计算机系统维护

计算机系统维护是指对计算机系统做定期检测以及优化和修理工作,是延长计算机使用寿命及提高计算机使用效率的重要措施.计算机系统维护主要体现在两方面——硬件系统的维护和软件系统的维护.当计算机出现故障时,应先检查并排除是否是软件故障,再去排查硬件故障.本文将分别从硬件和软件两个方面介绍计算机系统的组成、工作环境、常见的故障及检修方法,以及计算机的日常维护和保养方法.     

微型计算机系统故障的维修方法

本文对微型计算机系统故障的维修方法从硬件方面和软件方面分别进行阐述，对微型计算机系统的故障，首先要根据故障现象区分是硬件的故障还是软件的故障，对硬件故障的检修，先用“最小系统法”压缩到板块级，然后再用“原理检测法”检查到故障部位，直到排除故障。对软件引起的故障，则应从系统软件，应用软件，设置参数和计算机病毒等方面查找故障原因，并采取相应的修复措施，软件维修中应注意清除计算机病毒，恢复硬盘中的程序。     

软件密集型装备故障的静态检测

软件密集型装备是指软件和硬件肾密结合的装备,软件密集型装备中的故障多由软件与硬件相互作用引发.本文对程序流程违反硬件工作时序引起的故障进行了分析,并建立了故障模型,提出了一种静态故障检测方法,该方法通过对程序控制流进行分析,以检测程序流程是否违反硬件工作时序.     

刍议计算机硬件故障的识别与处理

当今社会是一个信息的社会,计算机被人们广泛使用,计算机的硬件出现故障的几率和原因也在增加,这个给我们的工作和生活带来了一定的损失。怎样快速识别出计算机的硬件故障以及怎样正确的处理这些影响变得意义重大起来。本文先是对计算机硬件故障进行了几类分类,然后讲解了硬件故障识别处理的一般方法和常见硬件的故障识别处理方法。     

DFTA在软硬件相关故障诊断中的应用研究

软件密集型系统中由于有大量软件的嵌入,其故障模式发生了变化,产生了新的软硬件相关的故障模式。在分析软硬件相关故障特征的基础上,提出了一种基于动态故障树分析方法的故障诊断方案,并给出了分析方法和步骤。最后通过实例分析,证明了这种方法的可行性。     

计算机软硬件故障检测与维修

计算机因自然或人为原因,极易出现各种软硬件故障,这给使用者带来了很多麻烦,一旦出现故障则影响计算机的正常运行,造成了使用者的不便,在计算机的故障中主要分为两类：软件系统错误、硬件物理损坏,即软件故障与硬件故障两类。熟悉和掌握计算机软硬件故障的检测、维修方法对计算机的使用者来说是非常重要的。本文对计算机的软硬件故障检测与维修进行简单的分析。     

计算机日常维护及常用方法探讨

计算机的安全运行会直接影响到用户的使用效果.计算机是一个复杂的系统,在实际应用过程中很可能会由于各种各样的问题而出现一些故障,比如丢失数据、计算机系统中毒、无法启动、计算机系统瘫痪、文件损失等,如果没有及时采取相应的维护及处理措施,可能会缩短计算机的使用寿命,难以充分发挥计算机的效能以及价值.做好计算机的日常维护工作,尽可能减少计算机故障发生,提高计算机运行速度都显得尤为重要.介绍了计算机在日常生活中的实际应用,分析了计算机常见的硬件故障以及软件故障,并且提出了相应的维护方法.     

基于Simics的系统级故障注入平台

故障注入技术是评价系统可靠性的有效方法。现有基于仿真的故障注入平台大多基于现场可编程门阵列或超高速集成电路硬件描述语言实现,对故障模型的支持非常有限。为此,基于Simics结构级模拟器,设计并实现系统级硬件故障注入平台。该平台上层支持不同固件、操作系统以及应用程序,底层支持对处理器典型流水部件的故障注入,同时实现瞬时故障、永久故障和间歇故障模型以及其他较全面的故障类型,并将一组系统级故障检测机制集成入平台中。实验通过监测硬件故障在系统级的传播,对比分析了故障对不同部件造成的系统级影响,结果表明,瞬时故障对系统影响较小,永久故障容易引起系统失效,间歇故障对各部件有不同程度的干扰作用。     

计算机系统中常见故障及相关维护方法探析

随着科学技术的飞速发展,所有工作都通过计算机来完成。因此如何保养和维护计算机系统越来越被关注,该文简要说明了计算机系统常见的硬件和软件故障及产生的原因,同时也介绍了日常维护和有些故障避免措施以及使用过程中的注意事项。     

基于机器学习的CT设备故障自动化识别系统

CT设备由于故障点数量存在不确定性,导致故障识别率低,提出基于机器学习的CT设备故障自动化识别系统。根据故障特征,设计系统硬件的框架和功能模块;软件单元设计根据CT设备正常状态信号分布,设定故障判定依据,采用神经网络算法,检测和划分故障信号的类型,结合大数据融合算法,优化故障识别系统,输出识别结果,实现CT设备故障自动化识别。实验结果表明：设计的故障自动化识别系统将电压波动控制在0值上下,峰值控制区间在[-0.5,0.5],并且最低故障识别准确率达到了90.3%,准确识别和划分了故障类型,因此,设计系统有效提升了故障识别的准确率和抗干扰能力,运行稳定性好。     

Emulation of Software Faults: A Field Data Study and a Practical Approach

The injection of faults has been widely used to evaluate fault tolerance mechanisms and to assess the impact of faults in computer systems. However, the injection of software faults is not as well understood as other classes of faults (e.g., hardware faults). In this paper, we analyze how software faults can be injected (emulated) in a source-code independent manner. We specifically address important emulation requirements such as fault representativeness and emulation accuracy. We start with the analysis of an extensive collection of real software faults. We observed that a large percentage of faults falls into well-defined classes and can be characterized in a very precise way, allowing accurate emulation of software faults through a small set of emulation operators. A new software fault injection technique (G-SWFIT) based on emulation operators derived from the field study is proposed. This technique consists of finding key programming structures at the machine code-level where high-level software faults can be emulated. The fault-emulation accuracy of this technique is shown. This work also includes a study on the key aspects that may impact the technique accuracy. The portability of the technique is also discussed and it is shown that a high degree of portability can be achieved     

基于RTLinux/Linux的计算机联锁系统可靠性分析

针对计算机联锁系统故障诊断多基于维修人员实际经验,无法实现快速、精准定位故障,基于贝叶斯网络在表达不确定性知识上的优势,提出了一套完善的计算机联锁系统故障的诊断方法和实现流程.首先,运用贝叶斯算法的理论知识,搭建了基于贝叶斯算法的计算机联锁系统故障诊断模型;然后对计算机联锁系统的整体架构和硬件、软件组成进行搭建.最后,...     

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。     

Connectivity Testing

This paper presents a new approach, connectivity testing, for testing embedded systems. Instead of testing the conformance of a system against its specification, which often turns out to be infeasible, we suggest to test only the composition of the software and the hardware. We assume the software to be correct so only the hardware component may be erroneous. Our framework is based on the notion of a (single fault) fault model, that is a model which formally captures errors in the interface between the hardware and the software. Output and input fault models are considered. An exhaustive test suite for a fault model is a test suite that detects the faults of the model with 100% coverage. We prove the problem of computing a smallest exhaustive test suite to be NP-hard and devise heuristic polynomial time algorithms computing minimal exhaustive test suites. We have carried out experiments with the algorithms implemented using Binary Decision Diagrams. Test generation from specifications containing more than 4.98 × 10¹⁰ states have been carried out.     

基于传感器模型的安全计算机仿真系统

在安全计算机信号系统的研发与测试中,没有真实的传感器接入会带来测试难、验证难等问题。为此,设计一种基于传感器模型的轨道交通安全计算机平台仿真系统。以真实的传感器为模型基础进行传感器建模,控制真实的硬件输出,通过软硬件结合的方法为安全计算机平台提供真实的输入与输出。同时利用软件修改模型参数,模拟传感器故障,为安全计算机平台提供无损坏的故障注入。测试结果证明,该仿真系统能够有效验证安全计算机平台的安全性、可靠性与可用性,加快安全计算机平台的开发进程。     

An Optimal Approach to Fault Tolerant Software Systems Design

A systematic method of providing software system fault recovery with maximal fault coverage subject to resource constraints of overall recovery cost and additional fault rate is presented. This method is based on a model for software systems which provides a measure of the fault coverage properties of the system in the presence of computer hardware faults. Techniques for system parameter measurements are given. An optimization problem results which is a doubly-constrained 0,1 Knapsack problem. Quantitative results are presented demonstrating the effectiveness of the approach.     

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。     

Fault tolerance in supervisory control systems: a knowledge-based approach

Fault tolerance in computerized systems involved in production has become an ever more important requirement. Existing fault tolerance approaches, wherever used, deal mainly with hardware faults. Nevertheless, the vast majority of contemporary system failures are software related. This paper introduces a knowledge-based approach to handling software related faults occurring in supervisory control systems. These systems are event driven and use data, stored in complex databases, to react to events coming from different kinds of devices by identifying, scheduling, initiating and monitoring operations. Failure of part of the supervisory control system's software to behave rationally when unexpected events occur is called an application fault. The approach introduced in this paper is based on a supervisory control system reference model which reveals the set of all possible application faults together with the major functions of the recovery processes associated with each fault, and leads to a high-level knowledge-based system architecture capable of handling every fault-related condition. This system is called PROFIT (Intelligent PROduction systems Fault Tolerance) and consists of three main components: the fault diagnosis module, the instant fault correction module and the learning module, co-ordinated by a PROFIT meta-level module. The prototype version of PROFIT is analysed and the development as well as the run-time environment that prove the applicability and effectiveness of the system are presented.     

电脑故障分类及其领域知识的表达

在电脑软硬件故障的分类和特点的基础上,抽象出其故障知识的表达模型。采用了基于模糊产生式规则的方法来表达其领域知识,同时阐述了该方法中模糊系数和权系数的确定过程,给出了一个典型的具体实例。证明了该方法的合理性和有效性,实现了一种与电脑故障实际情况相符的较为合理的知识表达方法。     

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.