Reliability indices for electric-power wheeling

Transmission networks are being used more extensively to accommodate energy transactions that use the economic benefits of pooling. This paper evaluates the probabilistic wheeling capability of an interconnected power transmission system. Large electric power wheeling can degrade the reliability of the electric power network. This problem is also important for power system control and planning purposes. This paper discusses, from the viewpoint of power network reliability, the problem of power wheeling from Western Europe to Greece through the Yugoslav electric power system. The relative cost of the wheeling due to reliability indices is 2% of the production cost for the power wheeled. The calculation of reliability indices of wheeling enable more adequate estimation of the wheeling rate for power transactions between utilities. This paper describes an approach to calculate cost of the average energy not supplied and load curtailed due to wheeling. This cost can even be negative, which depends mostly on the amount of wheeling power and consumer loads. The reliability index EENS (expected energy not supplied) is more comprehensive than PI (performance index) for the contingency severity selection procedure     

Optimal Reliability of a Complex System

In a complex system where the redundant units cannot be reduced to a purely parallel or series configuration, the reliability is obtained by using Bayes' theorem. A mathematical model is formulated for the reliability of a system with nonlinear constraints. The system reliability is optimized based on the model and the solution is obtained by using the sequential unconstrained minimization technique (SUMT). This method is an efficient method for solving this type of problem. Two life support systems, one is the communication system of a two-man space capsule and another is a high-pressure oxygen supply system in a space capsule, have been identified to have the complex system configuration treated in this work.     

Interconnect Lifetime Prediction for Reliability-Aware Systems

Thermal effects are becoming a limiting factor in high-performance circuit design due to the strong temperature dependence of leakage power, circuit performance, IC package cost, and reliability. While many interconnect reliability models assume a constant temperature, this paper analyzes the effects of temporal and spatial thermal gradients on interconnect lifetime in terms of electromigration, and presents a physics-based dynamic reliability model which returns reliability equivalent temperature and current density that can be used in traditional reliability analysis tools. The model is verified with numerical simulations and reveals that blindly using the maximum temperature leads to too pessimistic lifetime estimation. Therefore, the proposed model not only increases the accuracy of reliability estimates, but also enables designers to reclaim design margin in reliability-aware design. In addition, the model is useful for improving the performance of temperature-aware runtime management by modeling system lifetime as a resource to be consumed at a stress-dependent rate     

逐次截尾样本下电子元件混联系统可靠性指标的EB估计

在逐次截尾样本下,研究电子元件混联系统可靠性指标的估计问题。将Bayes方法和极大似然法相结合,在平方损失下,获得部件失效率、系统可靠度和平均寿命的经验Bayes估计。最后给出随机模拟例子,说明该方法的正确性。结果表明可靠性指标的经验Bayes估计值精度较高。     

基于非线性最小二乘的统计MIMO雷达动目标参数估计算法

根据统计MIMO雷达的工作原理和多脉冲信号模型,提出了一种含加速度的动目标模型,推导证明了位置、速度和加速度联合估计下的CRB,分析了收发天线数、天线配置对参数估计性能的影响,并利用非线性最小二乘模型简化了目标函数的形式,在降低参数估计成本的同时,利用脉冲积累特性又可提高估计精度。文中表明随着天线数、发射脉冲数的增加,系统参数估计性能会大幅提高,且利用最小二乘模型的目标函数,参数估计的效率更高。仿真实验有效地验证了理论分析的正确性。     

Software reliability estimations for telecommunications products

Reliability of software used in telecommunications networks is a crucial determinant of network performance. Software reliability (SR) estimation is an important element of a network product's reliability management. In particular, SR estimation can guide the product's system testing process and releasability decisions. SR estimation is performed using an appropriate SR estimation model. However, the art of SR estimation is still evolving. There are many available SR estimation models to select from, with different models being appropriate for different applications. Although there is no “ultimate” and “universal” SR model on the horizon (and there may not be one in the foreseeable future), methods have been developed for selecting a trustworthy SR model for each application. The present authors have been analyzing and adapting these methods for applicability to network software. Their results indicate that there already exist methods for SR model selection which are practical to use for telecommunications software. If utilized, these methods can promote significant improvements in SR management. The paper presents the results to date     

Modeling a Mobile Cellular Network Using a Discrete-Event Simulator

In this paper we introduce a discrete-event-based simulation technology suitable to model cellular mobile radio systems with respect to their dynamic behavior. The latter results in the need for simulation tools which support mutable system configurations. The problems encountered when modeling mutable system configurations using conventional discrete-event simulators are discussed. In order to show a possible solution to this problem we introduce configuration events and configuration objects into the theory of discrete-event simulations. We demonstrate how configuration objects can be implemented in a discrete-event simulator using a dynamic map-function, hereby extending a definition of higher order functions. We apply the configuration objects in a Multiple Layer Model for modeling an entire mobile cellular radio network in a discrete-event simulator as an application for the extended theory. In this model we use the configuration objects to change the network's configuration during runtime. We show that this solution—combined with an object-oriented software design and, possibly, a visual programming language—is a powerful tool for the simulation of the dynamic aspects of mobile cellular radio networks. The software technology presented will be applied by the German cellular network operator Mannesmann Mobilfunk to model dynamic features applicable to the radio resource management of mobile radio networks and to access their performance by simulation.     

Reliability analysis of real-time fault-tolerant task models

One notable advantage of Model-Driven Architecture (MDA) method is that software developers could do sufficient analysis and tests on software models in the design phase, which helps construct high confidence on the expected software behaviors and performance, especially for safety-critical real-time software. Most existing literature of reliability analysis ignores the effects from those deadline requirements of tasks which are critical properties for real-time software and thus cannot be ignored. Considering the contradictory relationship between the deadline requirements and time costs of fault tolerance in real-time tasks, in this paper, we present a novel reliability model, which takes schedulability as one of the major factors affecting the reliability, to analyze reliability of the task execution model in real-time software design phase. The tasks in this reliability model has no restrictions on their distributions and thus could be distributed on a multiprocessor or on a distributed system. Furthermore, the tasks also define arrival rates of faults and fault-tolerant mechanisms to model the occurrences of non-permanent faults and the corresponding time costs of fault handling. By analyzing the probability of tasks still being schedulable in the worst-case execution scenario with faults occurring, reliability and schedulability are combined into an unified analysis framework, and two algorithms for reliability analysis are given. To make this reliability model more pragmatic, we also present an estimation technique for estimating the fault arrival rate of each task. We show through two case studies respectively the detailed derivation process under static-priority scheduling in a multiprocessor system and in the design process of avionics software, and then analyze the factors affecting the reliability analysis by setting up simulation experiments. When no assumptions of fault occurrences made on the task model, this reliability model regresses to a generic schedulability model.     

Real-time performance reliability prediction

The purpose of this paper is to describe an approach to real-time reliability prediction, applicable to an individual product unit, operating under dynamic conditions. The concept of conditional reliability estimation is extended to real-time applications using time-series analysis techniques to bridge the gap between physical measurement and reliability prediction. The model is based on empirical measurements, self-generating, and applicable to online applications. This approach has been demonstrated to the prototype level. Physical performance is measured and forecast across time to estimate reliability. Time-series analysis is adapted to forecast performance. Exponential smoothing with a linear level and trend adaptation is applied. This procedure is computationally recursive and provides short-term, real-time performance forecasts which are linked directly to conditional reliability estimates. Failure clues must be present in the physical signals, and failure must be defined in terms of physical measures to accomplish this linkage. On-line, real-time applications of performance reliability prediction are useful in operation control as well as predictive maintenance     

A System Reliability Model with Classes of Failures

This paper presents an approach to system reliability involving s-dependence of the workload as well as the system configuration. Four classes of failures are described and then incorporated into the workload model. Mean time to failure and the system reliability are the functions of parameters estimated by monitoring a real system. The model allows multiple classes of users and priority requests to be represented. The model is validated using measurement data collected in an IBM installation.     

利用微分几何参数优化圆阵测向性能的方法研究

阵列测向系统的测向性能除了受所选算法影响外,阵列的几何结构也是重要的影响因素。本文利用微分几何工具,首先研究了平面阵阵列流形的微分几何参数与信号来向估计精度之间的关系,在此基础上推导并分析了圆阵在各方向上的测向性能与阵列几何布局的关系,讨论了影响测向性能的因素,最后给出了在圆阵孔径与阵元数固定的条件下,通过调整阵元在圆周上的位置来优化单信号入射时阵列某方向上测向性能的方法,并通过对几组不同几何布局的圆阵进行计算机仿真对比和分析,验证了设计思路的可行性,这为实际应用中阵列结构的选择提供了有用的参考。      

Thermal reliability prediction and analysis for high-density electronic systems based on the Markov process

Thermal-mechanical fatigue is one of the main failure modes for electronic systems, particularly for high-density electronic systems with high-power components. Thermal reliability estimation and prediction have been an increasing concern for improving the safety and reliability of electronic systems. In this paper, we propose a stochastic process prediction model to estimate the thermal reliability of an electronic system based on Markov theory. We first divided the high-density electronic systems into four modules: the energy transformation and protection module, the electronic control module, the connection module, and the signal transmission and transformation module. By integrating failure and repair characteristics of the four modules, a stochastic model of thermal reliability analysis and prediction for a whole electronic system was built based on the Markov process. The feature parameters of thermal reliability evaluation, including thermal reliability, thermal failure probability, mean time between thermal faults, and thermal stable availability, were derived based on our comprehensive model. Finally, we applied the model to an indoor electronic system of DC frequency conversion conditioning. The thermal reliability was estimated and predicted using tested failure and debugging repair data. Effective methods for improving thermal reliability are presented and analyzed based on the comprehensive Markov model.     

不同的退化模式共存时产品退化失效分析

在高可靠性、长寿命产品的可靠性分析中,当产品的失效为退化型失效时,利用产品的性能退化数据进行可靠性分析是一种更合理的方法。在考虑产品既存在平稳退化,又存在随机退化时,研究了产品退化失效的一般模型并给出了模型参数的估计方法。最后,利用所给的模型对强激光装置所用的某型金属化膜脉冲电容器进行了可靠性分析,并验证了该方法的可行性。     

Reliability growth plot - An underutilized tool in reliability analysis

System reliability and performance are improved by continuous improvement effort. The study of the increase in reliability as a function of time is the subject of reliability growth. Although the most well-known reliability growth model, the Duane model, is proposed more than thirty years ago, reliability growth analysis has attracted an increasing interest only recently because of the lack of time for testing and the high reliability of improved products leading to very few failures. In this paper we study a practical approach in reliability growth analysis. Based on the graphical plotting of failure data for some selected models, reliability can easily be estimated and predicted. This approach which is the original idea of the Duane model, overcomes the problem of parameter estimation and model validation that is usually complicated. It is especially useful when the model validation has to be done in order to select a suitable model. The approach, called the First-Model-Validation-Then-Parameter-Estimation approach, is simple and practical for the analysis of reliability growth data. We further develop some models and discuss their applicability in reliability engineering.     

Modeling fading channel-estimation errors in pilot-symbol-assisted systems, with application to turbo codes

In this paper, we address the issue of imperfect channel estimation in coded systems on fading channels. Since performance of channel codes is influenced in different ways by different components of channel-estimation errors, we develop a simplified model which separates the estimation errors of a Wiener-filtered received signal into the amplitude error and the phase error. Based on the model, we derive tight bounds on component error variances. Moreover, we prove that the classical Wiener filter results in a biased estimate of the channel amplitude. We also show that the probability of having a phase-estimation error large enough to cause decision errors in the receiver is significant. Using our model, we derive an approximate upper limit on the optimum pilot-symbol spacing and approximate lower limit on bit-error rate performance of coded systems with a given pilot-symbol separation. The proposed model and derivations are confirmed by extensive simulations.     

Reliability/performance evaluation for a multisystem with preventive maintenance

In general, a computing system with high reliability can be achieved by redundancy and/or maintenance. The sophisticated maintenance techniques are more and more important since a computing system can be operated by applying remote maintenance. In this paper we discuss a multisystem with preventive maintenance, which is one of the fault-tolerant computing systems. Making the suitable assumptions, we construct a stochastic model of such a computing system for evaluating reliability/performance. Applying Markov renewal and queueing theories, we analytically obtain several reliability/performance measures. Calculating such measures numerically, we show the impacts of preventive maintenance on such a computing system.     

Reliability and performance of tree-structured grid services

Grid computing is a new emerging technology aiming at large-scale resource sharing, and global-area collaboration. It is a next step in an evolution of parallel and distributed computing. Due to the large scale and complexity of the grid system, its performance and reliability are difficult to model, analyse, and evaluate. This paper presents a model that relaxes some assumptions unsuitable for grid computing systems that have been made in the existed works studying the distributed systems. The paper proposes a virtual tree model of the grid service. This model simplifies the physical structure of a grid service, allows service performance (execution time) to be estimated, and takes into account the common cause failures in communication channels. Based on the model, an algorithm for evaluating the grid service performance distribution and the service reliability indices is suggested. The algorithm is based on graph theory, and Bayesian analysis. Illustrative examples are presented in which the results of the suggested algorithm are compared with simulation results.     

A Multistage Architecture for Statistical Inference with Stochastic Signal Acquisition

We describe a statistical inference approach for designing signal acquisition interfaces and inference systems with stochastic devices. A signal is observed by an array of binary comparison sensors, such as highly scaled comparators in an analog-to-digital converter, that exhibit random offsets in their reference levels due to process variations or other uncertainties. These offsets can limit the performance of conventional measurement devices. In our approach, we build redundancy into the sensor array and use statistical estimation techniques to account for uncertainty in the observations and produce a more reliable estimate of the acquired signal. We develop an observational model and find a Cramér-Rao lower bound on the achievable square error performance of such a system. We then propose a two-stage inference architecture that uses a coarse estimate to select a subset of the sensor outputs for further processing, reducing the overall complexity of the system while achieving near-optimal performance. The performance of the architecture is demonstrated using a simulated prototype for parameter estimation and symbol detection applications. The results suggest the feasibility of using unreliable components to build reliable signal acquisition and inference systems.     

A Best Practice Guide to Resource Forecasting for Computing Systems

Recently, measurement-based studies of software systems have proliferated, reflecting an increasingly empirical focus on system availability, reliability, aging, and fault tolerance. However, it is a nontrivial, error-prone, arduous, and time-consuming task even for experienced system administrators, and statistical analysts to know what a reasonable set of steps should include to model, and successfully predict performance variables, or system failures of a complex software system. Reported results are fragmented, and focus on applying statistical regression techniques to monitored numerical system data. In this paper, we propose a best practice guide for building empirical models based on our experience with forecasting Apache web server performance variables, and forecasting call availability of a real-world telecommunication system. To substantiate the presented guide, and to demonstrate our approach in a step by step manner, we model, and predict the response time, and the amount of free physical memory of an Apache web server system, as well as the call availability of an industrial telecommunication system. Additionally, we present concrete results for a) variable selection where we cross benchmark three procedures, b) empirical model building where we cross benchmark four techniques, and c) sensitivity analysis. This best practice guide intends to assist in configuring modeling approaches systematically for best estimation, and prediction results.     

An accurate model for soft error rate estimation considering dynamic voltage and frequency scaling effects

Due to shrinking feature size and higher transistor count in a single chip in modern fabrication technologies, power consumption and soft error reliability have become two critical challenges which chip designers are facing in new silicon integrated circuits. Recent studies have shown that these issues have compromising effects on each other. Besides, power consumption and reliability significantly vary across workloads and among pieces of a single application which can be exploited to design adaptive runtime fault tolerant and low power systems. These attractions have been exploited in prior studies to design online reconfigurable fault tolerant systems with power management schemes. However, those attempts are driven by complicated simulations and hardly deliver a sense of direction to the designers. To achieve maximum efficiency in terms of power, performance, and reliability in dynamic scaling of voltage and frequency, it is critical to have a simple and accurate reliability model which estimates the value of fault rate considering supply voltage and operating frequency of a circuit. In this paper, we propose an accurate formula for analytic modeling of the soft error rate of a system which can be used to precisely track the reliability of the system under dynamic voltage and frequency adjustments. The experimental results of this paper prove that our proposed model offers precise estimates of reliability in accordance with the results of accurate soft error rate (SER) estimation algorithm for ISCAS85’s benchmark circuits.