Uncertainty of ESD Pulse Metrics Due to Dynamic Properties of Oscilloscope

This paper investigates how dynamics of an HF oscilloscope affects standardized metrics (i.e., rise time and peak value) of a recorded electrostatic discharge (ESD) current pulse and evaluates the oscilloscope measurement uncertainty. A frequency-domain dynamic model of the oscilloscope, based on measurements of the input reflection and voltage gain magnitude, is derived. The complex voltage gain is approximated with a transfer function to take into account unmeasured phase shift. A high-order discrete-time filter is designed to fit the measured voltage gain magnitude not only in the passband, but also beyond it as well. Since the measured data are burdened with measurement uncertainties, the worst-case deviations of the oscilloscope input impedance and the voltage gain (in the form of envelopes of the frequency responses) are calculated, and components of Type B uncertainty of the ESD pulse metrics corresponding to these deviations are estimated using the sensitivity method.      

Sensor system optimization to meet reliability targets

In this work, we show the influence of sensor system measurement uncertainties to sensor system reliability and ways to meet reliability targets. A general model to handle measurement uncertainties is defined and the according influence to reliability is presented, which is defined as probability of meeting specification requirements. Initial step is to optimize sensor systems concerning lowest influences of sensor system parameter fluctuations to the measurement uncertainty using statistical optimization methodologies. In case the influence of unknown nuisance parameters cannot be sufficiently suppressed, such parameters may be additionally measured in order to further reduce measurement uncertainties. The remaining uncertainties are again addressed using statistical optimization methodologies. Finally, measurement uncertainty also affects the reliability of such a system. For sensor systems in safety critical applications it may thus be required to include measures such as redundancy. This is also included in the investigations. Further examples for explained optimization methodologies of measurement uncertainty reduction are presented.     

一种多核系统可靠性加强的任务调度方法

 多核系统已经被广泛应用于各行各业,其稳定性和可靠性也越来越受到人们的关注.在分析了现有芯片可靠性模型的基础上,增加温度和工作负载对多核芯片可靠性的影响,建立了对多核系统的可靠性评估模型.该模型以处理器为依托,从指令到任务,逐层构建可靠性评估指标,以便于定量分析影响多核系统可靠性的因素.同时,以该模型为指导,设计了一种面向多核系统可靠性的任务调度方法,该方法通过对评估指标值的计算,选择评估指标值尽可能高的调度策略对多核系统中的任务进行调度,以减少由于芯片本身可靠性而导致的错误.通过模拟实验可以看出,该任务调度算法能有效减少系统的出错率45%左右,为系统的稳定运行提供了可靠的保证.     

On improved confidence bounds for system reliability

In this paper, new bounding strategies are presented to improve confidence interval estimation for system reliability based on component level reliability, and associated uncertainty data. Research efforts have been focused on two interdependent areas: 1) development & improvement of analytical approaches for quantifying the uncertainty associated with the system reliability estimate when data regarding component reliability is available; and 2) based on these analytical approaches, generating statistical inference methods that can be used to make accurate estimations about the reliability of a system. The analytical approach presented relies on a recursive rationale that can be applied to obtain the variance associated with the system reliability estimate, provided the system can be decomposed into a series-parallel configuration. The bounding procedure is independent of parametric assumptions regarding component time to failure, and can be applied whenever component reliability data are available. To assess the validity of the proposed procedure, three test cases have been analyzed. For each case, Monte-Carlo simulation has been used to generate component failure data, based on nominal component reliability values. Based on these simulated data, lower bounds have been constructed, and then compared against nominal system reliability to generate an expected confidence level. The results obtained exhibit a significant improvement in the accuracy of the confidence intervals for the system reliability when compared with existing approximation methods. The procedure described is effective, relatively simple, and widely applicable.     

Value‐centric framework and pareto optimality for design and acquisition of communication satellites

Investments in space systems are substantial, indivisible, and irreversible, characteristics of high‐risk investments. Traditional approaches to system design, acquisition, and risk mitigation are derived from a cost‐centric mindset, and as such they incorporate little information about the value of the spacecraft to its stakeholders. These traditional approaches are appropriate in stable environments. However, the current technical and economic conditions are distinctly uncertain and rapidly changing. Consequently, these traditional approaches have to be revisited and adapted to the current context. We propose that in uncertain environments, decision‐making with respect to design and acquisition choices should be value‐based. We develop a value‐centric framework, analytical tools, and an illustrative numerical example for communication satellites. Our two proposed metrics for decision‐making are the system's expected value and value uncertainty. Expected value is calculated as the expected NPV of the satellite. The cash inflow is calculated as a function of the satellite loading, its transponder pricing, and market demand. The cash outflows are the various costs for owning and operating the satellite. Value uncertainty emerges due to uncertainties in the various cash flow streams, in particular because of market conditions. We propagate market uncertainty through Monte Carlo simulation, and translate it into value uncertainty for the satellite. The end result is a portfolio of Pareto‐optimal satellite design alternatives. By using value and value uncertainty as decision metrics in the down‐selection process, decision‐makers draw on more information about the system in its environment, and in making value‐based design and acquisition choices, they ultimately make more informed and better choices. Copyright © 2009 John Wiley & Sons, Ltd.     

基于运行统计的网络管理系统连接质量模糊评价

为了评价网络管理系统的运行质量,提出了网络管理系统连接质量的概念,给出了建立连接质量评价指标体系的参考建议,设计了一组反映连接质量的评价指标体系并以连接质量作为考核网络管理系统运行质量的重要指标.应用模糊数学中的模糊评价算法建立了评价基于运行统计的网络管理系统连接质量的模糊评价模型,以不同本地话务汇接局网管数据为实例,构建了相应的连接质量实例并应用模糊评价方法对其进行了模糊评价,从而获得网络管理系统运行质量的量化评价结果.     

Efficient computation of the sensitivity of k-out-of-n system reliability

The first-order sensitivity of system reliability is useful in evaluating several criticality measures, uncertainty measures, and the instantaneous failure rate of the system. Three new algorithms are described herein for the computation of the sensitivity of k-out-of-n system reliability. Generally, the numerical results of these algorithms check very well versus one another as well as versus those of known special cases. The computational complexities of these algorithms vary from almost double to slightly less than that of the best known algorithm for computing the k-out-of-n system reliability. Some observations are made on the important rankings of system components for different values of k, n and component reliabilities.     

Interval methods for fault-tree analysis in robotics

This paper describes a novel technique, based on interval methods, for estimating reliability using fault trees. The approach encodes inherent uncertainty in the input data by modeling these data in terms of intervals. Appropriate interval arithmetic is then used to propagate the data through standard fault trees to generate output distributions which reflect the uncertainty in the input data. Through a canonical example of reliability estimation for a robot manipulator system, we show how the use of this novel interval method appreciably improves the accuracy of reliability estimates over existing approaches to the problem of uncertain input data. This method avoids the key problem of loss of uncertainty inherent in some approaches when applied to noncoherent systems. It is further shown that the method has advantages over approaches based on partial simulation of the input-data space because it can provide guaranteed bounds for the estimates in reasonable times     

System-reliability confidence-intervals for complex-systems withestimated component-reliability

A flexible procedure is described and demonstrated to determine approximate confidence intervals for system reliability when there is uncertainty regarding component reliability information. The approach is robust, and applies to many system-design configurations and component time-to-failure distributions, resulting in few restrictions for the use of these confidence intervals. The methods do not require any parametric assumptions for component reliability or time-to-failure, and allows type-I or -II censored data records. The confidence intervals are based on the variance of the component and system reliability estimates and a lognormal distribution assumption for the system reliability estimate. This approach applies to any system design which can be decomposed into series and/or parallel connections between the components. To evaluate the validity of the confidence limits, numerous simulations were performed for two hypothetical systems with different data sample-sizes and confidence levels. The test cases and empirical results demonstrate that this new method for estimating confidence intervals provides good coverage, can be readily applied, requires only minimal computational effort, and applies for a much greater range of design configurations and data types compared to other methods. For many design problems, these confidence intervals are preferable because there is no requirement for an exponential time-to-failure distribution nor are component data limited to binomial data     

一种改进的可靠性分配方法及其应用验证

可靠性分配是系统设计中的重要工作,常用的可靠性分配方法如AGREE分配法、评分分配法对影响可靠性指标的因素考虑不全,并且都是针对串联系统的。利用贝叶斯网络在不确定性概率推理方面的优势,将传统的故障树转化为贝叶斯网络求解最小割集和底事件重要度。建立了改进的可靠性分配模型,首先将顶层可靠性指标在最小割集之间分配,然后根据底事件重要度和评分将最小割集对应的可靠性指标分配到底事件。以某型飞机电传操纵系统可靠性指标分配为例,仿真结果表明,贝叶斯网络相对于故障树分析计算底事件重要度更为简单、精度更高。改进的分配方法能够考虑影响可靠性指标的主要因素,实现指标的科学分配。     

Error analysis leading to design criteria for transmission linemodel characterization of ohmic contacts

The transmission line model (TLM) is a standard method for planar specific contact resistance measurement. Although widely used, the accuracy of a measurement is typically not stated. In addition to contributions from random errors, there can be substantial contributions from systematic errors in typical TLM measurements. In this paper, we develop an analytical model for the experimental uncertainty from the fundamental TLM expressions in order to understand and calculate the uncertainty associated with the specific contact resistance and sheet resistance derived by the TLM method. The experimental uncertainties in measured resistances, together with the pad width and pad spacing, are the dominant contributions to the total uncertainty. Analytical expressions for relative random and systematic uncertainties in contact resistance and sheet resistance are developed in terms of the error contributions and the parameters of the TLM geometry. Expressions for minimum uncertainty, with associated optimum widths and sheet resistances, serve as a basis for the design of TLM structures with minimum uncertainty. The model quantifies the increase in relative uncertainty associated with decreasing contact resistance. Simulations of uncertainty under various sheet resistance, contact resistance, and pad width are implemented and uncertainties are calculated for realistic data sets     

Evaluating Reliance Level of a Virtual Metrology System

This paper proposes a novel method for evaluating the reliability of a virtual metrology system (VMS). The proposed method calculates a reliance index (RI) value between zero and one by analyzing the process data of production equipment to determine the reliability of the virtual metrology results. This method also defines an RI threshold. If an RI value exceeds the threshold, the conjecture result is reliable; otherwise, the conjecture result needs to be further examined. Besides the RI, the method also proposes process data similarity indexes (SIs). The SIs are defined to assess the degree of similarity between the input set of process data and those historical sets of process data used to establish the conjecture model. The proposed method includes two types of SIs: global similarity index (GSI) and individual similarity index (ISI). Both GSI and ISI are applied to assist the RI in gauging the reliance level and locating the key parameter(s) that cause major deviation, thus resolving the VMS manufacturability problem. An illustrative example involving 300-mm semiconductor foundry etching equipment is presented. Experimental results demonstrate that the proposed method is applicable to the VMS of production equipment (such as that for semiconductor and TFT-LCD).     

GSM手机总全向灵敏度（TIS）的测量不确定度评定

文章介绍了微波暗室中GSM手机总全向灵敏度（TIS）测试系统的测量不确定度评定方法,对不确定度的主要来源如测试设备精确度,系统连接失配,测试环境以及被测手机状态等作了定量评定,得出在95％置信区间下测试系统的扩展不确定度为1．54dB。研究结论可以评估TIS测试系统的可靠性,对如何降低测量不确定度提供指导,从而对手机性能进行更准确的判定。     

A new hierarchical belief-rule-based method for reliability evaluation of wireless sensor network

With the wide applications of wireless sensor network (WSN), its reliability evaluation has been attracted more attention. The reliability of a WSN is affected mainly by internal and external factors, which include internal faults and external attacks. In this paper, a reliability evaluation method based on a hierarchical belief rule base (BRB) method is proposed for the reliability evaluation of the WSN. First, the factors affecting the reliability of a WSN are analysed, and the reliability evaluation process that considers the WSN fault evaluation and WSN security evaluation is described. Second, the reliability evaluation model is constructed based on the hierarchical BRB model. The qualitative knowledge is used by the BRB model to build initial belief rules, and the quantitative data are used to optimize the initial parameters of the BRB model, which can utilize various types of uncertainty information effectively. Therefore, the proposed method can be applied to the WSN reliability evaluation, which is a complex and uncertain problem. Finally, a simulation case study and an actual case study of wellhead blowout monitoring are conducted to verify the effectiveness of the proposed method. The reliability results of actual WSN are obtained by the standard testing method, where the loss and accuracy rates of the collected data are treated as the observation factors for obtaining the actual reliability values. The estimated results of hierarchical BRB model are very close to the actual reliability values, which shows that the proposed method can be used for evaluating the reliability of the actual WSN accurately.     

On Bayesian estimation of system reliability

For a system with n s-independent components, the uncertainty regarding the reliability of components for a fixed point of time is expressed by a Bayesian probability distribution. Using the moments of these distributions, the exact moments of the system reliability distribution are derived, from which a discrete probability density function is obtained on the basis of the principle of maximum entropy. Taking this distribution as a prior distribution for system reliability, a posterior density function for the system reliability is constructed either using the data obtained from life tests conducted at a system level or from field data. For tracking the evolution of the reliability distribution over time, a modified Kalman filter technique, along with use of a Bayesian procedure, is proposed. This method is simple, elegant and easy to compute.     

Resolution depths for some transmitter-receiver configurations

Equivalent dipole polarizability matrices and equivalent dipole location are a convenient way to interpret magnetic field data due to currents induced in isolated conductive objects. The uncertainties in polarizability estimates and in the equivalent dipole location provide a quantitative measure of the performance of different configurations of transmitters and receivers. In another paper, we estimate these uncertainties using a linearized inversion. For many systems, consisting of one or more rectangular loop transmitters and a number of dipole receivers, sited on a horizontal grid, equivalent dipole depth is determined to 10% accuracy to depths approximately 20% deeper than the depths at which polarizability matrix elements can be determined to the same precision. Systems that have a lower product of rms polarizability uncertainty and square root of their number of transmitter-receiver pairs are considered more effective for the number of transmitter-receiver pairs. Among the systems studied, a system with three orthogonal transmitter loops and a three-component receiver is the most effective, for objects shallower than 0.6 times the instrument siting grid spacing, yielding an rms polarizability uncertainty 0.04 times that of a single-transmitter single-receiver system. At intermediate depths, a system with two vertical component receivers on the diagonal of a square horizontal transmitter loop is most effective for its number of transmitter-receiver pairs, yielding an rms polarizability uncertainty 0.07 times that of a single receiver system. At depths greater than 2.5 times, the siting grid spacing a three-orthogonal loop transmitter with a single vertical component receiver is about the most effective for its number of transmitter-receiver pairs, yielding an rms polarizability uncertainty 0.08 times that of a single-transmitter system.     

Sintered silver finite element modelling and reliability based design optimisation in power electronic module

This paper discusses the design for reliability of a sintered silver structure in a power electronic module based on the computational approach that composed of high fidelity analysis, reduced order modelling, numerical risk analysis, and optimisation. The methodology was demonstrated on sintered silver interconnect sandwiched between silicon carbide chip and copper substrate in a power electronic module. In particular, sintered silver reliability due to thermal fatigue material degradation is one of the main concerns. Thermo-mechanical behaviour of the power module sintered silver joint structure is simulated by finite element analysis for cyclic temperature loading profile in order to capture the strain distribution. The discussion was on methods for approximate reduced order modelling based on interpolation techniques using Kriging and radial basis functions. The reduced order modelling approach uses prediction data for the thermo-mechanical behaviour. The fatigue lifetime of the sintered silver interconnect and the warpage of the interconnect layer was particular interest in this study. The reduced order models were used for the analysis of the effect of design uncertainties on the reliability of the sintered silver layer. To assess the effect of uncertain design data, a method for estimating the variation of reliability related metrics namely Latin Hypercube sampling was utilised. The product capability indices are evaluated from the distributions fitted to the histogram resulting from Latin Hypercube sampling technique. A reliability based design optimisation was demonstrated using Particle Swarm Optimisation algorithm for constraint optimisation task consists of optimising two different characteristic performance metrics such as the thermo-mechanical plastic strain accumulation per cycle on the sintered layer and the thermally induced warpage.     

高功率微波辐射场功率密度测量不确定度分析方法

辐射场测量是检验高功率微波系统输出指标的重要手段.随着高功率微波测量技术的发展,辐射场测量系统的稳定性和可靠性不断提高,作为完整测量结果重要组成部分的测量不确定度越来越受到关注.文章介绍了高功率微波辐射场功率密度测量方法及系统组成,建立了功率密度测量的数学模型,给出了高功率微波辐射场功率密度测量的主要不确定度来源.对检波器输入功率计算、接收天线有效面积校准、衰减环节校准及测量系统各环节连接失配等测量不确定度分量进行了分析,并给出了高功率微波辐射场功率密度测量不确定度的合成方法.本文给出的测量不确定度分析方法较为科学、操作性强,对完善高功率微波辐射场功率密度测量结果具有一定的指导意义.     

Implementation of sliding mode control using the concept of perturbation

This work presents further improvements on a class of non-linear robust motion controllers, namely Sliding Mode Control (SMC) and Sliding Mode Control with Perturbation Estimation (SMCPE). Typically, the SMC methodology requires a priori knowledge of the uncertainty upper bounds for the system dynamics. It may be a difficult task to accomplish, and sometimes even impossible, due to the complexity in identifying the individual dynamic uncertainties. This work utilizes a perturbation analysis to remedy this hardship. SMCPE, on the other hand, requires the upper bounds of perturbation estimation errors, instead of the uncertainties themselves. We present an enhancement on this process also, by evaluating these bounds based on the given hardware limitations at hand. The contributions of this work appear at two points: (a) new guidelines to obtain uncertainty knowledge, (b) new feedback gain selection strategy to assure robustness without being overly conservative. All critical steps, such as robustness and stability, are rigorously proven. Practicality is the major objective in this new formulation. The claims are verified through experiments on a three-axes industrial manipulator. It is shown that the controller does not have to know more than the hardware specs of the sensory and actuation devices in order to assure robustness of the two control routines. This makes control design process much easier to complete.     

Optimal design of reliable network systems in presence of uncertainty

In practice, network designs can be based on multiple choices of redundant configurations, and different available components which can be used to form links. More specifically, the reliability of a network system can be improved through redundancy allocation, or for a fixed network topology, by selection of highly reliable links between node pairs, yet with limited overall budgets, and other constraints as well. The choice of a preferred network system design requires the estimation of its reliability. However, the uncertainty associated with such estimates must also be considered in the decision process. Indeed, network system reliability is generally estimated from estimates of the reliability of lower-level components (nodes & links) affected by uncertainties. The propagation of the estimation uncertainty from the components degrades the accuracy of the system reliability estimation. This paper formulates a multiple-objective optimization approach aimed at maximizing the network reliability estimate, and minimizing its associated variance when component types, with uncertain reliability, and redundancy levels are the decision variables. In the proposed approach, Genetic Algorithms (GA) and Monte Carlo (MC) simulation are effectively combined to identify optimal network designs with respect to the stated objectives. A set of Pareto optimal solutions are obtained so that the decision-makers have the flexibility to choose the compromised solution which best satisfies their risk profiles. Sample networks are solved in the paper using the proposed approach. The results indicate that significantly different designs are obtained when the formulation incorporates estimation uncertainty into the optimal design problem objectives.