贪心算法在系统故障诊断策略生成中的应用

诊断策略是具有一定顺序的测试序列.诊断策略生成的目标是隔离故障,并使测试开销达到最小.本文介绍测试序列生成的各种信息启发式算法并通过对比各种算法诊断策略生成的期望开销,着重讨论了Rollout算法的优越性.     

基于局域网的系统级概率分布式故障诊断

提出了一个系统级概率分布式故障诊断的算法 ,它能诊断局域网及其类似系统中结点机故障和链路故障 .该算法突破了现有研究成果中要求网络中故障结点数不超过 t(t相似文献   

数字微流控生物芯片测试诊断过程分析和优化

针对数字微流控生物芯片的测试和诊断过程进行建模和分析,并根据并行测试的分块数和单元出错概率为相应的测试和诊断成本建立函数。通过Matlab对测试诊断成本函数的分析表明：随着并行测试分块数的增大,测试诊断成本的变化趋势不明显,也就是说,并行测试的分块数对测试诊断成本的影响不大;而随着单元出错概率p的增加,测试成本呈明显的增加趋势,且增加的幅度较大。另外,诊断过程中,根据单元出错概率对出错的子阵列再进行诊断,诊断过程必须持续若干次,直到所有故障定位后才能结束。在这些诊断中,针对最后一次定位的诊断成本是最大的,而且与其他次的诊断过程的成本相差几十个数量级,决定了总成本的大小。这些结论为数字微流控生物芯片的测试和诊断过程优化提供重要的理论依据,并为测试诊断方法的设计提供指导。     

一个系统级故障诊断算法

本文提出了多处理机系统故障诊断的一个算法。为了度量 该算法的运行时间，定义了算法的概率时间复杂度函数，进而通过仿真实验和理论分析证明了这个算法能够用 较小的开销获得高的正确诊断率。     

通过单扫描链的构造实现最小测试应用时间

在交迭测试体系＾「１，２」的基础上提出了一种利用二选一开关辅助扫描寄存器的排序、能够实现最小应用时间的单扫描链的构造方法，给出了单扫描链的构造规则。此外还分析了由于二选一开关的引入带来的硬件开销问题，提出了一个能够减少硬件开销的算法。     

一种改进的基于跳数的无线传感器网络路由算法

路由协议设计是无线传感器网络的一个重要研究领域,可靠性、低开销和易于维护是无线传感器网络路由协议的设计目标,其中基于跳数的路由协议以其简易、有效的设计思路,一直以来得到广泛关注。在详细分析基于跳数的无线传感器路由协议发展现状的前提下,对最小跳数路由算法的组网和数据传播阶段加以改进。通过在OMNeT++仿真环境中与原始最小跳数协议、定向扩散协议的比较,验证了改进后的算法在可靠性、负载均衡、延长网络生命周期和低路由开销方面的优势。     

面向测试优化的片上网络映射算法

针对复杂片上系统(SoC)芯片的片上网络(NoC）映射方案未考虑测试需求的问题,提出了一种面向测试优化的NoC映射算法,兼顾了可测性的提升和映射开销的最小化。该映射方案首先依据特定的测试结构,使用划分算法进行片上系统所有IP核的测试分组,其优化目标为测试时间最短;之后,再基于分组内IP核之间的通信量,应用遗传算法实现NoC映射,其优化目标是在测试优化的基础上实现映射开销最小。通过多个ITC'02测试基准电路进行的实验结果表明:应用该方案后,测试时间平均减少12.67%;与随机任务映射相比,映射代价平均减少24.5%。     

基于DSDV的无线Mesh网络跨层路由设计

在DSDV协议的基础上,提出一种跨层路由设计的方法,选择一条具有合理长度和最小重传概率的路由进行测试。该链路质量机制可应用到基于IEEE802.11x的无线Mesh网络中,无须对现有的MAC层进行修改,即可作为一个单独模块加入到现有协议。在多跳网络中使用该跨层方案进行路由选择,能节省系统开销,优化系统的整体性能。     

基于概率分布及维度编码的关联规则挖掘

设计一种基于二进制数及项目的支持度分布的Apriori改进算法BF-Apriori。该算法通过分析项目的概率分布并对项目集中的项目按概率从大到小进行排序,经维度编码为二进制数后,降低事务数据库的读取开销和存储开销,同时采用切片运算和剪枝技术降低规则挖掘运算的时间复杂度。实验结果表明,BF-Apriori算法降低了50%左右的存储开销及400%以上的执行时间,能提高数据挖掘的存储效率和运算速度。     

基于重要抽样的软件统计测试加速

本文提出一种基于重要抽样的软件统计测试加速方法,该方法通过调整软件Markov链使用模型的迁移概率,在根据统计测试结果得到软件可靠性无偏估计的前提下,可以有效提高安全攸关软件的测试效率,部分解决了安全攸关软件统计测试时间和费用开销过大的问题。同时,本文给出了计算优化迁移概率的模拟退火算法。实验仿真结果表明,该方法可以有效地提高安全攸关软件统计测试的效率。     

网络管理系统分布式诊断方法研究

文中提出了一种网络管理系统的分布式诊断算法。该算法是一个基于概率模型的比较诊断算法。文中对算法的准确度和参数对准确度的影响进行了分析,并给出了准确度的上（下）界。     

Node grouping in system-level fault diagnosis

With the popularization of network applications and multiprocessor systems,dependability of systems has drawn considerable attention.This paper presents a new technique of node grouping for system-level fault diagnosis to simplify the complexity of large system diagnosis.The technique transforms a complicated system to a group network,where each group may consist of many nodes that are either fault-free or faulty.It is proven that the transformation leads to a unique group network to ease system diagnosis.Then it studies systematically one-step t-faults diagnosis problem based on node groupling by means of the concept of independent point sets and gives a simple sufficient and necessary condition.The paper presents a diagnosis procedure for t-diagnosable systems.furthermore,an efficient probabilistic diagnosis algorithm for practical applications is proposed based on the belief that most of the nodes in a system are fault-free.The result of software simulation shows that the probabilistic diagnoisis provides high probability of correct diagnosis and low diagnosis cost,is suitable for systems of any kind of topology.     

基于GPNN算法的电网区间故障诊断策略

为提升电网故障诊断算法的有效性,提出一种基于改进高斯概率神经网络(GPNN)核相似性合并预测的电网子区间故障诊断策略.首先,针对电网故障诊断中存在的数据不确定性现象,引入概率神经网络对其进行数据处理,同时为进一步提高概率神经网络对于数据不确定性的鲁棒处理效果,利用高斯算法对概率神经网络进行改进;其次,针对大型电网故障诊...     

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%.     

Probabilistic clock synchronization in distributed systems

Presents and analyzes a new probabilistic clock synchronization algorithm that can guarantee a much smaller bound on the clock skew than most existing algorithms. The algorithm is probabilistic in the sense that the bound on the clock skew that it guarantees has a probability of invalidity associated with it. However, the probability of invalidity may be made extremely small by transmitting a sufficient number of synchronization messages. It is shown that an upper bound on the probability of invalidity decreases exponentially with the number of synchronization messages transmitted. A closed-form expression that relates the probability of invalidity to the clock skew and the number of synchronization messages is also derived     

Hybrid fault characteristics decomposition based probabilistic distributed fault diagnosis for large-scale industrial processes

The performance of fault diagnosis is highly dependent on the representation of fault characteristics. However, for large-scale industrial processes with high-dimension variables, treating the whole process as a single subject will degrade the representation accuracy. It may result from the following reasons: First, fault may disturb a part of variables rather than the whole process where the fault information may be buried by the unaffected variables. Second, fault characteristics may be hybrid, in which linear fault patterns and nonlinear fault patterns coexist. Therefore, an effective process decomposition mechanism is of great demand to well describe the complex fault characteristics of large-scale processes. This paper proposes a fault characteristics decomposition based probabilistic and distributed fault diagnosis method. First, process is decomposed into different subsets by evaluating fault effects from linear and nonlinear aspects. Based on the decomposition result, distributed diagnosis models are developed where different fault modeling strategies are implemented for different subsets to closely describe fault characteristics. For online application, probabilistic fault diagnosis is implemented at two levels. At the lower level, distributed diagnosis models are adopted to reveal the underlying characteristics of new sample in each subset; at the upper level, the final affiliation can be revealed by integrating the results from each subset in a probabilistic way. The effectiveness of the proposed algorithm is tested by both the numerical example and industrial processes.     

Rollout strategy-based probabilistic causal model approach for the multiple fault diagnosis

Multiple fault diagnosis (MFD) is used as an effective measure to tackle the problems of real-shop floor environment for reducing the total lifetime maintenance cost of the system. It is a well-known computationally complex problem, where computational complexity increases exponentially as the number of faults increases. Thus, warrants the application of heuristic techniques or AI-based optimization tools to diagnose the exact faults in real time. In this research, rollout strategy-based probabilistic causal model (RSPCM) has been proposed to solve graph-based multiple fault diagnosis problems. Rollout strategy is a single-step iterative process, implemented in this research to improve the efficiency and robustness of probabilistic causal model. In RSPCM instead of finding all possible combinations of faults, collect the faults corresponding to each observed manifestations that can give the best possible result in compared to other methods. Intensive computational experiments on well-known data sets witness the superiority of the proposed heuristic over earlier approaches existing in the literature. From experimental results it can easily inferred that proposed methodology can diagnosed the exact fault in the minimum fault isolation time as compared to other approaches.     

Probability based vehicle fault diagnosis: Bayesian network method

Fault diagnostics are increasingly important for ensuring vehicle safety and reliability. One of the issues in vehicle fault diagnosis is the difficulty of successful interpretation of failure symptoms to correctly diagnose the real root cause. This paper presents an innovative Bayesian Network based method for guiding off-line vehicle fault diagnosis. By using a vehicle infotainment system as a case study, a number of Bayesian diagnostic models have been established for fault cases with single and multiple symptoms. Particular considerations are given to the design of the Bayesian model structure, determination of prior probabilities of root causes, and diagnostic procedure. In order to unburden the computation, an object oriented model structure has been adopted to prevent the model from overly large. It is shown that the proposed method is capable of guiding vehicle diagnostics in a probabilistic manner. Furthermore, the method features a multiple-symptoms-orientated troubleshooting strategy, and is capable of diagnosing multiple symptoms optimally and simultaneously.