On fault coverage of tests for finite state specifications

Testing is a trade-off between increased confidence in the correctness of the implementation under test and constraints on the amount of time and effort that can be spent in testing. Therefore, the coverage, or adequacy of the test suite, becomes a very important issue. In this paper, we analyze basic ideas underlying the techniques for fault coverage analysis and assurance mainly developed in the context of protocol conformance testing based on finite state models. Special attention is paid to parameters which determine the testability of a given specification and influence the length of a test suite which guarantees complete fault coverage. We also point out certain issues which need further study.     

A dynamic code coverage approach to maximize fault localization efficiency

Spectrum-based fault localization is amongst the most effective techniques for automatic fault localization. However, abstractions of program execution traces, one of the required inputs for this technique, require instrumentation of the software under test at a statement level of granularity in order to compute a list of potential faulty statements. This introduces a considerable overhead in the fault localization process, which can even become prohibitive in, e.g., resource constrained environments. To counter this problem, we propose a new approach, coined dynamic code coverage (DCC), aimed at reducing this instrumentation overhead. This technique, by means of using coarser instrumentation, starts by analyzing coverage traces for large components of the system under test. It then progressively increases the instrumentation detail for faulty components, until the statement level of detail is reached. To assess the validity of our proposed approach, an empirical evaluation was performed, injecting faults in six real-world software projects. The empirical evaluation demonstrates that the dynamic code coverage approach reduces the execution overhead that exists in spectrum-based fault localization, and even presents a more concise potential fault ranking to the user. We have observed execution time reductions of 27% on average and diagnostic report size reductions of 77% on average.     

一种新的故障检测与诊断方法及其应用研究

给出了在故障检测与诊断中采用经验模式分解与希尔伯特变换相结合的方法。经验模式分解不同于小波变换、KL变换、奇异值分解(SVD)等信号分解方法,它把数据序列分解为能够表示数据中嵌入的不同振荡的本征模式函数。首先介绍方法的原理与特点,然后将该方法用于齿轮故障的探测与诊断,结果显示,这种方法能准确识别出裂缝故障的特征频率。     

一种轴流通风机故障诊断方法

针对现有基于谱分析的轴流通风机故障诊断方法只将故障类型和频谱特征值进行简单关联而导致诊断效果较差的问题,提出了一种基于矢椭谱和隐Markov模型的轴流通风机故障诊断方法。该方法首先将轴流通风机同一截面内互相垂直的2个振动信号在时域上直接融合为复信号,并对该复信号进行快速Fourier变换,获得多个特征频率下振动信号的全谱幅值;然后用不同故障状态下振动信号的全谱幅值训练隐Markov模型;最后以实时振动信号的全谱幅值作为隐Markov模型输入量,采用Viterbi算法计算隐Markov模型输出的似然概率,根据最大似然概率对数判断故障类型,避免了将振动幅值和故障类型进行简单关联。试验结果表明,该方法的故障诊断正确率达90%以上。     

A neural network-based method for coverage measurement of shot-peened panels

Neural Computing and Applications - Shot peening is a cold metal working process to improve the material strength, reduce corrosion fatigue and prevent fracture. Measuring the coverage level is an...     

A fault detection and isolation method for complex industrialsystems

A method for the fault detection in complex industrial systems is presented. The plant devices, the sensors, the actuators, and diagnostic tests are described as discrete-event systems. A formal composition rule of these models is given and an inference procedure for the identification and isolation of the faults is discussed. Temporal information is also considered to speed up the fault isolation procedure and, in some cases, to uniquely identify faults with the same static fault signature     

一种煤矿机械轴承故障诊断方法

针对现有煤矿机械轴承故障自适应诊断方法易受高频噪声和间断噪声干扰而导致原始信号分解和特征提取精度较低的问题,提出了一种基于改进局部均值分解的煤矿机械轴承故障诊断方法。该方法在局部均值分解方法的自适应分解部分采用噪声辅助分解方法,将高斯白噪声加入原始信号,然后进行局部均值分解,以抑制高频噪声及间断噪声对信号分解的影响;在特征参数提取部分对乘积函数分量进行Hilbert变换,然后进行特征参数提取,以实现在全部取值范围内提取特征参数。仿真及测试结果表明,该方法对轴承故障信号分解和特征参数提取的效果较好,对轴承内外圈故障诊断的准确性较高。     

A matting method based on full feature coverage

Multimedia Tools and Applications - The sampling-based matting method is an important method for image matting. There are three key techniques in sampling-based matting: 1) how to build a...     

A method for fault detection using parameter and state estimation

In this paper, fault detection in linear dynamic systems based on analytical redundancy and identification of continuous time models, is considered. Particular emphasis is placed on the realisation of state variable filters by mean of interpolation techniques. Two examples illustrate interpolation for different noise levels.     

一种容差模拟电路的分级故障诊断方法

提出了一种基于分级故障字典的容差模拟电路故障诊断方法;在测前首先对待诊断电路进行最坏情况分析得到容差电路输出电压的正常区间,并存为第一级故障字典用于电路故障检测;再利用节点电压灵敏度序列作为故障特征建立用于定位故障元件的第二级故障字典;最后通过测前划分元件参数的七段式仿真建立用于故障元件参数区间识别的第三级故障字典;在测后实施故障诊断时可根据需要依次查阅字典,检测电路故障,定位故障元件及识别故障元件的参数区间;该方法能够提高测后故障诊断的效率,在实际应用中具有一定的参考价值;最后通过仿真实验验证了该方法的有效性。     

A fault recovery method for optical means of data exchange

We propose a method to speed up detection, neutralization, and recovery of multiple faults in means of data exchange in controlling and measuring distributed structures that operate in the “floating master—slaves” mode. We use double optical ring channels for communication with an opposite direction of signal transmission. We use fast distributed computations in order to control the state of the means of data exchange.     

一种新的光伏阵列故障诊断与定位方法

随着太阳能光伏应用规模的不断扩大,实时地对光伏阵列进行状态监控故障诊断与定位变得越来越重要.提出了一种新的光伏阵列传感器嵌入方法,在保证故障定位精度的基础上减少了传感器的使用量.利用模糊群体决策理论对系统所获取的数据进行综合处理,解决了诊断中各检测量之间的一致性和决策问题.实验结果证明:提出的光伏阵列故障诊断与定位方法有较强的抗干扰能力,提高了诊断的准确性和可靠性.     

一种小电流接地系统故障选线方法

针对小电流接地系统故障选线方法难以适用于不同接地方式的问题,提出了一种基于粒子群优化RBF神经网络的小电流接地系统故障选线方法。该方法利用粒子群优化算法来优化RBF神经网络,将RBF神经网络要确定的参数作为粒子群优化算法的粒子。仿真结果表明,该方法收敛效率高、误差平方和小、选线准确率高、灵敏度高,具有一定的可行性。     

一致性分类问题中规则提取的新方法

研究了一致性分类问题中挖掘分类规则的算法,提出了一种基于属性重要性及partition的分类规则的新方法。首先根据属性的重要性度量选择一个属性,根据该属性与决策属性构造信息系统的分区,然后对每一个分区进行描述,并根据是否会在信息系统中产生冲突对描述进行简化,最后根据简化后的描述形成分类规则。     

A library for constraint consistent learning

This paper introduces the first, open source software library for Constraint Consistent Learning (CCL). It implements a family of data-driven methods that are capable of (i) learning state-independent and -dependent constraints, (ii) decomposing the behaviour of redundant systems into task- and null-space parts, and (iii) uncovering the underlying null space control policy. It is a tool to analyse and decompose many everyday tasks, such as wiping, reaching and drawing. The library also includes several tutorials that demonstrate its use with both simulated and real world data in a systematic way. This paper documents the methods contained within the library, including the implementations of said methods in tutorials and associated helper methods. The software is made freely available to the community, to enable code reuse and allow users to gain in-depth experience in statistical learning in this area.     

一种交通视频专网故障定位方法*

针对复杂异构网络环境中故障定位难的问题,采用代理技术,分析视频专网服务质量（QoS）,提出了一种基于改进代码书的网络故障定位算法。该算法采用视频QoS阈值进行故障事件检测,引入时间参数,建立具有三个维度参数的专网故障代码书,实现了视频监控质量评估与网络故障的快速定位。实验结果表明该算法可实现文中的故障事件实例检测,准确率比原有方法有较大提升,是一种有效的故障定位方法。     

一种复合故障预测动态建模方法

本文提出一种复合故障预测动态建模方法,将原有故障数据样本经过参数相关性分析,按特征输入支持向量回归机建立相应的故障预测模型,可以很有效预测未来较长时间的数据变化.对航空发动机的飞行参数进行预测的结果表明,该方法的预测精度优于现有的支持向量回归方法.     

故障诊断的信息融合方法

对基于信息融合的故障诊断方法进行综述．首先简要阐述信息融合的基本概念以及信息融合与故障诊断的关系;然后介绍贝叶斯定理融合故障诊断、模糊融合故障诊断、证据理论融合故障诊断、神经网络融合故障诊断和集成信息融合故障诊断方法的诊断原理与步骤。并分析其特点和局限性;最后给出了信息融合故障诊断研究的若干发展方向．     

一种带式输送机故障诊断方法

针对传统浅层神经网络用于带式输送机故障诊断时存在故障状态样本数据不足、准确率不高等问题,提出了一种基于合成少数类过采样技术(SMOTE)和深度置信网络(DBN)的带式输送机故障诊断方法.该方法利用SMOTE生成带式输送机故障状态样本数据,克服样本数据分布不平衡现象;将样本数据输入DBN,利用无监督逐层训练方式提取数据中的故障特征,并通过有监督微调来优化故障诊断能力,实现带式输送机故障精确诊断.仿真结果表明,该方法提高了带式输送机故障诊断准确率.     

一种滚动轴承故障诊断方法

针对基于支持向量机的滚动轴承故障诊断方法中支持向量机的参数优化问题,提出一种改进的果蝇优化算法,即以模式分类准确率作为果蝇味道浓度函数,并采用该算法来优化支持向量机模型的惩罚因子和核函数参数;基于改进果蝇优化算法和支持向量机对滚动轴承的故障模式进行分类诊断,结果表明改进的果蝇优化算法具有较高的收敛速度和寻优效率,基于该算法和支持向量机的滚动轴承故障诊断方法具有较高的分类准确率。