FaLoFiN:基于不确定性过滤的硬件故障局部化方法

运行已有大型商业软件进行系统级验证是获得高质量SoC设计的有效手段,然而一旦硬件故障导致该类软件运行失效,故障局部化过程将十分困难.为此提出一种基于不确定性过滤的硬件故障局部化方法FaLoFiN.首先提取不确定性函数调用集;然后分别从参考运行和故障运行中过滤不确定性函数调用集;最后通过比较、定位异常函数调用,实现与硬件故障相关代码范围的局部化.实验结果表明,FaLoFiN方法具有较高的硬件故障局部化准确度,能有效地将PKUnity-86系统中的真实硬件故障局部化.     

基于暂态行波的铁路电力电缆故障定位系统设计与实现

本文基于人工神经网络(ANN)确定铁路电力电缆故障位置,采用基于阻抗的稳态信号或瞬态信号过程中特征提取的合适时间间隔方式.通过MATLAB对本文所设计模型进行了仿真,以传输线的单端获取的故障电流信号作为输入.该系统考虑了在不同运行条件下的不同类型的故障,提取了五个特征,分别是两个条件下最小到最大的增量、幅度、标准偏差、...     

基于更新补偿的实化视图增量过滤研究

针对WHIPS实化视图维护模型不能检测无效增量问题,提出了增量过滤机制.该机制利用视图定义中的约束条件,并通过更新补偿机制,对基表增量进行有效性检测和过滤,减少了网络通信并避免了数据仓库端的无效的视图增量计算.文章最后通过实验对比,证明了这种机制的有效性.     

支持向量机在反垃圾邮件过滤中的应用

垃圾邮件对计算机系统的安全和人们的生活造成了严重的威胁,反垃圾邮件问题已经成为的具有重要现实意义的研究课题.针对垃圾邮件过滤本质是分类问题,提出了一种基于服务器前端的反垃圾邮件过滤方法,它采用了改进的v支持向量机算法对邮件内容进行分类,过滤垃圾邮件.研究结果表明该方法与直接的支持向量机增量算法相比,提高了过滤的准确率,具有一定的应用价值.     

局部变量的存储赋值研究

DSP芯片通常提供了特定的内存地址生成单元(AGU)和包含地址运算的自增量/自减量的间接寻址模式.通过合理地安排变量在堆栈上的存储位置,尽可能多地使用自增量/自减量寻址方式来来访问局部变量,从而减少地址运算指令的数量,提高程序性能.本文详细地讨论了实现局部变量最优布局的算法,并且给出该算法在gcc上的实现.     

基于自适应学习的文本信息过滤系统

面对实时网络信息过滤的新挑战,自适应信息过滤能够解决问题。针对现有自适应系统的不足,提出了提高模板准确性的学习和过滤阈值优化的新方法。采用增量迭代学习算法来逼近真实的过滤模板,结合非法网页的分布函数自适应调整阈值,不断提高过滤精度,并运行于一个校园网关之上,取得了较好的效果。     

基于ICSVM的风电机组齿轮箱故障诊断

针对风电机组齿轮箱运行过程中故障样本缺乏、正常样本充裕的特点,提出基于增量代价敏感支持向量机（Incremental Cost-sensitive Support Vector Machine,ICSVM）的风电机组齿轮箱故障诊断方法。由于齿轮箱故障样本缺乏,建立以误分类代价最小化为目标的代价敏感支持向量机故障诊断模型;在增量训练代价敏感支持向量机阶段,利用KKT条件,以增量样本和初始样本训练增量代价敏感支持向量机。实验结果表明,该方法能有效地减少平均误分类代价和训练时间,提高齿轮箱故障识别率。     

基于模型的飞机虚假故障过滤方法

故障探测技术的不完善导致飞机故障处理系统收到一些虚假故障,关联着驾驶舱效应的虚假故障必然会对驾驶者的心理造成不同程度的影响.如何快速精准地过滤飞机虚假故障是目前急需解决的问题.提出一种过滤飞机虚假故障的方法,通过构建的故障关联模型和致命故障模型来设计虚假故障过滤器.该方法能有效地过滤虚假故障,对减轻驾驶员负担、提高飞行安全有着重要意义.     

关于计算机的维修防护及应急故障的诊断检修分析

在信息化时代背景下,计算机已经成为人们生产生活必不可少的物品之一,不仅能够迅速传播信息,还可有效节省工作时间、提高工作效率、减少多种人为失误,但受到操作因素、零部件自然老化或损害等因素的影响,计算机在运行过程中极易出现多种应急故障,造成文件流失、沟通不及时等问题,严重的情况下,还会给相关企业带来严重经济损失.结合计算机运行过程中易出现的故障,分析了其维修防护方式,探讨了其诊断检修措施,以期保障计算机能够高速稳定运行.     

动态决策树算法研究

该文在增量决策树算法的基础上,提出一种能够处理变化数据集的减量决策树算法,提出并证明了减量决策树算法中的三个基本定理,保证了减量决策树算法的可靠性。同时将传统的增量决策树算法与该文所提出的减量决策树算法相结合,构造出一种动态决策树算法,该算法很好地解决了发生增减变化的动态数据集构造决策树的问题,另外动态决策树算法的提出也促进了在线规则提取的发展与完善。     

FAULT DIAGNOSIS OF HYDRAULIC SERVO SYSTEM USING THE UNSCENTED KALMAN FILTER

Fault occurrence can be embodied by the physical parameter variations of the hydraulic servo system. Faults can, therefore, be diagnosed according to the model coefficient variations of the hydraulic servo system. This paper proposes an approach for fault diagnosis based on the unscented Kalman filter (UKF) with a mathematical model of the hydraulic servo system. The mathematical model is established using the dynamic equations of the hydraulic servo system. Based on the fault mechanism analysis results, several important system model parameters that can separately represent different faults in different components of the hydraulic servo system are chosen. Discrete state space equations are derived from the dynamic equations. The UKF algorithm is used to estimate the important system model parameters of the hydraulic servo system by utilizing the discretized state space model. According to the variations of these model parameters, the fault modes and locations of the hydraulic servo system can be diagnosed and isolated. Two types of faults, namely, abrupt fault in servovalve gain and slow wear fault in hydraulic cylinder piston, which cannot be directly detected from the system output, are introduced individually to the hydraulic servo system in this work. By comparing with the extended Kalman Filter, three different experimental cases are used to validate the effectiveness of the UKF for hydraulic servo system fault diagnosis.     

基于信息增益率的WNB水下机器人故障分类

提高故障诊断能力对于确保水下机器人系统的稳定运行具有重要意义,故障分类是目前水下机器人故障诊断所面临的一个重要问题。针对水下机器人推进器系统数据特征,提出一种基于信息增益率的加权朴素贝叶斯故障分类算法。首先,计算故障训练样本的先验概率,将各属性的信息增益率作为权值;其次,构建基于增益率加权的朴素贝叶斯分类模型;然后,对检测的故障数据利用分类模型获取具有最大后验概率的故障模式,实现故障分类。与朴素贝叶斯算法和决策树算法相比,仿真实验结果表明基于信息增益率加权的朴素贝叶斯算法的分类成功率更高,能够有效地实现水下机器人的故障分类。     

一种视频质量智能检测系统的研究

目前的视频监控系统对于摄像头的维护还是主要依靠人工巡查的方式检测摄像头画面质量,工作量大,工作效率低。本文提出了一种视频质量智能检测系统,对视频图像出现的雪花、滚屏、模糊、偏色、画面冻结、增益失衡、云台失控、视频信号丢失等等多种摄像头故障、视频信号干扰、视频质量下降进行准确分析、判断和报警。     

Cyberloafing at the workplace: gain or drain on work?

This study examined the impact of cyberloafing on employees' emotion and work. We also examined gender differences in employees' perception towards cyberloafing. In general, respondents felt that some form of cyberloafing at work was acceptable. Men were also more likely to report that cyberloafing has a positive impact on work compared to women. As well, our findings suggest that browsing activities have a positive impact on employees' emotion while emailing activities have a negative impact. Results of our study provide useful insights for researchers and managers in understanding employees' attitudes towards cyberloafing, and how cyberloafing can result in gain or drain in employees' work productivity.     

Subspace identification of continuous time models for process fault detection and isolation

This paper proposes a novel subspace approach towards identification of optimal residual models for process fault detection and isolation (PFDI) in a multivariate continuous-time system. We formulate the problem in terms of the state space model of the continuous-time system. The motivation for such a formulation is that the fault gain matrix, which links the process faults to the state variables of the system under consideration, is always available no matter how the faults vary with time. However, in the discrete-time state space model, the fault gain matrix is only available when the faults follow some known function of time within each sampling interval. To isolate faults, the fault gain matrix is essential. We develop subspace algorithms in the continuous-time domain to directly identify the residual models from sampled noisy data without separate identification of the system matrices. Furthermore, the proposed approach can also be extended towards the identification of the system matrices if they are needed. The newly proposed approach is applied to a simulated four-tank system, where a small leak from any tank is successfully detected and isolated. To make a comparison, we also apply the discrete time residual models to the tank system for detection and isolation of leaks. It is demonstrated that the continuous-time PFDI approach is practical and has better performance than the discrete-time PFDI approach.     

粗糙集在电力变压器故障诊断中的应用

电力变压器是一种比较复杂的系统,在实际故障诊断中要想获得完备的实验数据比较困难。针对该问题,提出了一种基于粗糙集的电力变压器故障诊断新方法,即分析搜集到的电力变压器历史故障数据,确定条件属性集和决策属性集;对条件属性集进行约简,去除冗余信息,提取关键信息,得到相应的规则集;利用该规则集对电力变压器进行故障诊断。实例分析验证了该方法的正确性和有效性。     

Current-sensor fault detection and isolation for induction-motor drives using a geometric approach

This work presents the design of a current-sensor fault detection and isolation system for induction-motor drives. A differential geometric approach is addressed to determine if faults can be detected and isolated in drives with two line current sensors by using a model based strategy. A set of subsystems is obtained based on the observability co-distribution, whose outputs are decoupled from the load torque (detectability) and only affected by one of the sensors (isolability). A bank of observers is designed for these subsystems in order to obtain residuals for the fault detection and isolation. It is demonstrated that the proposed strategy allows detecting single and multiple sensor faults, including disconnection, offset and gain faults. Experimental results validate the proposal.     

基于自适应滑模观测器的不匹配非线性系统执行器故障重构

针对一类含有未知干扰的不匹配非线性Lipschitz系统,提出了基于自适应滑模观测器的执行器故障重构方法.首先引入辅助输出矩阵,使得辅助输出系统的观测器匹配条件得以满足,同时设计了高增益观测器实现对未知辅助输出的精确估计;然后针对辅助输出系统建立故障重构滑模观测器,设计了自适应律在线修正滑模控制器增益,考虑故障上界未知的前提下,提出了观测器状态估计误差稳定的存在定理,运用Schur补引理将观测器反馈增益矩阵设计方法转化为求解线性矩阵不等式约束优化问题,同时引入线性变换矩阵,在故障上界未知的前提下设计了滑模控制增益,使得输出估计误差收敛稳定,确保了滑模运动在有限时间内发生,在此基础上利用等效控制输出误差注入原理实现了执行器故障重构;最后通过仿真算例验证了本文方法的有效性.     

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.