A bulk queueing system with random failures and two phase repairs

The paper studies a queueing system with bulk arrivals and bulk departures wherein the service channel is subject to random failures and the repairs are performed in two phases. Units wait until both phases of repairs of the service channel are complete. The probability generating functions of the queue length have been obtained for the various states of the system and the corresponding results for the steady state have been derived. Two particular cases have been discussed and the mean queue length has been obtained explicitly. Finally depenedence of the mean queue length on failure and repair parameters have been shown graphically.     

Bayesian Analysis of the Weibull Process with Unknown Scale Parameter and Its Application to Acceptance Sampling

The Weibull process with unknown scale parameter is taken as a model for Bayesian decision making. The family of natural conjugate prior distributions for the scale parameter is exhibited and used in prior and posterior analysis. Preposterior analysis and several sampling schemes are then discussed. Preposterior analysis is given for an acceptance sampling problem with utility linear in the unknown mean of the Weibull process, in which the sampling scheme yields the first r failures in a life test of n items. An example is included.     

A system-based component test plan for a series system, withtype-II censoring

Acceptance testing is analyzed for a series system of n components, each having an unknown, different, constant failure rate. Components are tested individually, and tests are terminated when a preassigned number of failures is observed for each component. The total time-on-test for each component is noted, and a statistic is constructed using observed test times and the number of failures of the components; the statistic is based on the maximum likelihood estimation of system reliability. This statistic is then used in specifying a decision rule for accepting or rejecting the entire system. The design of the test plan is stated as an optimization problem which minimizes test costs while ensuring that specified consumer and producer risks on system reliability are not exceeded. Numerical examples are provided, and implications of the test plan are discussed     

Customer-Rush Near Warranty Expiration Limit, and Nonparametric Hazard Rate Estimation From Known Mileage Accumulation Rates

Time or mileage data obtained from warranty claims are generally more accurate for hard failures than for soft failures. For soft failures, automobile users sometimes delay reporting the warranty claim until the warranty coverage is about to expire. This results in an unusually high number of warranty claims near the end of warranty coverage. Because such a phenomenon of customer-rush near the warranty expiration limit occurs due to user behavior rather than due to the vehicle design, it creates a bias in the warranty dataset. Design improvement activities that use field reliability studies based on such data can potentially obtain a distorted picture of the reality, and lead to unwarranted, costly design changes. Research in the area of field reliability studies using warranty data provides several methods for warranty claims resulting from hard failures, and assumes reported time or mileage as actual time or mileage at failure. In this article, the phenomenon of customer-rush near the warranty expiration limit is addressed for arriving at nonparametric hazard rate estimates. The proposed methodology involves situations where estimates of mileage accumulation rates in the vehicle population are available. The claims influenced by soft failures are treated as left-censored, and are identified using information in technician comments about the repair carried out plus, if required, a more involved engineering analysis of field returned parts. Maximum likelihood estimates for the hazard function and their confidence limits are then obtained using Turnbull's iterative procedure. An application example illustrates use of the proposed methodology.     

A Comparison of Several Component-Testing Plans For A Parallel System

We consider a parallel (1-out-of-n:G) system of n components with constant failure rates and treat three different classes of component testing procedures all of which guarantee that the given consumer and producer risks are not exceeded. It is necessary to impose certain restrictions on the magnitude of the unknown failure rates for guaranteeing the producer risk. The three classes of component test procedures use Type-I censoring and use decision rules based on: A) the total number of component failures during the testing periods, B) the number of failures for each individual component, and C) the maximum likelihood estimate of system reliability. Based on the requirement that both the consumer and producer risks lie within specified levels, class A plans exhibit lower testing costs in the selected numerical examples.     

Sample Size for Selecting the Better of Two Weibull Populations

Suppose an experimenter wishes to select the better of two Weibull populations. In a life testing situation he may have some time t* for which the device must perform properly and he would consider the better population to be the one with the larger reliability at time t*. It is reasonable for the experimenter to test n items from each of the two populations until there are r(r < n) failures from each and then select as better the population for which the maximum likelihood estimate of the reliability at time t* is the larger. This paper presents the sample sizes needed to achieve a desired level for the probability of correct selection with such a decision rule.     

Prediction intervals for Weibull observations, based onearly-failure data

Let n similar components with Weibull lives be put on life-test simultaneously, and let the first r failures be observed. A prediction interval for failure-time X_k, 0相似文献   

Reliability modeling and assessment of the Star-Graph networks

The reliability of the Star Graph architecture is discussed. The robustness of the Star Graph network under node failures, link failures, and combined node and link failures is shown. The degradation of the Star Graph into Substar Graphs is used as the measure of system effectiveness in the face of failures. Models are provided for each of the failure and re-mapping modes evaluated herein, and the resilience of the Star Graph to failures is emphasized. This paper defines failure of a Star Graph as being when no fault-free (n - 1)-substars remain operational and the intermediate states are defined by the number of (n - 1)-substars that remain operational. A powerful tool (re-mapping) is introduced in which the number of operational (n 1)-substars can be maintained for longer periods, thus improving the overall MTTF (mean time to failure). For comparison the results of a similar reliability analysis of the hypercube is shown. The comparisons are considered conservative due to the failure model used herein for the star graph. One might apply re-mapping to hypercubes; while it would improve the overall MTTF of hypercubes, the hypercubes would still have an appreciably poorer performance than star graphs     

Estimation for a probabilistic stress-strength model

Estimation for an unknown strength distribution is considered in two situations: (1) independent identically distributed stresses are applied to the component until it fails (no cumulative damage); and (2) each applied stress causes damage to the component and damage cumulates until the component fails. Both situations lead to mixtures of probability distributions, with the strength distribution playing the role of the mixing distribution. Based on the observation of cycles to failure of several independent components and on the theory of mixtures of distributions, estimators of the mixing distributions are obtained using linear programming. In particular, the solution to the linear-programming problem yields a probability mass function which approximates the unknown strength distribution. From this estimate of the strength distribution, an estimate of the mean strength of the item can be obtained by the usual computation of the mean of a probability distribution. Hence, the results provide a method of estimating the mean strength, or other parameters of the strength distribution, without requiring observations directly on the strengths of the test components     

Effect of System Processes on Error Repetition: A Probabilistic and Measurement Approach

This paper presents an approach to system reliability modeling where failures and errors are not statistically independent. The repetition of failures and errors until their causes are removed is affected by the system processes and degrades system reliability. Four types of failures are introduced: hardware transients, software and hardware design errors, and program faults. Probability of failure, mean time to failure, and system reliability depend on the type of failure. Actual measurements show that the most critical factor for system reliability is the time after occurrence of a failure when this failure can be repeated in every process that accesses a failed component. An example involving measurements collected in an IBM 4331 installation validates the model and shows its applications. The degradation of system reliability can be appreciable even for very short periods of time. This is why the conditional probability of repetition of failures is introduced. The reliability model allows prediction of system reliability based on the calculation of the mean time to failure. The comparison with the measurement results shows that the model with process dependent repetition of failures approximates system reliability with better accuracy than the model with the assumption of independent failures.     

Estimation of Weibull Parameters With Competing-Mode Censoring

Existing results are reviewed for the maximum likelihood (ML) estimation of the parameters of a 2-parameter Weibull life distribution for the case where the data are censored by failures due to an arbitrary number of independent 2-parameter Weibull failure modes. For the case where all distributions have a common but unknown shape parameter the joint ML estimators are derived for i) a general percentile of the j-th distribution, ii) the common shape parameter, and iii) the proportion of failures due to failure mode j. Exact interval estimates of the common shape parameter are constructable in terms of the ML estimates obtained by using i) the data without regard to failure mode, and ii) existing tables of the percentage points of a certain pivotal function. Exact interval estimates for a general percentile of failure-mode-j distribution are calculable when the failure proportion due to failure-mode-j is known; otherwise a joint s-confidence region for the percentile and failure proportion is calculable. It is shown that sudden death endurance test results can be analyzed as a special case of competing-mode censoring. Tabular values for the construction of interval estimates for the 10-th percentile of the failure-mode-j distribution are given for 17 combinations of sample size (from 5 to 30) and number of failures.     

Prior Distributions Fitted to Observed Reliability Data

This paper describes methods of fitting prior distributions to equipment MTBF = ?, shows the priors fitted to different equipments, establishes data criteria for fitting prior distribution to ?, and presents the results of a robustness analysis performed on the fitted priors. Systematic procedures for fitting priors are shown for Type 1 data (number of failures in fixed time T) and Type 2 data (observed MTBF, number of failures not the same for all equipments), and specific data criteria, in the form of minimum values of n (number of equipments) and K (number of failures) are presented. The inverted-gamma prior-distributions were derived from operational failure data obtained from Tinker AFB. The equipments are primarily electronic, therefore, the time-to-failure distribution was assumed to be exponential; however, the methods are generally applicable whatever the form of the conditional distribution. The robustness analysis shows the effects of errors in estimating the parameters of the prior on the posterior distribution. In general, the effect of errors in estimating parameters of the prior was practically negligible for large values of K.     

结构自适应反步容错控制器设计

基于一类由状态空间描述的非线性系统固有的结构级联特性,在飞行器发生卡死故障的情况下,设计了基本控制律加补偿控制律的控制器结构形式,补偿故障对系统性能造成的影响。提出了确保对参考模型精确跟踪的控制器结构和条件。控制器的基本控制律部分是利用系统的结构特性设计反步(backstepping)控制律,系统发生未知卡死故障时,利用实际对象和参考模型之间的误差,更新控制器的故障补偿部分。控制器同时保证了闭环系统的稳定性和参考模型状态跟踪误差渐近性。仿真结果表明了该控制律对给定参考模型跟踪的有效性。     

Estimation and testing in an imperfect-inspection model

An imperfect inspection model in which failures can only be detected with probability β is considered. The lifetimes of all the components are IID exponential with rate λ. Using the approximate likelihood filtering method, a first order binomial autoregressive model is obtained when 1-β is small. When λ is known, a locally most powerful test is proposed for testing the hypothesis that β=1 against the alternative that β<1. The asymptotic distribution of the test statistic is obtained under the null hypothesis as well as under the alternative. The test statistic is modified to handle the case of unknown λ. Its asymptotic distribution is also given. The bias of the maximum likelihood estimators of β and λ obtained from the approximated likelihood function is studied by simulation     

An attempt to explain thermally induced soft failures during low level ESD stresses: study of the differences between soft and hard NMOS failures

This work focuses on the thermal soft failure mechanism occurring in NMOS transistors during low level ESD stresses. Soft and hard failure modes are both precisely characterized using atomic-force microscope (AFM) technique. Thermally induced soft failures are shown to be the first step of the hard failure mechanism. Furthermore, a strong relationship between both soft and hard failures is revealed. Contrary to what was reported until now, our investigation underlines the presence of two different hot spots during the formation of the large molten silicon filament leading to the disastrous short circuit called hard failure.     

Reliability Models of NMR Systems

Majority voted redundant systems are widely used. A reliability model is developed and analyzed for N-tuple Modular Redundancy-NMR: (n + 1)-out-of-(2n + 1)-where the units are subject to stuck-at-0, stuck-at-1 or stuck-at-X failures and where failures can occur in a mutually compensatory manner. A reconfiguration of the NMR redundancy, the NMR/Simplex strategy, is proposed and evaluated and its model shown to be included in the general model for the compensated NMR.     

Neural Adaptive Compensation Control for a Class of MIMO Uncertain Nonlinear Systems with Actuator Failures

A neural adaptive compensation control scheme for a class of multi-input multi-ouput (MIMO) uncertain nonlinear systems with actuator failures is proposed based on prescribed performance bound (PPB) transient performance which characterizes the convergence rate and maximum overshoot of the tracking error. RBF neural networks are used to approximate the error of plant model, the control law proposed can guarantee the asymptotic output tracking and closed-loop signal bounds. The control scheme is applied to a twin otter aircraft longitudinal nonlinear dynamics model in the presence of unknown failures. Simulation results demonstrate the effectiveness of the proposed method.     

Analyzing and Improving Network Availability in Interdomain Routing

The Internet now-a-days has become indispensable to each and everyone. It is vulnerable to node failures, link failures, and fluctuations due to many known or unknown reasons in the network connectivity. The bitter truth is even today networks’ failure, link faults are happening. A single change in a link or a node has a potential to trigger the unstable-routing-tables of many nodes. These failures may lead the network in an unstable state by increasing its convergence time significantly longer. In this paper we propose an algorithm to keep the value of the minimum route advertisement interval timer variable unlike the conventional approach of keeping it constant. The proposed approach makes the timer value varying depending on the receiver’s position with respect to the origin of the prefix advertised on the network. Simulations’ results show that the convergence time becomes significantly low and make the network converge relatively quicker.     

An inspection model with generally distributed restoration and repair times

In classical inspection models, it is assumed that the inspection time and the restoration time are either zero or fixed, and the production facility never breaks down. However, in real production, the production system is subject to random failures and the repair and restoration times are usually random. In this paper, we will study the effect of the exponential failure time and generally distributed restoration and repair time on the optimal inspection interval of a production unit subject to deterioration. Treating the process as a semi-regenerative process (SRP) and analyzing the SRP by Markov renewal theory, the formula for the long-run expected average cost per unit time and formulae for the steady-state probabilities of the SRP are obtained in an explicit form. The optimal inspection interval is obtained by minimizing the average cost function.