Reliability Engineering最新文献

There is an urgent need for manufacturers and users of programmable electronic systems to be able to quantify the risk of system failure due to design faults in software. The reliability of the hardware components of such systems can be assessed using well-tried techniques. By contrast, software reliability is still a ‘grey area’, with no generally accepted methods of assessment.

This paper describes the results of using the Littlewood Stochastic Reliability Growth Model with maximum likelihood parameter estimation to forecast the behaviour of sets of simulated failure data, generated on the assumptions of the model and using a variety of parameter values. The forecasts are long-term, such as would be made for large software products whose reliability is important from the support cost point of view, but not critical as regards safety. The data is of the ‘grouped’ variety: counts of faults found in successive intervals.

The predictions are generally of low accuracy. They are particularly bad for extreme parameter values, corresponding to very many, very infrequently manifest faults, and to few frequently manifest faults. The length of the period of observation relative to the average rate of fault manifestation is also crucial.

Possible reasons for this poor performance and improvements to the estimation methods are discussed.

The existing fault tree technique is static, whereas the proposed technique using the Markovian process can treat the fault tree dynamically.

By using the Markovian process, it is possible to model the dynamic features of the existing fault tree and to handle the dependencies on the state of the system. This conbination allows detailed consideration of component maintenance, which is normally not considered in the on/off logic of the fault tree.

The Markovian process is based on the probabilistic models. It is also characterized by the state and the time, so the system, which is composed of a number of basic events, can be described at any time by specifying its state at that time.

In the Markovian approach for fault tree, the concept of the supercomponent is introduced in order to reduce the number of system states and the size of the transition matrix. Now, a number of basic events are considered to be one component in the Markovian process.

Using the proposed dynamic fault tree analysis, a sample calculation is performed. As a result, the unavailability is much less than the value for the static fault tree analysis. Namely, the conservatism of the current analysis is excluded in this paper. The dynamic behavior of each system state and of the overall system is well analyzed. The interactions between the supercomponent tested and the supercomponent not tested are dynamically analyzed, too.

In conclusion, by using the Markovian process and the concept of the supercomponent, the size of transition matrix is reduced, and especially, the effect of the tested supercomponent on the system is dynamically analyzed.

This paper describes a proportional hazards reliability analysis and some of the methods of statistical inference, based on asymptotic theory. The authors report application of the Weibull proportional hazards model to the analysis of actual field warranty data for automotive air conditioning compressors. Statistical inference is illustrated through asymptotic normal confidence intervals and the likelihood ratio test statistic. The research employed a commercial data set to test theories relevant to defensive systems reliability analysis, and illustrates technology transfer from defense-research to commercial applications.

A fault tree analysis and qualitative common cause failure analysis of a power distribution box system was performed utilizing the Mocus-Bacfire Beta Factor (MOBB) code developed previously. This paper illustrates the advantages of this method over other available common cause procedures. This study indicates that the accepted use of the rare event approximation in performing fault tree analysis may lead to underestimates of risk by neglecting common cause dependencies. These dependencies have been fully modelled here, where it is shown that the error factor for the total system failure rate is high when common cause failures dominate random failures.

This paper deals with product development to improve product quality. It examines two stochastic models incorporating development testing and derives the optimal testing plans to minimize expected costs for products sold with warranty.

In space-borne systems, the reliability of an electronic component is increased by derating, i.e. operating the component at a temperature or voltage stress below its normal capacity. The improvement in reliability due to derating can be estimated if the failure rate at derated stress is known. This paper shows a method of evaluating the failure rates of some electronic components at various stress ratios when the normal failure rate is known.

A simple closed form equation is presented for the asymptotic unavailability of a system consisting of N identical units of which M are required for operation. Nonoperating units can fail during standby, and at most R failed units can be under repair at any time. A Markov model is assumed, e.g. the units fail randomly and require a random time interval for repair; there are no periodic inspections or tests. The equation has useful pedagogical applications since there are six independent variables that can be changed.