基于非解析模型的动态系统故障预报技术

首先，介绍了动态系统故障预报技术的发展状况和重要性．然后，将现有的各种故障预报方法分为两类进行介绍，分析了各种故障预报方法的适用范围以及优缺点，其中着重讨论了基于非解析模型的故障预报方法．最后，探讨了这一领域中有待于进一步研究的若干问题和发展趋势．     

交互式全球集合预报产品数据管理应用研究

数值天气预报技术的成熟,日益增多的各种观测资料、预报产品成为日益重要且紧迫的任务。本文详细介绍了交互式全球集合预报产品所采用的数据管理框架和方法,针对全球集合预报产品来源多样、报文格式复杂、数据量大的特点,从数据交换、数据处理和存储及数据服务三个层面讨论海量气象数据管理的方法和技术,使得高效便捷的数据服务和共享成为可能。     

基于案例推理的竖炉故障预报系统

为降低竖望炉焙烧过程的故障发生率,基于故障机理的分析,将过程参量预报与案例推理技术相集成,提出了竖炉焙烧过程的智能故障预报方法.参量量预报模型对不易在线连续测量但能反映故障征兆的关键工艺参数进行实时预报,在此基础上,采用案例推理技术对焙烧过程进行全面分析并给出一些典型故障发生的概率和操作指导.将所建立的故障预报系统成功应用于竖炉焙烧过程的生产实际中,故障发生率明显降低,取得了显著应用成效.     

基于小波变换的高压断路器机械故障监测

介绍了高压断路器机械方面和电气方面的各种状态监测方法,探讨了基于小波理论的故障检测技术.     

故障检测与诊断技术

本文介绍了故障检测与诊断技术的历史及发展状况,对其所用的各种方法分类进行了详细评述,指出了当前急待解决的若干前沿课题,并对这门技术的发展进行了展望。     

一种基于时序预报神经网络的故障预报方法及其应用

提出一种基于时序预报神经网络的工业过程故障预报方法,同时给出了描述神经网络预 报和外推能力的表达方式,并以氯碱电解工艺的现场数据验证了这种故障预报方法的有效性. 实验结果表明,该方法可成功地用以实现氯中含氢的24小时预报.     

动态系统故障诊断技术研究进展与展望

阐述了动态系统故障诊断技术的发展状况，对各种诊断方法进行详细的分类介绍及比较；最后对故障诊断技术的发展趋势和有待解决的问题进行了分析与探讨。     

多用户数据库管理系统故障恢复技术

本文提出一种基于更新事务和有缓冲区后映象文件的故障恢复技术，详细介绍了ＤＢＭＳ运行过程中事务故障，介质故障等各种故障的处理策略。     

动态可扩展操作系统的研究

动态扩展技术的研究能够解决操作系统设计中许多长期存在并难以解决的问题。这些问题包括：如何在提高应用程序的性能同时保证系统的安全性;如何提高操作系统使用的灵活性;如何包容和管理随着操作系统发展而不断增加的复杂性。文章对当前各种操作系统扩展类型进行详细分类,具体介绍了各种动态扩展技术,评估了不同的扩展技术对操作系统性能和安全性的影响,最后提出了需要研究的内容以及其中的关键问题。     

基于数据融合技术的多模型状态监测与故障预报

提出了将多传感器数据融合技术引入到工程系统的状态监测与故障预报领域，实现多模型监测，以提高故障预报的准确性和可靠性，并研究了将Bayes融合算法和自适应加权算法相结合实现多模型的决策融合。并针对一套石油化工工业的流化催化裂化装置(FCCU)仿真系统建立了多模型监测系统，融合的结果表明，对比于单一模型预报，通过使用融合算法实现多模型监测可以有效地提高预报的可靠性。     

基于动态灰色预测的网格故障检测算法

针对网格系统故障检测的需求，提出了一种基于动态灰色预测的网格故障检测算法。基于不可靠故障检测思想，建立了网格系统及故障检测模型；结合心跳策略和灰色预测方法，设计了一种动态心跳机制，并给出了预测模型和实时预测策略；提出了基于该动态心跳机制的网格故障检测算法，分析了算法的可靠性,并通过仿真实验验证了算法的正确性和有效性。     

A systematic review of machine learning techniques for software fault prediction

BackgroundSoftware fault prediction is the process of developing models that can be used by the software practitioners in the early phases of software development life cycle for detecting faulty constructs such as modules or classes. There are various machine learning techniques used in the past for predicting faults.MethodIn this study we perform a systematic review of studies from January 1991 to October 2013 in the literature that use the machine learning techniques for software fault prediction. We assess the performance capability of the machine learning techniques in existing research for software fault prediction. We also compare the performance of the machine learning techniques with the statistical techniques and other machine learning techniques. Further the strengths and weaknesses of machine learning techniques are summarized.ResultsIn this paper we have identified 64 primary studies and seven categories of the machine learning techniques. The results prove the prediction capability of the machine learning techniques for classifying module/class as fault prone or not fault prone. The models using the machine learning techniques for estimating software fault proneness outperform the traditional statistical models.ConclusionBased on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability for predicting software fault proneness and can be used by software practitioners and researchers. However, the application of the machine learning techniques in software fault prediction is still limited and more number of studies should be carried out in order to obtain well formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.     

Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem

Software quality engineering comprises of several quality assurance activities such as testing, formal verification, inspection, fault tolerance, and software fault prediction. Until now, many researchers developed and validated several fault prediction models by using machine learning and statistical techniques. There have been used different kinds of software metrics and diverse feature reduction techniques in order to improve the models’ performance. However, these studies did not investigate the effect of dataset size, metrics set, and feature selection techniques for software fault prediction. This study is focused on the high-performance fault predictors based on machine learning such as Random Forests and the algorithms based on a new computational intelligence approach called Artificial Immune Systems. We used public NASA datasets from the PROMISE repository to make our predictive models repeatable, refutable, and verifiable. The research questions were based on the effects of dataset size, metrics set, and feature selection techniques. In order to answer these questions, there were defined seven test groups. Additionally, nine classifiers were examined for each of the five public NASA datasets. According to this study, Random Forests provides the best prediction performance for large datasets and Naive Bayes is the best prediction algorithm for small datasets in terms of the Area Under Receiver Operating Characteristics Curve (AUC) evaluation parameter. The parallel implementation of Artificial Immune Recognition Systems (AIRS2Parallel) algorithm is the best Artificial Immune Systems paradigm-based algorithm when the method-level metrics are used.     

DTIF数据在数字电路故障诊断系统中的应用

为正确有效地使用IEEE标准的数字测试交换格式(DTIF),提高自动测试系统对数字电路进行故障诊断的水平和兼容性,深入分析和研究了该数据格式的结构和组成;在使用探测组数据进行探笔引导测试中提出智能动态关联网络技术,以提高探测效率;在使用故障字典组数据进行故障字典诊断中提出991匹配法则,以准确隔离故障集;通过对某数字电路的实际诊断,证明了这两种方法的有效性和准确可行性;该技术的实现对于DTIF数据格式的推广应用、数字电路故障诊断水平的提高具有重要意义。     

基于能量演化的航空发动机突发性故障预示模型设计

为减少突发性故障的发生及其对航空发动机的危害,提出了一种用于突发性故障预示的方法,提取系统能量特征,对其运行状态趋势进行预示。建立了突发性故障预示模型,应用能量耗散结构理论对该模型的演化过程与规律进行了描述。利用空停仿真数据对该模型进行了验证,结果表明,该模型具有较好的故障预示能力。     

基于核主元分析与神经网络的传感器故障诊断新方法

提出综合利用核函数主元分析(KPCA)和神经网络的方法实现非线性系统内传感器故障的检测和识别,克服了以往核函数主元分析法只能给出故障检测结果,却无法对故障进行识别的缺陷,并给出了在特征空间中计算残差的简单方法.最后,对一个简单的多变量过程进行了故障检测和识别,验证了这一诊断策略的有效性.     

基于KPCA和灰色模型的凝汽器故障预测

汽轮机凝汽器的故障预测为其故障自愈的研究提供了理论依据.提出一种基于核主元分析和灰色预测模型的汽轮机凝汽器故障预测方法,首次将灰色预测理论应用于凝汽器的故障预测.采用核主元分析法对故障特征数据进行分析和处理,提取反映故障的主要特征量,以降低特征变量之间的非线性相关性,同时减少灰色预测模型的预测参数的数目.然后应用灰色预测理论建立故障特征的预测模型,对每一个主要特征量的趋势值进行预测,重构故障特征向量,用于汽轮机凝汽器故障的预测分析.     

融合代码静态特征和频谱的软件缺陷定位技术

基于频谱的缺陷定位(spectrum-based fault localization, SBFL)通过分析测试用例的覆盖信息和执行结果信息进行快速定位,是目前最常用的缺陷定位技术。然而,该方法未能充分利用代码中隐含的语义和结构信息。若能将缺陷预测中使用到的代码结构信息和频谱信息融合使用,将有助于进一步提升缺陷定位的效果。为此,提出了一种融合代码静态特征和频谱的软件缺陷定位(fault localization combing static features and spectrums, FLFS)技术。首先,从Halstead等度量元集合中选取度量元指标并进行修改,以适用于度量代码的方法级特征;然后,根据选取的度量元指标提取程序中各个方法的静态特征并用于训练缺陷预测模型;最后,使用缺陷预测模型预测程序中各方法存在缺陷的预测可疑度,并与SBFL技术计算的频谱可疑度进行融合,以定位缺陷所在方法。为验证FLFS的有效性,将其与两种定位效果最好的SBFL技术DStar和Ochiai在Defects4J数据集上进行了对比实验。结果表明,FLFS具有更好的缺陷定位性能,对于E_inspe...     

Accelerated Techniques in Stem Fault Simulation

In order to cope with the most expensive stem fault simulation in fault simulation field.several accelerated techniques are presented in this paper.These techniques include static analysis on circuit structure in preprocessing stage and dynamic calculations in fault simulation stage.With these techniques,the area for stem for stem fault simulation and number of the stems requiring explicit fault simulation are greatly reduced,so that the entire fault simulation time is substantially decreased.Experimental results given in this paper show that the fault simulation algorithm using these techniques is of very high efficiency for both small and large numbers of test patterns.Especially with the increase of circuit gates,its effectiveness improves obviously.