Fault Tolerant Synchronization for a General Complex DynamicalNetwork with Random Delay

A fault tolerant synchronization strategy is proposed to synchronize a complex network with random time delays and sensor faults. Random time delays over the network transmission are described by using Markov chains. Based on the Lyapunov stability theory and stochastic analysis, several passive fault tolerant synchronization criteria are derived,which can be described in the form of linear matrix inequalities. Finally,a numerical simulation example is carried out and the results show the validity of the proposed fault tolerant synchronization controller.

Measuring the Qatar-Kazeron Fault Dip Using Random Finite Fault Simulation of September 27, 2010 Kazeron Earthquake and Analytical Signal Map of Satellite Magnetic Data

In this research the fault parameters causing the September 27, 2010 Kazeron Earthquake with a magnitude of MW = 5.8 (BHRC) were determined using the random finite fault method. The parameters were recorded by 27 accelerometer stations. Simulation of strong ground motion is very useful for areas about which little information and data are available. Considering the distribution of earthquake records and the existing relationships, for the fault plane causing the September 27, 2010 Kazeron Earthquake the length of the fault along the strike direction and the width of the fault along the dip direction were determined to be 10 km and 7 km, respectively. Moreover, 10 elements were assumed along the length and 7 were assumed along the width of the plane. Research results indicated that the epicenter of the earthquake had a geographic coordination of 29.88N - 51.77E, which complied with the results reported by the Institute of Geophysics Tehran University (IGTU). In addition, the strike and dip measured for the fault causing the Kazeron Earthquake were 27 and 50 degrees, respectively. Therefore, the causing fault was almost parallel to and coincident with the fault. There are magnetic discontinuities on the analytical signal map with a north-south strike followed by a northwest-southeast strike. The discontinuities are consistent with the trend of Kazeron fault but are several kilometers away from it. Therefore, they show the fault depth at a distance of 12 km from the fault surface.

Random weighting error estimation for the inversion result of finite-fault rupture history

Since the non-unique solution exists in the inversion for finite-fault rupture history, the random weighting method hasbeen used to estimate error of the inversion results in this paper. The resolution distributions of slip amplitude, rake,rupture time and rise time on the finite fault were deduced quantitatively by model calculation. By using the randomweighting method, the inversion results of Taiwan Strait earthquake and Myanmar-China boundal earthquake showthat the parameters related to the rupture centers of two events have the highest resolution, and the solutinn are the mostreliable(otherwise the resolution of the slip amplitudes and rise time on the finite-fault boundary is low.

Mechanical Fault Diagnosis Based on Band-phase-randomized Surrogate Data and Multifractal

The vibration signals of machinery with various faults often show clear nonlinear characteristics.Currently,fractal dimension analysis as the common useful method for nonlinear signal analysis,is a kind of single fractal form,which only reflects the overall irregularity of signals,but cannot describe its local scaling properties.For comprehensive revealing of internal properties,a combinatorial method based on band-phase-randomized（BPR） surrogate data and multifractal is introduced.BPR surrogate data method is effective to eliminate nonlinearity in specified frequency band for a fault signal,which can be utilized to detect nonlinear degree in whole fault signal by nonlinear titration method,and the overall nonlinear distribution of fault signal is displayed in nonlinear characteristic curve that can be used to analyze the fault signal qualitatively.Then multifractal theory as a quantitative analysis method is used to describe geometrical characteristics and local scaling properties,and asymmetry coefficient of multifractal spectrum and multifractal entropy for fault signals are extracted as new criterions to diagnose machinery faults.Several typical faults include rotor misalignment,transversal crack,and static-dynamic rubbing fault are analyzed,and the results indicate that those faults can be distinguished by the proposed method effectively,which provides a qualitative and quantitative analysis way in the field of machinery fault diagnosis.

Method Based on Time Randomization to Resist Fault Sensitivity Analysis

A fault sensitivity analysis（FSA）-resistance model based on time randomization is proposed.The randomization unit is composed of two parts,namely the configurable register array（R-A）and the decoder（chiefly random number generator,RNG）.In this way,registers chosen can be either valid or invalid depending on the configuration information generated by the decoder.Thus,the fault sensitivity information can be confusing.Meanwhile,based on this model,a defensive scheme is designed to resist both fault sensitivity analysis（FSA）and differential power analysis（DPA）.This scheme is verified with our experiments.

Fault-tolerant topology in the wireless sensor networks for energy depletion and random failure

Nodes in the wireless sensor networks （WSNs） are prone to failure due to energy depletion and poor environment, which could have a negative impact on the normal operation of the network. In order to solve this problem, in this paper, we build a fault-tolerant topology which can effectively tolerate energy depletion and random failure. Firstly, a comprehensive failure model about energy depletion and random failure is established. Then an improved evolution model is presented to generate a fault-tolerant topology, and the degree distribution of the topology can be adjusted. Finally, the relation between the degree distribution and the topological fault tolerance is analyzed, and the optimal value of evolution model parameter is obtained. Then the target fault-tolerant topology which can effectively tolerate energy depletion and random failure is obtained. The performances of the new fault tolerant topology are verified by simulation experiments. The results show that the new fault tolerant topology effectively prolongs the network lifetime and has strong fault tolerance.

H_∞-based fault detection for nonlinear networked systems with random packet dropout and probabilistic interval delay

The fault detection problem for the nonlinear networked control system （NCS） with packet dropout and delay is investigated. A nonlinear stochastic system model is proposed to account for the NCS with random packet dropout and network- induced non-uniformly distributed time-varying delay in both from sensor to controller （S/C） and from controller to actuator （C/A）. Based on the obtained NCS model, employing an observer-based fault detection filter as the residual generator, the addressed fault detection problem is converted into an auxiliary nonlinear H∞ control problem. Then, with the help of Lyapunov functional approach, a sufficient condition for the desired fault detection filter is constructed in terms of certain linear matrix inequalities, which depend on not only the delay interval but also the delay interval occurrence rate and successful packet communication rate. Especially, a trade-off phenomenon between the maximum allowable delay bound and successful data packet transmission rate is found, which is typically resulted from the limited bandwidth of communication networks. The effectiveness of the proposed method is demonstrated by a simulation example.

A NEW APPROACH OF SIMULTANEOUS FAULTS DIAGNOSIS BASED ON RANDOM SETS AND DSMT

Simultaneous faults often occur in running equipments, in order to solve the problems of the simultaneous faults, a new approach based on random sets and Dezert-Smarandache Theory (DSmT) is proposed in this paper. Firstly, the simultaneous faults' model is built based on the generalized frame of discernment in DSmT. Secondly, according to the unified description of combination rules in evidence reasoning based on random sets, a new combination rule for simultaneous faults diagnosis is proposed. Thirdly, according to the working characteristics and environment of the sensors used to acquire fault characteristic information, a new method to construct basic probability assignment function is pro- posed based on membership. Finally, diagnosis result is obtained by use of the new combination rule combined with decision rules. A case pertaining to the fault diagnosis for a multi-function rotor test-bed is given, and the result shows that the proposed diagnosis approach is feasible and efficient.

基于IHHT‑RF的配电网单相接地故障选线方法

小电流系统发生单相接地故障时故障特征易受高接地过渡电阻、小初相角等弱故障条件影响而导致选线准确率低。为此,提出一种基于改进希尔伯特黄变换—随机森林(improved Hilbert⁃Huang transform⁃random forest,IHHT⁃RF)的配电网单相接地故障选线方法。首先,提取每条线路在故障发生时的电流暂态信号,通过IHHT提取纯净的暂态电气量,构造标准差、能量熵和幅值畸变度3类特征向量;然后,将特征向量输入RF分类器建立故障选线模型,把故障选线问题转化为二分类问题;最后,将测量数据输入RF分类器中得出分类结果,实现故障线路的自动识别。仿真结果表明,该选线方法综合利用暂态信号的幅值、频率和能量等特征信息,不受弱故障条件、馈线结构等因素的影响,能有效提高故障选线的准确率,具有较强的适应性和可靠性。

基于随机森林的硬盘故障率预测研究

为了避免硬盘出现故障而造成大量数据丢失,文章提出一种基于随机森林的方法对硬盘的故障进行预测,降低其丢失数据的风险。首先,在数据预处理方面,对所采用的数据做特征映射预处理;其次,通过对决策树进行构建及选取等,构建随机森林预测模型,根据所选取的特征属性预测硬盘故障率所在的区间,并且特征属性的变化能反映出硬盘故障率的变化趋势;最后,对构建的随机森林模型参数进行调优,选取不同的n_estimators参数值进行测试和优化。实验结果表明,与XGBoost(Extreme Gradient Boosting)、LSTM(Long Short-Term Memory)等方法相比,本文方法的F1值(F-Measure)分别提高了0.93%和1.84%,并且对随机森林预测模型的参数值进行不同取值测试,最终准确率达到98.18%,比默认值提高了1.23%,证明该方法能更精确地预测硬盘故障率,反映出硬盘故障率基于特征属性的变化趋势。

基于广域信息分析的智能配电网故障自愈技术研究

针对传统电网接地故障检测和自愈方法存在识别速度慢且精确度较低的问题,文中结合信号广域信息提出了一种配电网故障自愈方法。该方法构建了单相接地故障模型,并将零序电流作为分析的对象。将零序电流信号分解为时域、频域以及小波域分量来作为广域特征向量,并采用随机森林算法对其权重进行分类,利用LightGBM算法对分类后的广域特征向量加以训练,进而得到故障预测结果。在仿真测试中,所提算法能够抵抗过渡电阻与初始相位角变化对预测结果的影响,且其故障分析准确率的平均值为98.9%,在对比算法中为最优,表明该算法可为配电网故障自愈提供有效的技术支撑。

近断层地震动作用下土石坝加速度分布研究

近断层地震动较远场地震动具有频率高、速度快、能量更加集中的特点,在短时间内会释放大量能量,对工程结构具有严重危害。规范提供的加速度分布系数不适用于高度超过150 m的大坝,随着技术不断进步,我国超过150 m的土石坝越来越多,规范已不能满足现有需求。基于中国台湾集集近断层地震动记录,建立随机脉冲型地震动模型,分别对高度为40 m、70 m、150 m和200 m的土石坝进行随机动力分析,研究坝体的动态分布系数。结果表明,近断层地震动的低频速度脉冲使坝体的加速度反应谱有较宽的敏感段,易覆盖坝体自振周期,显著增加坝体的加速度。该研究通过总结近断层区域土石坝的加速度分布规律,建议了近断层脉冲型地震动区域的土石坝坝体动态分布系数,为近断层高地震风险情况下土石坝的抗震理论和方法提供参考。

基于OVMD-RF方法的风力发电机滚动轴承故障诊断

风电机组运行时轴承受到交变应力和冲击载荷,振动信号非线性、不平稳且具有噪声,特征提取不充分.针对风力发电机轴承故障信号处理和特征提取的固有缺陷,提出了基于优化变分模态分解与随机森林算法结合的故障诊断方法.首先,利用乌燕鸥优化算法对变分模态分解的参数进行搜索寻优;然后,利用优化参数的变分模态分解对滚动轴承振动信号进行分解,获得模态分量;最后,以峰值、峭度和包络熵构建融合特征训练集,并输入至随机森林分类器进行模型训练,实现故障识别.实例分析的结果表明,该方法识别风力发电机轴承故障的准确率高达100%,可实现故障的准确判别.

基于RF与D-S证据理论融合的多通道齿轮箱复合故障诊断

针对齿轮箱复合故障特征难于提取,诊断缺乏自动识别性,且单通道往往无法全面表征故障信息等问题,提出一种基于随机森林与证据理论相融合的多通道齿轮箱复合故障诊断方法。首先通过小波包变换对各通道复合故障信号进行分解,得到故障信号的特征向量;之后引入一种新的特征集组合框架构建针对不同故障的特征数据集,通过随机森林算法划分单个分类模型;接着综合考虑各分类模型,合成每个通道下的集成分类器,并提出一种新的迭代自更新策略不断完善分类器的性能;最后设计一种基于Lance距离的改进D-S证据理论算法,该算法采用Lance距离来度量各空间证据间的证据距离,并构造Lance矩阵,由此获得相似度矩阵来衡量各证据体间的相似程度和支持度,通过计算各通道的敏感度权重系数进行BPA修正,获得最终的诊断融合结果。通过齿轮箱试验平台进行算法验证,结果表明该方法能有效识别出复合故障中包含的每类故障,并能全面融合不同通道的故障冗余信息,实现齿轮箱复合故障的精确诊断。

考虑系统故障随机性的电热联合系统备用与DNE分布鲁棒协同优化调度

为实现电热联合系统的安全稳定运行,提高可再生能源消纳,提出考虑系统设备故障随机性的电热联合系统备用与“不超过”(DNE)分布鲁棒协同优化调度模型。首先,以常规机组和热电联产机组运行成本最小为综合优化目标,电功率平衡约束、热功率平衡约束等为约束条件,建立确定性电热联合系统优化调度模型;其次,在综合考虑风电功率、设备故障随机性以及DNE极限基础上建立电热联合系统分布鲁棒优化调度模型;最后,以修改的9节点系统为例,验证了所提模型可有效提高风电消纳率和系统的经济性。

可兼容四种March系列算法的PMBIST电路设计

存储器是系统级芯片(System on chip,SoC)中最重要的组成部分之一,也是最容易出现故障的部件。存储器故障可能会导致整个SoC失效,对存储器进行充分的测试和验证是至关重要的。目前,主流的存储器测试方法是采用存储器内建自测试(Memory build-in-self test,MBIST)技术,传统的可测性技术采用单一的测试算法进行测试,为了满足不同类型存储器的测试需求以及不同工艺制造阶段的测试强度,需要使用不同类型的测试算法进行测试。结合存储器常见的故障模型以及多种测试算法,设计了具有较高灵活性和可扩展性的可编程存储器内建自测试(Programmable memory built-in-self test,PMBIST)电路,可兼容四种不同的March系列算法进行存储器内建自测试,采用寄存器传输语言(Reigster transfer language,RTL)级代码的编写方式,针对静态随机存储器(Static random-access memory,SRAM)采用不同March系列测试算法进行仿真,并以常用的March C+算法为例进行说明。仿真结果表明,所设计的PMBIST电路可对四种不同的March算法进行测试,满足不同类型存储器的内建自测试需求。

柱塞泵滑靴磨损信号随机森林算法故障诊断

为了提高低负载下柱塞泵滑靴磨损故障状态诊断精度,提出基于随机森林算法的柱塞泵滑靴磨损故障状态识别方法。重点分析了低负载条件下各类柱塞泵滑靴故障信号的频域特征值,构建了特征数据库。验证了上述方法的适应性,并测试了柱塞泵不同程度松靴故障的诊断情况。研究结果表明:滑靴磨损后频域表现出明显的波动性,袋外错误率和决策树数量呈现反比变化规律,基本都在0.05附近,将决策树最优棵数n设定在400。以随机森林算法诊断特征数据库时,在250组样本中只发生了1组误识别情况,达到了98.75%的识别准确度。随机森林方法训练时间、训练准确度与测试准确度都比其它各算法更优。松靴故障诊断结果获得了高于99.5%的总体诊断准确度。采用随机森林方法柱塞泵磨损故障状态诊断表现出优异适应性,能够对柱塞泵各故障状态进行准确诊断。

基于RMT-CNN的电网短路故障定位研究

随着我国智能电网的快速发展,电网监测数据呈现多元化、高速化、海量化的趋势.为了充分挖掘电力大数据的潜在价值,实现电网内异常区域的自动识别与定位,本文研究了基于随机矩阵理论(random matrix theory,RMT)和卷积神经网络(convolutional neural networks,CNN)的电网异常事件定位方法.首先根据电网内部联系将电网划分为若干子系统,分区构建监测矩阵;然后采用RMT作为数据挖掘的特征提取方法,提取分区矩阵特征向量作为输入,根据电网监测数据和异常识别需求的特点搭建CNN模型;最后基于分区矩阵特征向量构建数据集,训练获得有效的异常事件自动定位CNN模型.以IEEE39节点电网模型三相短路故障为例,分析表明通过RMT提取特征向量的预处理方法能有效降低数据维度,提高CNN模型的故障定位准确率,分区RMT-CNN模型能有效定位电网内异常事件的发生地点,定位精度可达97.96%,精确率可达98.65%.

针对隐性故障的新型电力系统连锁故障模型

鉴于目前对电力系统连锁故障的研究大多是基于传统电力系统,且忽略了导致连锁故障重要因素之一的隐性故障。基于此,针对电网隐性故障,利用随机潮流(stochastic load flow,SLF)和元胞自动机算法(cellular automata,CA)结合构建一种针对隐性故障的新型电力系统连锁故障模型。首先,利用SLF引入风电和光伏机组,构造新型电力系统,同时计算出系统内各个线路的有功初始潮流。然后,使用CA算法简化系统的拓扑结构,结合潮流越限隐性故障概率模型判断元胞状态,以元胞及其邻居的状态进行故障传递从而模拟连锁故障。最后,以改进的IEEE39节点系统为算例验证了模型的有效性。该模型能为新型电力系统连锁故障模型以及隐性故障的研究提供新的参考。

执行器随机失效系统鲁棒预测容错切换控制

针对一类具有执行器随机失效问题的离散线性系统,提出一种基于故障概率情况下的鲁棒预测容错切换控制方法。首先,将工业过程建立成新型多自由度状态空间模型,设计含有故障概率的容错控制器;其次,引入系统故障和其恢复时的随机概率,利用李雅普诺夫判据给出基于线性矩阵不等式形式的稳定性条件,再通过指数稳定的相关证明求解出不同执行器切换时的稳定条件,以保证系统故障时容错控制与无故障时常规控制间的切换;然后,控制器设计时还充分考虑了设定值变化时所产生的跟踪误差带来的影响。最后,通过仿真结果验证了所提方法的可行性。