多信号模型故障模式与信号概率关联算法

为适应复杂装备的测试性建模,多信号模型采用层次化的建模方式.针对建模过程中信号概率和故障模式(功能故障和完全故障)概率的冲突问题,提出了三种概率关联更新算法,并通过某装备模块多信号模型的故障模式及信号概率的更新验证了算法的有效性.算法提高了测试性分析与评估的精度,为模型的修正和故障诊断策略的生成提供了更加可靠的依据.     

基于DR-BN的冷水机组故障检测

在冷水机组现场的故障数据通常难以获得,这是导致基于多分类算法的故障检测方法未被广泛现场应用的主要障碍之一。本文将距离拒绝(DR)机制融入贝叶斯网络(BN)中,将冷水机组故障检测转化为一类划分问题,提出一种基于DR-BN的冷水机组故障检测方法,该方法仅使用正常数据训练模型,从而有效克服上述障碍。本文通过使用ASHRAE RP-1043的故障实验数据对提出方法的性能进行验证,并与传统方法的性能进行了对比,可知基于DR-BN的模型具有更高的故障检测性能,尤其对低劣化等级下的故障,故障检测正确率最高时可高出94%。     

统计过程控制在无人机故障预报系统中的应用

无人机故障预报是无人机故障检测的重要组成部分之一,它能有效地减小无人机的故障发生率。利用统计过程控制(SPC)理论,从数理统计的角度对某无人机的测试数据进行分析,并根据常见的五种故障数据,设计了一套无人机故障预报系统,同时给出了系统的工作原理和具体的实现过程。本系统有助于技术人员发现无人机的潜在故障并判断检测设备是否虚警,具有良好的使用性能和价值。     

测试性建模中故障模式概率更新算法研究

为适应复杂装备全寿命周期内的测试性分析与评估,测试性建模中多采用层次化的建模方式．针对建模过程中信号(功能)概率和故障模式概率的冲突问题,提出了两种故障模式概率更新算法,并通过某装备模块的故障模式概率的更新验证了算法的有效性．提高了测试性分析与评估的精度,为模型的修正和故障诊断策略的生成提供了更加可靠的依据．     

基于单类支持向量机的冷水机组温度传感器故障检测

冷水机组系统中,温度传感器出现故障会严重影响机组工作效率及使用寿命。针对冷水机组温度传感器偏差故障,本文提出一种基于单类支持向量机(one-class support vector machine,OC-SVM)的故障检测方法,采用冷水机组正常数据建立OCSVM模型,通过十折交叉验证法获得模型优化参数。分别采用工程实测数据和实验数据(共4组)对该方法进行了验证,结果表明:基于OC-SVM的方法能有效检测出4组冷水机组的温度传感器偏差故障。其中对于螺杆式冷水机组(数据集Ⅰ)的故障检测效果明显,当冷冻水侧温度传感器偏差故障幅值绝对值大于1℃时,检测效率达到100%。     

核主成分分析的高炉故障检测研究

由于高炉冶炼系统的复杂性,传统的故障检测方法在高炉故障检测中的应用效果不佳.同时,高炉冶炼过程中的数据具有明显的非线性特征,利用主成分分析(PCA)等线性多元统计方法也难以取得良好的故障检测效果.针对这种情况,提出了利用核主成分分析(KPCA)方法对高炉冶炼过程中的故障进行检测,以适应高炉的非线性特征,实现对高炉故障的快速检测.     

基于电压畸变观测的PMSM逆变器故障检测方法

针对永磁同步电机(PMSM)驱动系统中脉宽调制(PWM)电压源型逆变器(VSI)容易发生开路故障的问题,提出一种简单且低成本的开路故障检测方法。首先,对PWM逆变器中功率开关发生开路故障时引起的对应端电压变化进行分析,构建开路故障与各相电压波形畸变的关系模型。然后,通过观测各相的电压畸变,并与阈值进行比较来构建各相的布尔错误模型。最后,基于布尔错误模型和错误检测时间来检测故障状态并识别故障开关。同时对检测时间进行分析,避免故障误报。实验结果表明:该方法能够快速且有效地进行故障诊断,具有良好的可行性和实用价值。     

基于主元分析的制冷系统渐变故障检测

针对制冷系统渐变故障的特性和监测参数维数过大、变量间相关严重、易受干扰而信噪比低造成系统故障检测困难的难题,建立了基于主元分析的制冷系统渐变故障检测模型.应用实验数据对模型进行了验证和评估.结果表明,基于主元分析的制冷系统渐变故障检测模型具有良好的检测性能,对早期故障具有非常好的敏感性和及时捕捉的能力,并能够清晰地呈现出渐变故障发展的过程和趋势.     

计算机故障检测中数据挖掘的运用分析

伴随计算机技术的迅速发展和广泛应用,计算机故障检测难题逐渐被人们所关注。为了提高计算机故障检测效率,本文将数据挖掘技术引入到了计算机实验室管理当中,通过创建挖掘模型,提取相关数据能够有效查询计算机故障信息,准确实现了故障预测查询,对提升计算机故障检测效率大有裨益。     

基于主元分析的多联式空调系统传感器故障检测和诊断

作为多元数据分析方法之一,主元分析(PCA)被广泛运用于诊断制冷空调系统的传感器故障。本文首先结合热平衡原理以及多联机运行的控制逻辑,筛选系统中常用的18个传感器变量建立多联机(VRF)传感器的故障分析(FDD)模型。然后结合主元分析的算法原理,给出以Q统计量和Q贡献率为检验标准的传感器故障检测与诊断流程。接着用实测数据进行验证工作,引入不同类型和程度的传感器故障,分析得到不同故障条件下的故障检测和诊断特性。结果表明,总体上,主元分析应用于多联机传感器故障检测与诊断过程是可靠的。其具体特征表现为：不同类型的传感器在不同故障类型及程度条件下,故障检测效果差异明显;在小偏差故障条件下,基于主元分析的传感器故障检测方法的故障检测效率较低,并且针对个别传感器而言,其整体故障检测效率偏低。鉴于故障诊断是基于故障检测的结果,因此上述故障检测方法在FDD过程中将起到重要的作用。     

Computer-aided fault tree synthesis III: Real-time fault location

This paper is devoted to fault tree synthesis and is split up into three parts. Part I starts with the introduction of component models that show all fault propagation through the components and fault initiation by the components in both directions (upstream and downstream). Subsequently, it is shown how to create system models that interconnect a system's components and environmental variables. Then a fault tree construction algorithm is introduced which is able to generate fault trees from the given system and component models in two steps. First a causal tree is constructed showing the propagation paths for all basic events leading to any deviation in the top parameter. All control loops (feedback and feedforward loops) in this causal tree must be traced prior to any fault tree construction since they might prevent some faults from reaching the top parameter. They consequently require a special treatment. Part I ends showing how to adapt the causal trees for these loops. Part II discusses the final step of the fault tree construction algorithm, i.e. it shows how fault trees can be abstracted from the causal diagram, and ends with a comprehensive example. Finally, Part III discusses a method for real-time fault location which is based on the causal tree construction procedure introduced in Part I.     

Robotic fault detection and fault tolerance: A survey

Fault tolerance is increasingly important for robots, especially those in remote or hazardous environments. Robots need the ability to effectively detect and tolerate internal failures in order to continue performing their tasks without the need for immediate human intervention. Recently, there has been a surge of interest in robot fault tolerance, and the subject has been investigated from a number of points of view. Ongoing research performs off-line and on-line failure analyses of robotic systems, develops fault-tolerant control environments, and derives fault detection and error recovery techniques using hardware, kinematic, or functional redundancy. This paper presents a summary of the current, limited, state-of-the-art in fault-tolerant robotics and offers some future possibilities for the field.     

Integrated system fault diagnostics utilising digraph and fault tree-based approaches

With the growing intolerance to failures within systems, the issue of fault diagnosis has become ever prevalent. Information concerning these possible failures can help to minimise the disruption to the functionality of the system by allowing quick rectification. Traditional approaches to fault diagnosis within engineering systems have focused on sequential testing procedures and real-time mechanisms. Both methods have been predominantly limited to single fault causes. Latest approaches also consider the issue of multiple faults in reflection to the characteristics of modern day systems designed for high reliability. In addition, a diagnostic capability is required in real time and for changeable system functionality. This paper focuses on two approaches which have been developed to cater for the demands of diagnosis within current engineering systems, namely application of the fault tree analysis technique and the method of digraphs. Both use a comparative approach to consider differences between actual system behaviour and that expected. The procedural guidelines are discussed for each method, with an experimental aircraft fuel system used to test and demonstrate the features of the techniques. The effectiveness of the approaches is compared and their future potential highlighted.     

Design fault tolerance

Typical software fault tolerance techniques are modeled on successful hardware fault tolerance techniques. The software fault tolerance techniques rely on design redundancy to tolerate residual design faults in the software; the hardware fault tolerance techniques rely on component redundancy to tolerate physical degradation in the hardware. Investigations of design redundant software have revealed difficulties in adapting the hardware strategy to software.We survey three categories of issues: (1) practical issues in the implementation of design-redundant software, (2) economic considerations for the development and maintenance of multiple software implementations, and (3) assessment difficulties in measuring and predicting the performance of design-redundant software. All of these issues should be considered by would- be developers of design-redundant software to justify use of the technique.     

Design of fault simulator

Fault simulator is proposed to understand and evaluate all possible fault propagation scenarios, which is an essential part of safety design and operation design and support of chemical/production processes. Process models are constructed and integrated with fault models, which are formulated in qualitative manner using fault semantic networks (FSN). Trend analysis techniques are used to map real time and simulation quantitative data into qualitative fault models for better decision support and tuning of FSN. The design of the proposed fault simulator is described and applied on experimental plant (G-Plant) to diagnose several fault scenarios. The proposed fault simulator will enable industrial plants to specify and validate safety requirements as part of safety system design as well as to support recovery and shutdown operation and disaster management.     

基于瞬时故障特征频率趋势线和故障特征阶比模板的变转速滚动轴承故障诊断

针对难以从滚动轴承的时频分布中提取瞬时转频分量的问题,本文利用由轴承包络时频谱中提取的瞬时故障特征频率替代传统瞬时转频实现重采样,进而基于故障特征因子与转频阶比边带构造故障特征阶比模板以实现变转速运行模式下滚动轴承故障诊断。其具体算法由以下四个部分组成：首先,联合应用谱峭度滤波算法与短时傅里叶变换得到能够突出瞬时故障特征频率的包络时频谱;其次,提出基于幅值重调的峰值搜索算法对瞬时故障特征趋势线进行提取;再次,以瞬时故障特征频率趋势线为基础对原信号进行故障相角域重采样并得到故障特征阶比谱;最后,根据被监测轴承的故障特征因子构造故障特征阶比模板对滚动轴承的运行状态与故障类别进行判断。仿真算例和应用实例将对该算法的有效性予以证明。     

A dynamic fault tree

The fault tree analysis is a widely used method for evaluation of systems reliability and nuclear power plants safety. This paper presents a new method, which represents extension of the classic fault tree with the time requirements. The dynamic fault tree offers a range of risk informed applications. The results show that application of dynamic fault tree may reduce the system unavailability, e.g. by the proper arrangement of outages of safety equipment. The findings suggest that dynamic fault tree is a useful tool to expand and upgrade the existing models and knowledge obtained from probabilistic safety assessment with additional and time dependent information to further reduce the plant risk.     

基于故障字典法的电路板在线故障诊断系统

阐述了将故障定位到电路板元器件的实现方法;对系统上、下位机实现形式的组成、功能、通讯方式、通讯协议和特点进行了详细说明;详细介绍了基于规则的逻辑推理和基于控制策略方式的故障字典方法在本系统中的应用阐述了该系统的软、硬件组成部分的构成和各模块功能.     

故障树分析法在某型飞机火控系统故障诊断中的应用

故障树分析法是系统安全、可靠性分析研究中常用的一种方法。基于故障树分析法与专家系统相结合的某型飞机火控系统故障诊断仪,以机载火控系统不工作为顶事件,建立了故障树,并对故障树作了定性分析,本系统不但具有故障诊断能力,还具有较强的自学习的功能。结果表明,故障树分析法是机载火控系统故障诊断的一种有效方法。     

Decision tree-based fault zone identification and fault classification in flexible AC transmissions-based transmission line

Transmission line distance relaying for flexible AC transmission lines (FACTS) including thyristor controlled series compensator (TCSC), STATCOM, SVC and unified power flow controller (UPFC) has been a very challenging task. A new approach for fault zone identification and fault classification for TCSC and UPFC line using decision tree (DT) is presented. One cycle post fault current and voltage samples from the fault inception are used as input vectors against target output dasia1psila for fault after TCSC/UPFC and dasia0psila for fault before TCSC/UPFC for fault zone identification. Similarly, the DT-based classification algorithm takes one cycle data from fault inception of three phase currents along with zero-sequence current and voltage, and constructs the optimal DT for classifying all ten types of shunt faults in the transmission line fault process. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network including noisy environment. The results indicate that the proposed method can reliably identify the fault zone and classify faults in the FACTs-based transmission line in large power network.