced automatically through the cluster process. As an application case of this diagnostic system, the fault diagnosis experiment of the rotating axis is simulated.     

一种新的缩短随机测试序列长度的方法

本文提出了一种新的缩短随机测试序列长度的方法,它是在找到电路中难测故障分布的基础上,通过对电路的初始输入施加概率不等的“１”信号,使这些难测故障的测试率升至最大值,这样,就可以达到提高故障覆盖率和缩短测试序列长度的目的。