基于先锋光纤通道交换网的故障监测与诊断的研究与设计

基于先锋网的天气预报系统对先锋网的可靠性和可管理性提出了要求。分析了先锋网的原理后,我们设计了先锋网故障监测与诊断子系统,并在分析了SNMP下目前故障诊断方法的基础上,提出了主动式的根据各硬件设备的关系收集信息来确定故障源的诊断算法;系统采用以太网与光纤网双型网络,能依据设备之间的关系,经过主动的测试、推断和排除来确定故障的位置,具有很高的智能性,特别是当光纤通路出现故障时,系统还能利用第二条通路进行详细的诊断;系统采用的诊断算法能够根据各部分间的关系对可能出现故障的部分进行主动测试,收集需要的测试信息,从而确定故障源。     

基于现场可编程门阵列的SMS4故障检测实现

硬件实现的SMS4加密算法计算过程中容易出现故障,为防止攻击者利用故障信息进行故障攻击从而破解SMS4算法,提出一种针对SMS4算法的故障检测方案。该方案首先分析了硬件实现的SMS4算法出现故障的位置及其影响,然后在关键路径上建立了3个检测点,通过实时监测检测点来定位算法执行过程中出现的故障。一旦成功检测到故障,立即重新执行算法以保证攻击者难以获取有效的故障信息。将提出的方案和原无故障检测的算法分别在Xilinx公司的Virtex-7和Altera公司的Cyclone Ⅱ EP2C35F76C6两个现场可编程门阵列(FPGA)上综合实现,在Virtex-7上,提出的带故障检测的方案比原算法占用逻辑资源增加30%,吞吐量相当;在EP2C35F76C6上比原算法增加0.1%的硬件资源,吞吐量达到原来的93%。实验结果表明,在尽量不影响吞吐量的前提下,提出的方案占用硬件资源小,并且可以有效地检测出故障,从而避免SMS4算法受到故障攻击。     

An efficient numerical-based crosstalk avoidance codec design for NoCs

With technology scaling, crosstalk fault has become a serious problem in reliable data transfer through Network on Chip (NoC) channels. The effects of crosstalk fault depend on transition patterns appearing on the wires of NoC channels. Among these patterns, Triplet Opposite Direction (TOD) imposes the worst crosstalk effects. Crosstalk Avoidance Codes (CACs) are the overhead-efficient mechanisms to tackle TODs. The main problem of CACs is their high imposed overheads to NoC routers. To solve this problem, this paper proposes an overhead-efficient coding mechanism called Penultimate-Subtracted Fibonacci (PS-Fibo) to alleviate crosstalk faults in NoC wires. PS-Fibo coding mechanism benefits the novel numerical system that not only completely removes TODs but also, is applicable to a wide range of NoC channel widths. The PS-Fibo coding mechanism is evaluated using BookSim-2 and VHDL-based simulations in the terms of codec efficiency on the crosstalk fault reduction, codec power consumption, codec area occupation and network performance. Evaluation results, carried out for a wide range of NoC channel widths indicate that PS-Fibo can improve power consumption and area occupations of codec and NoC performance with respect to the other state-of-the-art coding mechanisms.     

多型号电缆故障诊断的设计与实现

针对某武器系统故障诊断装置缺少电缆故障诊断功能的问题,通过对其内部电路板的改制实现电缆故障诊断;采用对电缆的某一芯加高电平信号,测量其它各芯是否有高电平信号的方法,来判断电缆是否存在短路或断路故障,并给出了具体的软件实现流程;使用多路输出选择器件,解决I/O口数量不足的问题;使用双向开关,实现与原诊断装置共用面板接口的问题;改进后的诊断装置能够快速、准确地诊断出不超过41芯的多种型号电缆的短路和断路故障,缩减了开发时间和研制费用。     

Application-driven dynamic bandwidth allocation for two-layer network-on-chip design

Network-on-Chip (NoC) architecture has been widely used in many multi-core system designs. To improve the communication efficiency and the bandwidth utilization of NoC for various applications, we firstly propose a table-based algorithm for identifying the dominant flows at runtime. Then a two-layer NoC architecture with an application-driven bandwidth allocation scheme is presented, which is capable of identifying heavy-load dataflows and dynamically reconfiguring point-to-point (P2P) connections to optimize the heavy-load traffic. Experimental results reveal that our design (8 × 8 mesh NoC) achieves 28.5% performance improvement and 25.9% power consumption saving compared to the baseline NoC.     

Observer-based fault detection and isolation filter design for linear time-invariant systems

In this paper we consider a model-based fault detection and isolation problem for linear time-invariant dynamic systems subject to faults and disturbances. We use a state observer scheme that cancels the system dynamics and defines a residual vector signal that is sensitive only to faults and disturbances. We then design a stable fault detection and isolation filter such that the ?_∞-norm of the transfer matrix function from disturbances to the residual is minimised (for fault detection) subject to the constraint that the transfer matrix function from faults to residual is equal to a pre-assigned diagonal transfer matrix (for isolation of possibly simultaneous occurring faults). Our solution is given in the form of linear matrix inequalities using state-space techniques, as well as a model matching problem using matrix factorisation techniques. A numerical example is given to illustrate the efficiency of the fault detection and isolation filter.     

SISO过程突发性故障基于滑动容错拟合的在线诊断

以SISO过程输出环节脉冲型故障为研究对象,建立了过程轨线具有容错功能的滑动容错拟合算法,以滑动容错拟合算法为基础,构造了一组适用于多故障过程脉冲型故障的在线检测方法,以及故障幅度的统计辨识算法。通过理论分析和仿真计算,证实了本文滑动容错拟合和故障在线诊断方法的有效性。     

基于QEMU实现的处理器类故障模拟与注入方法研究

故障注入技术在BIT软件测试中是一种有效的手段。针对电路板级BIT软件测试中遇到的问题,介绍了一种基于开源模拟器QEMU实现的处理器类故障模拟方法。采用该方法对多种处理器故障进行仿真建模,通过对QEMU的扩展开发,加入故障行为模拟模块和故障注入模块,以实现一个具有处理器类故障注入功能的系统级模拟器BitVaSim。首先分析处理器功能故障模式,提取故障的关键字值对,用XML Schema定义故障并用于故障建模;其次对QEMU代码进行二次开发以实现对处理器故障行为的模拟;然后通过配置故障注入接口实现模拟器运行时的故障模式匹配、故障按条件触发等功能;最后通过实验案例来观察模拟器的故障表现,评价这种基于模拟器的故障注入技术。实验过程和结果显示这种方法是有效可行的。     

基于神经网络的非线性系统故障检测及容错控制方法

利用神经网络的非线性建模能力，提出了一种非线性系统的故障检测及容错控制方法。在本方法中，首先应用神经网络设计故障估计器，在线估计系统故障向量，实现故障检测；在此基础上，引入补偿控制器，消除故障对系统运行的影响，从而实现容错控制。同时基于Ｌｙａｐｕｎｏｖ方法进行了稳定性分析。     

Exploiting and evaluating the potentials of the link addition method for NoC transient error mitigation

Transient errors in a Network on Chip (NoC) result in some problems such as network blockage, packets loss or incorrect delivery, which would decrease the network throughput and degrade the successful delivery rate. Many fault tolerant mechanisms, such as error correcting code, retransmission and redundancy for the NoC, have been proposed to mitigate transient errors and guarantee the communication quality. Different from these existing methods, the paper aims at exploiting the potentials of the link addition strategy for transient error alleviation. The regular link addition as well as the customized link addition based on Mesh is designed for alleviating NoC transient errors. The regular design is suitable for the general purpose case while the partially customized design exploits the inherent communication characteristics and the reliability requirement of applications for some specified cases. The experimental results for typical network benchmarks confirm that the proposed link addition methods are effective to improve NoC performance and reliability. (1) In the case of the regular link addition, 4 × 4 Torus brings the throughput to increase by 45.76% and 87.34% over Mesh for the transpose traffic and the uniform traffic respectively. The reliability metrics of Torus over Mesh are up to 56.65% and 12.71% for the transpose traffic and the uniform traffic respectively. (2) The novel customized reliability-aware link addition mechanism makes the throughput improvement up to 17.4%, 53.5% and the reliability metric up to 16.34%, 57.76% over standard Mesh for the transpose traffic and the hotspot traffic respectively. In addition, the area overhead and power consumption of NoCs are also evaluated by the tool—Orion in the paper.     

基于规范变量残差的化工过程微小故障检测与诊断

微小故障因其幅值低而易被噪声和过程扰动所掩盖,并且会随时间慢慢演变成过程中的严重故障.因此,微小故障的检测和诊断变得越来越重要.为了更有效地监测和诊断微小故障,提出了基于规范变量残差的化工过程微小故障检测和诊断方法.首先,对Hankel矩阵执行奇异值分解来获得主元和残差空间并根据过去和未来数据的差异,求得两个不同的规范变量残差d_1, d_2.其次,考虑数据的时间序列特性,提出了基于规范变量残差的两个加权平均统计量W_(D1), W_(D2)及其控制限,进行故障检测;然后,计算出各个统计量的归一化贡献并绘制二维贡献图,进行故障诊断.最后,在连续搅拌釜式反应器(CSTR)过程中进行两种微小故障的应用研究.结果表明,与传统的统计量T~2,Q以及规范变量差异分析(CVDA)中统计量D相比,基于规范变量残差的加权平均统计量W_(D1), W_(D2)不仅能够及时检测到微小故障,而且在故障检测率和诊断率方面,均有不同程度的提高.     

An iterative learning observer for fault detection and accommodation in nonlinear time‐delay systems

This article addresses fault detection, estimation, and compensation problem in a class of disturbance driven time delay nonlinear systems. The proposed approach relies on an iterative learning observer (ILO) for fault detection, estimation, and compensation. When there are no faults in the system, the ILO supplies accurate disturbance estimation to the control system where the effect of disturbances on estimation error dynamics is attenuated. At the same time, the proposed ILO can detect sudden changes in the nonlinear system due to faults. As a result upon the detection of a fault, the same ILO is used to excite an adaptive control law in order to offset the effect of faults on the system. Further, the proposed ILO‐based adaptive fault compensation strategy can handle multiple faults. The overall fault detection and compensation strategy proposed in the paper is finally demonstrated in simulation on an automotive engine example to illustrate the effectiveness of this approach. Copyright © 2005 John Wiley & Sons, Ltd.     

协议被动测试的错误标识

被动测试不仅能够检测协议实现是否有错误,而且利用检测过程中的症状信息能够标识错误.提出一种扩展了的被动测试错误标识算法,它能够在标记错误过程中记录该错误所导致的系统状态;又给出了一种以上述状态为初始状态继续向前观察多步以区分错误的算法.所提方法能够区分等价错误;继续向前测试步数不受限制,应用该方法对一个简化的BGP协议状态机模拟器进行错误标识,实验结果表明在观察步骤数足够多的情况下所提方法能够高效定位系统实现错误.     

Detecting Faults in Context-Aware Adaptation

Internetware applications are context-aware. They adapt their behavior based on environmental changes. However, faulty adaptation may arise when these applications face unanticipated situations. Such adaptation faults can be difficult to detect at design time. One promising approach is to statically analyze model-based context-aware applications exhaustively for all potential faults. However, it suffers from expressiveness and precision problems. To address these limitations, we propose in this paper a dynamic adaptation model (AM) approach. AM offers increased expressive power to model complex adaptation rules, and guarantees soundness in its fault detection. In addition, AM deploys an incremental rule evaluation (IRE) technique to cater for context-aware applications, such that it can effciently handle environmental changes in its fault detection. We evaluated AM using both simulated and real-world experiments with two context-aware applications. The experimental results confirmed that AM can detect real faults missed by existing work, and avoid numerous false warnings that were misreported otherwise.     

A theoretical study of fault coupling

Fault coupling is the phenomenon whereby a test set is able to detect faults when they occur in isolation, but fails to do so when they occur in combination. It is widely regarded as a nuisance in fault‐based approaches to software testing, which focus on the detection of single faults and normally neglect multiple faults. This paper presents a theoretical study of fault coupling, based on a simple model of fault‐based testing. This provides for the presence of two faults that interact with each other and thus includes the possibility of fault coupling between them. The model is analysed mathematically, the conclusion reached being that fault coupling only occurs infrequently. This result provides support for current approaches to fault‐based testing, but it is not quite enough to conclude that they are thereby validated. In effect, the paper generalizes the results of a previous paper that dealt with the restricted case where the functions underlying programs are bijective as well as finite. The restriction that functions be bijective is lifted here, but they are still required to be finite. Though the same theoretical framework is used in both cases, and more or less the same results are obtained, the techniques employed to arrive at the results in the general case are quite different. Copyright © 2000 John Wiley & Sons, Ltd.     

加权k最近邻重构分析的工业过程故障诊断

k--最近邻(k--nearest neighbor, k--NN)是一种有效的基于数据驱动的故障检测方法, 该方法在工业过程监视方面已经得到了广泛的应用. 但在过程中存在故障时, 精确地寻找故障根源和识别故障变量是故障诊断的重要目标, 也是保证工业过程安全生产的重要任务. 本文在k--NN故障检测技术的基础上, 提出了一种加权的k--NN重构方法, 对使控制指标减小最大(maximize reduce index, MRI)的过程变量依次进行重构, 进而确定发生故障的传感器. 根据理论分析并结合数值仿真对提出的方法进行了验证, 数值仿真先从精度方面验证了该方法能够有效地对故障传感器数值进行重构, 然后验证了该方法不仅适用于单一传感器 故障诊断, 对于同时发生或者因变量相关性而传播的传感器故障也具有很好的效果. 最后, 该方法被成功应用于TE(Tennessee Eastman)化工过程.     

Iterative Learning Fault Diagnosis Algorithm for Non-uniform Sampling Hybrid System

For a class of non-uniform output sampling hybrid system with actuator faults and bounded disturbances, an iterative learning fault diagnosis algorithm is proposed. Firstly, in order to measure the impact of fault on system between every consecutive output sampling instants, the actual fault function is transformed to obtain an equivalent fault model by using the integral mean value theorem, then the non-uniform sampling hybrid system is converted to continuous systems with timevarying delay based on the output delay method. Afterwards, an observer-based fault diagnosis filter with virtual fault is designed to estimate the equivalent fault, and the iterative learning regulation algorithm is chosen to update the virtual fault repeatedly to make it approximate the actual equivalent fault after some iterative learning trials, so the algorithm can detect and estimate the system faults adaptively. Simulation results of an electro-mechanical control system model with different types of faults illustrate the feasibility and effectiveness of this algorithm.      

Intermittent sensor fault detection for stochastic LTV systems with parameter uncertainty and limited resolution

ABSTRACT

This paper considers the detection problem of intermittent sensor faults in stochastic linear time-varying systems with both parameter uncertainty and limited resolution. By introducing the soft measurement model, a state estimator is designed whose upper bound of estimation error covariance is obtained and minimised at each time step. Based on it, the residual is generated and its relationship with the fault is analysed quantitatively. Then the evaluation function and corresponding detection threshold is given. Our proposed method is recursive and therefore suitable for real-time online applications. At last, two simulation studies are carried out to illustrate the validity of our proposed method.     

Active probing based Internet service fault management in uncertain and noisy environment

In Internet service fault management based on active probing, uncertainty and noises will affect service fault management. In order to reduce the impact, challenges of Internet service fault management are analyzed in this paper. Bipartite Bayesian network is chosen to model the dependency relationship between faults and probes, binary symmetric channel is chosen to model noises, and a service fault management approach using active probing is proposed for such an environment. This approach is composed of two phases： fault detection and fault diagnosis. In first phase, we propose a greedy approximation probe selection algorithm （GAPSA）, which selects a minimal set of probes while remaining a high probability of fault detection. In second phase, we propose a fault diagnosis probe selection algorithm （FDPSA）, which selects probes to obtain more system information based on the symptoms observed in previous phase. To deal with dynamic fault set caused by fault recovery mechanism, we propose a hypothesis inference algorithm based on fault persistent time statistic （FPTS）. Simulation results prove the validity and efficiency of our approach.