E级高性能计算机的维护故障诊断系统研究

E级计算机系统规模巨大,使得故障异常总量随之增多,导致诊断发现的难度增加,因此,迫切需要一套更加准确高效的实时维护故障诊断系统,对硬件系统进行全面的异常及故障信息实时检测、故障诊断及故障预测。传统故障诊断系统在面对数万节点规模的诊断时存在执行效率低、异常检测误报率高的问题,异常检测及故障诊断的覆盖率不足。对异常及故障检测、故障诊断与故障预测相关技术进行研究,分析技术原理及适用性,并结合E级高性能计算机实际工程需求,设计一套满足数E级高性能计算机需求的维护故障诊断系统。基于维护系统的结构组成设计可扩展的边缘诊断架构,将高性能计算机系统知识、专家知识与数理统计、机器学习相融合给出故障检测、诊断及预测算法,并针对专用场景建立预测模型。实验结果表明,该系统具有较好的可扩展性,能在10 s内完成对十万个节点规模系统的故障诊断,与传统故障诊断系统相比,异常检测某特定指标误报率从3.3%降低到几乎为0,硬件故障检测覆盖率从90.2%提升至96%以上,硬件故障诊断覆盖率从71%提升至约94%,能较准确地预测多个重要应用场景下的故障。     

双通道自适应Lattice滤波器及其在故障检测中的应用

推导出一种双通道自适应Ｌａｔｔｉｃｅ滤波器算法，并将它用于动态系统的故障检测。这种故障检测方案不需要建立准确的数学模型，只要根据系统的输入输出数据，利用Ｌａｔｔｉｃｅ滤波器算法生成故障残差序列，再对故障残差序列进行统计检验，可实现动态系统的故障检测。该方法用于一个仿真的直流伺服系统的故障检测，实验效果是满意的。     

Particle filtering-based fault detection in non-linear stochastic systems

Much of the development in model-based fault detection techniques for dynamic stochastic systems has relied on the system model being linear and the noise and disturbances being Gaussian. Linearized approximations have been used in the non-linear systems case. However, linearization techniques, being approximate, tend to suffer from poor detection or high false alarm rates. A novel particle filtering based approach to fault detection in non-linear stochastic systems is developed here. One of the appealing advantages of the new approach is that the complete probability distribution information of the state estimates from particle filter is utilized for fault detection, whereas, only the mean and covariance of an approximate Gaussian distribution are used in a coventional extended Kalman filter-based approach. Another advantage of the new approach is its applicability to general non-linear system with non-Gaussian noise and disturbances. The effectiveness of this new method is demonstrated through Monte Carlo simulations and the detection performance is compared with that using the extended Kalman filter on a non-linear system.     

Fault detection and isolation in manufacturing systems with an identified discrete event model

In this article a generic method for fault detection and isolation (FDI) in manufacturing systems considered as discrete event systems (DES) is presented. The method uses an identified model of the closed-loop of plant and controller built on the basis of observed fault-free system behaviour. An identification algorithm known from literature is used to determine the fault detection model in form of a non-deterministic automaton. New results of how to parameterise this algorithm are reported. To assess the fault detection capability of an identified automaton, probabilistic measures are proposed. For fault isolation, the concept of residuals adapted for DES is used by defining appropriate set operations representing generic fault symptoms. The method is applied to a case study system.     

An Online Fault Detection Model and Strategies Based on SVM-Grid in Clouds

Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potentially help cloud managers take some timely measures before fault occurrence in clouds. Because of the complex structure and dynamic change characteristics of the clouds, existing fault detection methods suffer from the problems of low efficiency and low accuracy. In order to solve them, this work proposes an online detection model based on asystematic parameter-search method called SVM-Grid, whose construction is based on a support vector machine (SVM). SVM-Grid is used to optimize parameters in SVM. Proper attributes of a cloud system's running data are selected by using Pearson correlation and principal component analysis for the model. Strategies of predicting cloud faults and updating fault sample databases are proposed to optimize the model and improve its performance. In comparison with some representative existing methods, the proposed model can achieve more efficient and accurate fault detection for cloud systems.      

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。     

Fault estimation and controller compensation in Lure systems by LPV-embedding

Previous works have considered the use of set invariance theory for fault detection and isolation in nonlinear Lure systems. This paper extends those results and proposes a new actuator fault-tolerant control approach. The fault-tolerant control scheme is designed based on linear parameter-varying (LPV) models of Lure systems. The actuator fault situation is diagnosed by an invariant set-based fault detection and isolation algorithm. Faults are compensated by adapting the controller gain based on estimates of the fault magnitude. Conditions for correct fault detection and isolation, and closed-loop stability are derived. The proposed fault-tolerant control scheme is compared with a linearised model approach and the performance of both, LPV-embedding and linearised, approaches are analysed for scalar and second-order systems. An example of a chaotic Chua circuit is also provided to illustrate the proposed fault-tolerant control scheme in higher-order systems.     

Fault detection in Rule-based Software systems

Motivated by packet filtering of firewall systems in Internet applications, we study the fault detection problem in the general Rule-based Software systems. We discuss algorithms for the detection of conflicts in a given set of rules. We first study a constrained version of the fault detection problem and propose a two-phase algorithm. The first phase is to do the rule normalization. The second phase is to detect conflicting rules. For this constrained version of the fault detection problem, the algorithm takes polynomial time. For the general problem, it is NP-hard. We apply the algorithms to the Rule Table getting from one of the firewalls in Bell Labs and report the experiment result.     

SD310机顶盒主板电源输入电路常见的故障排除

以SD310机顶盒主板电源输入电路等一些常见故障为例,详述了机顶盒的一些检测和故障排除的方法。首先根据机顶盒主板电源输入电路对主板电源的测试点进行检测,再根据检测结果找出问题元件及其相关问题,最后替换问题元件并解决相关问题。     

基于区间观测器的动态系统故障诊断技术综述

介绍一种基于区间观测器的故障诊断技术.为突出区间观测器在故障诊断方面的优势,总结归纳针对不同类型系统构造的可以达到故障诊断目的的区间观测器,并讨论观测器的设计条件.例如,事件触发机制下的故障检测区间观测器,针对线性参数变化系统设计的基于区间观测器的故障检测机制,在区间观测器的框架下实现带有未知互联项的T-S模糊大系统的故障隔离,以及利用区间观测器对多智能体系统实现故障隔离.最后探讨该领域亟待解决的问题.     

Modeling and Tracking of Transaction Flow Dynamics for Fault Detection in Complex Systems

With the prevalence of Internet services and the increase of their complexity, there is a growing need to improve their operational reliability and availability. While a large amount of monitoring data can be collected from systems for fault analysis, it is hard to correlate this data effectively across distributed systems and observation time. In this paper, we analyze the mass characteristics of user requests and propose a novel approach to model and track transaction flow dynamics for fault detection in complex information systems. We measure the flow intensity at multiple checkpoints inside the system and apply system identification methods to model transaction flow dynamics between these measurements. With the learned analytical models, a model-based fault detection and isolation method is applied to track the flow dynamics in real time for fault detection. We also propose an algorithm to automatically search and validate the dynamic relationship between randomly selected monitoring points. Our algorithm enables systems to have self-cognition capability for system management. Our approach is tested in a real system with a list of injected faults. Experimental results demonstrate the effectiveness of our approach and algorithms     

具有随机丢包的非线性网络化控制系统鲁棒故障检测

针对具有随机丢包的Lipschitz非线性网络化控制系统,研究了系统存在外部干扰情况下的鲁棒故障检测问题。由于带宽有限,传感器-控制器和控制器-执行器的网络通道中存在随机数据包丢失,鉴于此,设计一种基于观测器的故障检测滤波器,给出存在故障检测滤波器的充分条件,使误差系统均方指数稳定并达到鲁棒H∞干扰抑制水平,同时给出基于观测残差均方值的故障检测策略。通过仿真算例验证了所提出方法的有效性。     

Fast fault diagnosis for industrial processes applied to the reliable operation of robotic systems

Fault detection, identification and monitoring play a primary role in systems engineering. This also holds for advanced processes, such as robotic systems, with highest demands on reliability and safety. A method is presented for fast fault detection and location, due to the data deweighting in the identification procedure of the monitored process parameters and the iterative on-line calculation of the statistics of the fault detection scheme. This method has many advantages for microcomputer applications and guarantees a very early process fault detection. Application of the method to the fast fault detection of the de motor actuators of a robotic system is described.     

基于输出时滞方法的非均匀采样数据系统传感器故障检测(英文)

研究一般非均匀采样数据系统鲁棒传感器故障检测设计问题.首先,基于输出时滞方法将非均匀采样数据系统转换成具有时变时滞输出的连续系统;然后,选择故障检测滤波器作为残差产生器,并将故障检测设计问题描述成一个多目标优化问题,即连续时间过程噪声和离散时间测量噪声对残差信号的H∞范数小于一个给定值,同时传感器故障对残差信号的l2增益大于一个给定值,基于输入输出方法以矩阵不等式的形式给出该多目标优化问题有解的充分条件;进一步的,提出一个迭代算法来权衡噪声鲁棒性与故障灵敏度,并将矩阵不等式转换成可解的线性矩阵不等式.最后,对某型飞控系统的仿真实验验证了所提方法的有效性.     

采样数据系统故障检测的H∞方法

提出一种对采样数据系统进行故障检测的H_∞方法. 首先构造离散时间故障检测系统, 分析残差产生器的动态特性. 然后从故障检测的角度引入算子, 定量考察连续时间未知输入信号和故障信号对离散时间残差信号的影响. 最后优化残差产生器的参数, 使得故障检测系统对未知输入具有较强的鲁棒性, 同时对故障具有较高的灵敏度.     

Fault detection for nonlinear discrete-time systems via deterministic learning

Recently, an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems. In this paper, a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning. By using a discrete-time extension of deterministic learning algorithm, the general fault functions (i.e., the internal dynamics) underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function (RBF) networks. Then, a bank of estimators with the obtained knowledge of system dynamics embedded is constructed, and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems. A fault detection decision scheme is presented according to the smallest residual principle, i.e., the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals. The fault detectability analysis is carried out and the upper bound of detection time is derived. A simulation example is given to illustrate the effectiveness of the proposed scheme.     

计算机网络故障检测

计算机网络故障种类较多,计算机网络故障检测是一个复杂的系统工程,不仅对网络故障处理步骤有所要求,而且对网络故障分析方法以及计算机故障维护都有极高的要求。为此,本文也主要从这几个方面对计算机网络故障检测进行分析。     

An OSGi-based flexible and adaptive pervasive cloud infrastructure

There is a trend that different computing paradigms such as cloud computing, pervasive, and mobile computing are converging with each other. Due to this convergence, there arise unprecedented complexities, such as huge number of computing devices, flexibilities and adaptation of service infrastructure （infrastructure elasticity） for fitting dynamics of large smart city applications, expectations of powerful computing and storage capabilities on handhold devices, and so on. Therefore, a supporting infrastructure is needed, which can flexibly switch services at run time and can be used to enhance capabilities for small devices through component/service migrations. In this paper, we propose an elastic open service gateway initiative （OSGi）-based pervasive cloud （OSGi-PC） infrastructure which can make use of both the cloud computing capabilities and the component flexibilities from OSCi. OSGi-PC provides flexible management of component migrations between small devices themselves and powerful nodes in between, which is remaining a critical challenge for enabling mobile clouds. We have evaluated the OSGi-PC in terms of performance for adaptive service provision, and power consumption during service adaptation, performance and power consumption for component migrations in different scenarios, which show the usability of OSGi-PC.     

Discrete time-varying fault detection filter for non-uniformly sampled-data systems

This paper investigates the fault detection problem for non-uniformly sampled-data systems. No periodic assumption is made for the sampling instants. In contrast to most currently available resuks that are limited to strictly proper systems, measurement noises are considered. With the operators introduced to capture the inter-sampling behaviors of disturbances and faults, an omine fault detection algorithm is first derived to optimize the ratio-type design objective. It is then equivalently transformed into a recursive algorithm consisting of a discrete time-varying fault detection filter and the corresponding residual evaluation function. As repeated computation of the parity vectors is avoided, the proposed fault detection filter can help reduce the online computational burden with comparison to the existing parity relation based fault detection method.