Low overhead fault-tolerant FPGA systems

Fault-tolerance is an important system metric for many operating environments, from automotive to space exploration. The conventional technique for improving system reliability is through component replication, which usually comes at significant cost: increased design time, testing, power consumption, volume, and weight. We have developed a new fault-tolerance approach that capitalizes on the unique reconfiguration capabilities of field programmable gate arrays (FPGA's). The physical design is partitioned into a set of tiles. In response to a component failure, a functionally equivalent tile that does not rely on the faulty component replaces the affected tile. Unlike application specific integrated circuit (ASIC) and microprocessor design methods, which result in fixed structures, this technique allows a single physical component to provide redundant backup for several types of components. Experimental results conducted on a subset of the MCNC benchmarks demonstrate a high level of reliability with low timing and hardware overhead     

Determining Component Reliability and Redundancy for Optimum System Reliability

The usual constrained reliability optimization problem is extended to include determining the optimal level of component reliability and the number of redundancies in each stage. With cost, weight, and volume constraints, the problem is one in which the component reliability is a variable, and the optimal trade-off between adding components and improving individual component reliability is determined. This is a mixed integer nonlinear programming problem in which the system reliability is to be maximized as a function of component reliability level and the number of components used at each stage. The model is illustrated with three general non linear constraints imposed on the system. The Hooke and Jeeves pattern search technique in combination with the heuristic approach by Aggarwal et al, is used to solve the problem. The Hooke and Jeeves pattern search technique is a sequential search routine for maximizing the system reliability, RS (R, X). The argument in the Hooke and Jeeves pattern search is the component reliability, R, which is varied according to exploratory moves and pattern moves until the maximum of RS (R, X) is obtained. The heuristic approach is applied to each value of the component reliability, R, to obtain the optimal number of redundancies, X, which maximizes RS (R, X) for the stated R.     

Estimation of system reliability for uncooled optical transmittersusing system reliability function

This paper investigates the system reliability for 155 Mb/s optical transmitters by deriving a system reliability function from reliability data of each component for transmitters, laser diode, photodiode, optical assembly, and driver IC. The reliability data for each component reliability function have been obtained from accelerated aging test. The reliability parameters such as failure rate, mean time-to-failure (MTTF), standard deviation are obtained from a probability plotting method. From the system reliability function, the MTTF of the optical transmitter at 65°C was estimated to be 47000 h with 95% confidence. In this estimation, we introduced modified lifetime of laser diodes and reliability function of optical assembly     

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.     

An algorithm for computing the reliability of weighted-k-out-of-nsystems

This paper constructs a new k-out-of-n model, viz, a weighted-k-out-of-n system, which has n components, each with its own positive integer weight (total system weight=w), such that the system is good (failed) if the total weight of good (failed) components is at least k. The reliability of the weighted-k-out-of-n:G system is the complement of the unreliability of a weighted-(w-k+1)-out-of-n:F system. Without loss of generality, the authors discuss the weighted-k-out-of-n:G system only. The k-out-of-n:G system is a special case of the weighted-k-out-of-n:G system wherein the weight of each component is 1. An efficient algorithm is given to evaluate the reliability of the weighted-k-out-of-n:G system. The time complexity of this algorithm is O(n.k)     

基于敏捷开发的构件化软件可靠性研究

通过对敏捷开发中基于构件的增量迭代开发过程的分析,并结合缺陷纠正率和测试充分度提出了一种可靠性分析方法.该方法将基于构件的敏捷开发软件项目划分成优先级不同的功能构件,通过对每次迭代过程中各个构件的可靠性点估计,获取迭代结束时软件系统的可靠性.最后以实际例子说明了方法的应用.     

System reliability design with deteriorative components

In constrained optimum system reliability problems, the reliability of each component is usually assumed to be fixed, and the optimal number of redundancies at each stage is determined. However, in real world the component reliability decreases as component deteriorates; i.e. the component reliability is dependent on its age. This paper presents a system reliability optimization problem with deteriorative components. We formulate this problem as a parametric nonlinear integer programming problem where the objective function has a time parameter t. A solution method is proposed for solving it. We believe that this model can provide very useful information for decision makers and reliability designers.     

Optimal Reliability Design of a System: A New Look

The reliability literature offers an abundance of methods for the optimal design of systems under some constraints. In most of the papers, the problem considered is: given reliabilities of each constituent component and their constraint-type data, optimize the system reliability. This amounts to the assignment of optimal redundancies to each stage of the system, with each component reliability specified. This is a partial optimization of the system reliability. At the design stage, a designer has many options, e.g., component reliability improvement and use of redundancy. A true optimal system design explores these alternatives explicitly. Our paper demonstrates the feasibility of arriving at an optimal system design using the latter concept. For simplicity, only a cost constraint is used, however, the approach is more general and can be extended to any number of constraints. A particular cost-reliability curve is used to illustrate the approach.     

基于Copula函数相依串联系统的可靠性分析

论文将Copula函数引人元件相依系统可靠性的研究中,利用Copula函数韵特性将两元件系统拟合为单部件系统,并求出拟合后系统的寿命分布函数;然后分别讨论了拟合后系统作为马尔科夫型与非马尔科夫型两种情况时的可靠性指标;最屠给出一个实例,并比较了系统相依葑独立时可靠性指标的差异。论文去除了传统研究中部件独立的假设,说明部...     

Hierarchical Variance Decomposition of System Reliability Estimates With Duplicated Components

A hierarchical decomposition procedure is proposed to determine the variance of the reliability estimate for complex systems with duplicated components. For these systems, multiple copies of the same component type are used within the system, but only a single reliability estimate is available for each distinct component type. The variance of the reliability estimate is magnified at the system-level due to the covariance of component reliability estimates. Estimating the covariance becomes a formidable task if the system structure is complicated. A hierarchical model is proposed to decompose the system reliability estimate into component levels through intermediate layers. The decomposition procedure causes reliability estimates of duplicated components to remain $s$-independent when computing the associated variance on the adjacent upper layer. The first order Taylor series expansion is used to propagate the variance from the component level to the system level via intermediate layers. The hierarchical decomposition is preferable for designing robust, reliable systems by reducing or minimizing the system reliability variance at the component level.      

System optimization with component reliability estimation uncertainty: a multi-criteria approach

Summary & Conclusions-This paper addresses system reliability optimization when component reliability estimates are treated as random variables with estimation uncertainty. System reliability optimization algorithms generally assume that component reliability values are known exactly, i.e., they are deterministic. In practice, that is rarely the case. For risk-averse system design, the estimation uncertainty, propagated from the component estimates, may result in unacceptable estimation uncertainty at the system-level. The system design problem is thus formulated with multiple objectives: (1) to maximize the system reliability estimate, and (2) to minimize its associated variance. This formulation of the reliability optimization is new, and the resulting solutions offer a unique perspective on system design. Once formulated in this manner, standard multiple objective concepts, including Pareto optimality, were used to determine solutions. Pareto optimality is an attractive alternative for this type of problem. It provides decision-makers the flexibility to choose the best-compromise solution. Pareto optimal solutions were found by solving a series of weighted objective problems with incrementally varied weights. Several sample systems are solved to demonstrate the approach presented in this paper. The first example is a hypothetical series-parallel system, and the second example is the fault tolerant distributed system architecture for a voice recognition system. The results indicate that significantly different designs are obtained when the formulation incorporates estimation uncertainty. If decision-makers are risk averse, and wish to consider estimation uncertainty, previously available methodologies are likely to be inadequate.     

Modeling and maximizing burn-in effectiveness

System burn-in can get rid of many residual defects left from component and subsystem burn-in since incompatibility exists not only among components but also among different subsystems and at the system level. Even if system, subsystem, and component burn-in are performed, the system reliability often does not achieve the requirement. In this case, redundancy is a good way to increase system reliability when improving component reliability is expensive. This paper proposes a nonlinear model to: estimate the optimal burn-in times for all levels, and determine the optimal amount of redundancy for each subsystem. For illustration, a bridge system configuration is considered; however, the model can be easily applied to other system configurations. Since there are few studies on system, subsystem, and component incompatibility, reasonable values are assigned for the compatibility factors at each level     

Quality-reliability chain modeling for system-reliability analysis of complex manufacturing processes

System reliability of a manufacturing process should address effects of both the manufacturing system (MS) component reliability, and the product quality. In a multi-station manufacturing process (MMP), the degradation of MS components at an upstream station can cause the deterioration of the downstream product quality. At the same time, the system component reliability can be affected by the deterioration of the incoming product quality of upstream stations. This kind of quality & reliability interaction characteristics can be observed in many manufacturing processes such as machining, assembly, and stamping. However, there is no available model to describe this complex relationship between product quality, and MS component reliability. This paper, considering the unique complex characteristics of MMP, proposes a new concept of quality & reliability chain (QR-Chain) effect to describe the complex propagation relationship of the interaction between MS component reliability, and product quality across all stations. Based on this, a general QR-chain model for MMP is proposed to integrate the product quality with the MS component reliability information for system reliability analysis. For evaluation of system reliability, both the exact analytic solution, and a simpler upper bound solution are provided. The upper bound is proved to be equal to the exact solution if the product quality does not have self-improvement, which is generally true in many MMP. Therefore, the developed QR-chain model, and its upper bound solution can be applied to many MMP.     

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     

Variance of system-reliability estimates with arbitrarily repeatedcomponents

A model is developed to determine the variance of system reliability estimates and to estimate confidence intervals for series-parallel systems with arbitrarily repeated components. For these systems, different copies of the same component-type are used several or many times within the system, but only a single reliability estimate is available for each distinct component-type. The single estimate is used everywhere the component appears in the system design, and component estimation-error is then magnified at the system-level. The "system-reliability estimate" variance and confidence intervals are derived when the number of component failures follow the binomial distribution with an unknown, yet estimable, probability of failure. The "system-reliability estimate" variance and confidence intervals are obtained by expressing system reliability as a linear sum of products of higher order moments for component unreliability. The generating function is used to determine the moments of the component-unreliability estimates. This model is preferable for many system reliability estimation problems because it does not require independent component and subsystem reliability estimates; it is demonstrated with an example     

Optimization Procedure for the Analysis of Coherent Structures

The determination of the reliability level at which to manufacture the components of a coherent structure so that the system reliability h(p) is at a certain level and the overall system cost is minimized is considered. The cost of utilizing component ci at reliability level pi, Ci(pi), is assumed to be a convex increasing function of pi with a continuous first derivative and Ci'(qi)>0 where qi is the lower bound on the reliability level for component ci. Since for most coherent structures the constraint set defines a nonconvex set, any mathematical programming procedure blindly applied to the program converges to a local optimum rather than a global optimum. However, in certain cases, the global optimum can be found for the series and parallel (SP) type of systems. The key to the solution is to optimize each module separately and then to substitute a component for each module where the cost function for the component is the value of the objective function for the module. As long as the cost function for each module maintains the convexity property with In R or In(1 - R) as the argument (R being the reliability of the module), the optimization procedure can continue and a global optimum found.     

Approximated reliability of r-out-of-n(F) system with common cause failure and maintenance

In modern industries very high reliability system are needed. To improve the reliability of system, the component redundancy and maintenance of component or system play an impotant role and must be studied. This paper presents a reliability model of a r-out-of-n(F) redundant system with maintenance and Common Cause Failure. Failed component repair times are arbitrarily distributed. The system is in a failed state when r units failed because of the combination of single element failure or CCF(Common Cause Failure). Laplace transformation of reliability is derived by using analysis of Markov state transition graph. By using the analyzed MTBF, we compute MTBF of r-out-of-n(F) system. The MTBF with CCF is saturable even if repair rate is large.Approximated reliability of the r-out-of-n(F) system with maintenance and Common Cause Failure O.SummaryThe paper presents a reliability model of a r-out-of-n(F) redundant system with maintenance and Common Cause Failure. Failed component repair times are arbitrarily distributed. The system is in a failed state when r units failed because of the combination of single element failure or Common Cause Failure. Laplace transformation of reliability is derived by using analysis of Markov state transition graph. By analyzing this mean visiting time equations, we compute MTBF and shows computational example. The MTBF with CCF is saturable even if repair rate is large. In general the maintenance overcomes MTBF bounds, But the repair method not overcome the MTBF saturation when the system has Common Cause Failure.     

部附件送修费用的偏最小二乘回归模型及敏感性分析

为了克服部附件送修费用的一般模型不能方便进行敏感性分析的缺点,考虑设计阶段下输入数据采集的可行性,应用偏最小二乘回归理论,通过变量投影重要度分析,筛选出重量、价格、平均非计划拆卸时间、平均车间修理时间、SRU的个数5个参数。采用对数线性关系式,构建了部附件送修费用的参数模型,并通过实际值与回归值的误差分析验证了该模型的可靠度。根据得到的参数模型和工程经验,确定了各参数的单一函数表达式及其取值范围;结合切线斜率即敏感度的事实,仿真计算了各个参数变化的显著敏感区间。结果表明,该模型精度较高,能方便进行敏感性分析,可作为飞机设计阶段工程人员选择合适部附件的实用工具。     

Solving the Redundancy Allocation Problem With a Mix of Components Using the Improved Surrogate Constraint Method

When designing a system, there are two methods that can be used to improve the system's reliability without changing the nature of the system: 1) using more reliable components, and/or 2) providing redundant components within the system. The redundancy allocation problem attempts to find the appropriate mix of components & redundancies within a system in order to either minimize cost subject to a minimum level of reliability, or maximize reliability subject to a maximum cost and weight. Redundancy allocation problems can be classified into two groups; one allows the system to have a mix of components with different characteristics incorporated in the system, while the other only allows one type of each component. The former group has a much larger solution space compared to the latter, and therefore obtaining an exact optimal or even a high quality solution for this problem may be more difficult. Optimization techniques, based on meta-heuristic approaches, have recently been proposed to solve the redundancy allocation problem with a mix of components. However, an exact solution method has not been developed. In this paper, we develop an exact solution method, based on the improved surrogate constraint (ISC) method, and use this method to find optimal solutions to problems previously presented in the literature     

Evaluation of system reliability with common-cause failures, by apseudo-environments model

An efficient method for calculating system reliability with CCFs (common-cause failures) is presented by applying the factoring (total probability) theorem when the system and its associated class of CCFs are both arbitrary. Existing methods apply this theorem recursively until no CCF remains to be considered, and so can be time-consuming in computation. The method applies such a theorem only once and can be carried out in two steps: (1) determine each state in terms of the occurrence (or not) of every CCF in the associated class, to regard it as a pseudo-environment and to calculate its probability or weight; (2) determine each resulting subsystem of the system under the environment, calculate its reliability as in the no CCF case and take the weighted sum of such reliabilities, viz, the system reliability. This method is in terms of a Markov process and requires only the occurrence rate of each CCF to obtain the probability of each environment and only the failure rate of each component to obtain the system reliability under each environment; hence, it is practical, efficient, and useful