Mechanical reliability by design

With distributed loads and strengths the condition of minimum whole life cost defines uniquely the safety margin (or factor of safety) that must be used. Established empirical factors used in current deterministic design are not then relevant. It is postulated that computer-aided design packages should incorporate a simultaneous optimization of the safety margin and maintenance policy in place of these factors, since once the design is finalized the whole life cost is finalized. The full development of stochastic CAD methods is seen as the next essential step to advance design techniques.     

Mechanical reliability,how do we advance?

The development of theory and practice for electronic and mechanical reliability is described, and mechanical reliability is seen to be lagging in practice. Theory and practice are described for a project from conception to market feedback for mechanical as well as for electronic reliability. It is noted that for each phase theoretical and practical methods exist for mechanical as well as for electronic reliability analysis. Activities are listed which can strengthen mechanical reliability theory and practice.     

Mechanical/structural reliability evaluation through Esscher''s method of approximation

A simple and effective computational procedure is presented for computing probabilities of failure of mechanical and structural systems. The method provides very close approximate solutions for well behaved linear limit state functions with independent normal or nonnormal, continuous or discrete random variables. But the random variables should have moment generating functions. This method is also applicable when the limit state function is a hyper sphere. Numerical examples are given including one for hyper sphere limit state.     

Mechanical reliability of ceramic windows in high frequency microwave heating devices

The temperature and stress distributions generated in ceramic window materials currently employed in microwave gyrotron tubes were determined for a variety of operating conditions. Face-cooled windows of both polycrystalline BeO and polycrystalline Al₂O₃ were considered. The actual analysis involved three steps. First, a computer program was used to determine the electric field distribution within the window at a given power level and frequency. This program was capable of describing both the radial and axial dependence of the electric field. Second, the field distribution was used to derive an expression for the heat generated per unit volume per unit time within the window due to dielectric losses. A generalized heat conduction computer code was then used to compute the temperature distribution based on the heat generation function. Finally, the stresses were determined from the temperature profiles using a finite-element computer program. Primary emphasis was given to determining the effect of frequency upon the resulting thermal stress distribution. In addition, the effects of increasing window diameter and enhancing heat removal from the windows were considered.     

Mechanical system reliability analysis using a combination of graph theory and Boolean function

A new method based on graph theory and Boolean function for assessing reliability of mechanical systems is proposed. The procedure for this approach consists of two parts. By using the graph theory, the formula for the reliability of a mechanical system that considers the interrelations of subsystems or components is generated. Use of the Boolean function to examine the failure interactions of two particular elements of the system, followed with demonstrations of how to incorporate such failure dependencies into the analysis of larger systems, a constructive algorithm for quantifying the genuine interconnections between the subsystems or components is provided. The combination of graph theory and Boolean function provides an effective way to evaluate the reliability of a large, complex mechanical system. A numerical example demonstrates that this method an effective approaches in system reliability analysis.     

基于差异演化算法和多属性决策的机电系统可靠性多目标优化设计

针对机电系统可靠性设计问题,以可靠性和费用(或体积等)最优为目标建立可靠性设计的多目标优化模型．提出了自适应多目标差异演化算法,该算法提出了自适应缩放因子和混沌交叉率,采用改进的快速排序方法构造Pareto最优解,采用NSGA-II的拥挤操作对档案文件进行消减．采用自适应多目标差异演化算法获得多目标问题的Pareto最优解,利用TOPSIS方法对Pareto最优解进行多属性决策．实际工程结果表明：自适应多目标差异演化算法调节参数更少,且求得的Pareto最优解分布均匀;采用基于TOPSIS的多属性决策方法得到的结果合理可行．     

Multinomial-exponential reliability function: a software reliability model

The multinomial-exponential reliability function (MERF) was developed during a detailed study of the software failure/correction processes. Later on MERF was approximated by a much simpler exponential reliability function (EARF), which keeps most of MERF mathematical properties, so the two functions together makes up a single reliability model. The reliability model MERF/EARF considers the software failure process as a non-homogeneous Poisson process (NHPP), and the repair (correction) process, a multinomial distribution. The model supposes that both processes are statistically independent.The paper discusses the model's theoretical basis, its mathematical properties and its application to software reliability. Nevertheless it is foreseen model applications to inspection and maintenance of physical systems. The paper includes a complete numerical example of the model application to a software reliability analysis.     

Optoelectronics reliability

This paper reviews degradation modes and reliability in several kinds of InGaAsP/InP lasers and photodetectors used in optical fibre transmission systems. From the viewpoint of degradation mechanisms, BH type Fabry–Perot lasers, DFB lasers, a 0.98 μm strained quantum well InGaAs/GaAs laser, and InGaAs PIN photodetectors and APDs are reviewed.     

基于可靠性框图的可靠性建模研究

针对传统可靠性仿真模型建模繁琐、编程困难的问题,以可靠性框图为基础,设计了基于ExtendSim的可靠性建模流程,以两单元并联可修系统为例,建立了基于“致命修复”和“即坏即修”策略的仿真模型,并与解析模型进行了对比验证.仿真结果表明建立的可靠性模型是可信的,且此建模方法易于工程技术人员掌握,具有一定的推广价值．     

A reliability allocation method of mechanism considering system performance reliability

It is generally believed that the reliability of a mechanical system is determined by its composition. The system operates properly when all of its components do not fail. Based on this assumption, the reliability of the system can be represented by the reliability of its components. A problem arises when applying this hypothesis to a system containing motion mechanisms. There is a phenomenon in motion mechanism that the components do not happen structural failure (we call it “Type I failure”) and joint failure (we call it “Type II failure”), but the function of the mechanism cannot meet the requirements (we call it “Type III failure”). A reliability allocation method, which synthetically considers the composition and Type III failure modes of the motion mechanism, is proposed to solve this problem. A relative dispersion factor is introduced to describe the failure dependence of components and is proposed to calculate the complexity and criticality. A series system reliability allocation model considering three types of failure modes is established. Finally, using an airplane gear door lock mechanism as an example, a comparative analysis of the system reliability allocation results with and without considering Type III failure modes is made. The allocation results show the component reliability value without considering Type III failure modes is less than that when considering them, which will increase the system hazards. The result considering Type III failure modes is more reasonable than that from the traditional method.     

Modelling schedule reliability

The significance of schedule reliability as a particularly important logistic objective is indisputable. In comparison to the objectives WIP, utilisation and throughput time, the modelling of the schedule reliability is, however, not very advanced until now. This paper transfers the process capability indices used in quality management to schedule reliability and demonstrates two methods for depicting the schedule reliability as a function of its influencing parameters.     

Exploring reliability data

Despite the recent revolution in statistical thinking and methodology. practical reliability analysis and assessment remains almost exclusively based on a black-box approach employing parametric statistical techniques and significance tests. Such practice, which is largely automatic for the industrial practitioner, implicity involves a large number of physically unreasonable assumptions that in practice are rarely met. Extensive investigation of reliability source data indicates a variety of differing data structures, which contradict the assumptions implicit in the usual methodology. As well as these, lack of homogeneity in the data, due, for instance, to multiple failure modes or misdefinition of environment, is commonly overlooked by the standard methodology. In this paper we argue the case for exploring reliability data. The pattern revealed by such exploration of a data set provides intrinsic information which helps to reinforce and reinterpret the engineering knowledge about the physical nature of the technological system to which the data refers. Employed in this way, the data analyst and the reliability engineer are partners in an iterative process aimed towards the greater understanding of the system and the process of failure. Despite current standard practice, the authors believe it to be critical that the structure of data analysis must reflect the structure in the failure data. Although standard methodology provides an easy and repeatable analysis, the authors' experience indicates that it is rarely an appropriate one. It is ironic that whereas methods to analyse the data structures commonly found in reliability data have been available for some time, the insistence about the standard black-box approach has prevented the identification of such ‘abnormal’ features in reliability data and the application of these approaches. We discuss simple graphical procedures to investigate the structure of reliability data, as well as more formal testing procedures which assist in decision-making methodology. Partial reviews of such methods have appeared previously and a more detailed development of the exploration approach and of the appropriate analysis it implies will be dealt with elsewhere. Here, our aim is to argue the case for the reliability analyst to LOOK AT THE DATA. and to analyse it accordingly.     

Forecasting reliability growth

This paper describes a method for estimating and forecasting reliability from attribute data, using the binomial model, when reliability requirements are very high and test data are limited. Integer data—specifically, numbers of failures — are converted into non-integer data. The rationale is that when engineering corrective action for a failure is implemented, the probability of recurrence of that failure is reduced; therefore, such failures should not be carried as full failures in subsequent reliability estimates. The reduced failure value for each failure mode is the upper limit on the probability of failure based on the number of successes after engineering corrective action has been implemented. Each failure value is less than one and diminishes as test programme successes continue. These numbers replace the integral numbers (of failures) in the binomial estimate. This method of reliability estimation was applied to attribute data from the life history of a previously tested system, and a reliability growth equation was fitted. It was then ‘calibrated’ for a current similar system's ultimate reliability requirements to provide a model for reliability growth over its entire life-cycle. By comparing current estimates of reliability with the expected value computed from the model, the forecast was obtained by extrapolation.     

Estimating geodesic-measurement reliability

It is pointed out that measurement reliability is a quantitative indication of required junctions being available. Formulas are given for the reliability criteria and examples are taken.Translated from Izmeritel'naya Tekhnika, No. 2, pp. 12–13, February, 1994.     

Quadric reliability indices

Limit state models based on multi-dimensional rotational paraboloids and hyperboloids are described, and approximate closed form solutions obtained for the associated failure probability and corresponding reliability index. The solutions presented depend only on the number of variables, minimum distance of the limit state surface from the origin and mean curvature at the design point, in a standard normal space. These results extend the range of analytic solutions for reliability indices, from linear and spherical surfaces, to include these rotational quadrics, and so permit a wider choice of limit state models.     

Component reliability specification

Building reliability into a product is costly and needs to be traded against the consequences of product unreliability. This article is the third in a series of three articles, where the first deals with optimal investment in reliability, which involves executing two tasks—(i) deciding on the reliability requirements and (ii) deciding on component specifications (SP) to achieve the desired reliability. The second article deals with the first task and in this third article, we focus on the second task.     

A study of operational and testing reliability in software reliability analysis

Software reliability is an important aspect of any complex equipment today. Software reliability is usually estimated based on reliability models such as nonhomogeneous Poisson process (NHPP) models. Software systems are improving in testing phase, while it normally does not change in operational phase. Depending on whether the reliability is to be predicted for testing phase or operation phase, different measure should be used. In this paper, two different reliability concepts, namely, the operational reliability and the testing reliability, are clarified and studied in detail. These concepts have been mixed up or even misused in some existing literature. Using different reliability concept will lead to different reliability values obtained and it will further lead to different reliability-based decisions made. The difference of the estimated reliabilities is studied and the effect on the optimal release time is investigated.