One-shot Systems Research Articles

A multi-objective mathematical model for selecting reliable suppliers for one-shot systems

A One-shot device is defined as a one-time unit that cannot be subjected to destructive tests or used more than once. Each component/sub-component used in one-shot systems, regardless of how it is supplied, has a specific risk level. On the other hand, each component affects the factors influencing the total system failure. It is the main objective of the present paper to provide a multi-objective model for determining suppliers of components used in one-shot systems. In this model, the risk associated with each supplier is initially estimated using risk assessment indicators. In the second stage, the risks associated with sub-components are assess using cause-and-effect matrices. Based on the likely catastrophic incidents to the system, the fault tree analysis is used and the relevant diagrams are drawn to identify the best composition of equipment suppliers. The results obtained from a case-study are then used in the especially developed multi-objective model to identify the allocations. The optimum values for the first and second objective functions are calculated to be 1489.349 and 0.809, respectively. Finally, the performance of the model is validated by sensitivity analysis.

Developing a novel mathematical approach toward minimizing sustainable circular economy costs of one-shot systems

Developing a novel mathematical approach toward minimizing sustainable circular economy costs of one-shot systems

Appropriate strategy selection for reliability-centered maintenance of one-shot systems using fuzzy model

PurposeThis paper addresses special reliability-centered maintenance (RCM) strategies for one-shot devices by providing fuzzy inferences system with the assumption that, to data, there is no data available on their maintenance. As far as one-shot devices are concerned, the relevant data is inadequate.Design/methodology/approachIn this paper, a fuzzy expert system is proposed to effectively select RCM strategies for one-shot devices. In this research: (1) a human expert team is provided, (2) spatial RCM strategies for one-shot devices and parameters bearing upon those strategies are determined, (3) the verbal variables of the expert team are transformed into fuzzy sets, (4) the relationship between parameters and strategies are designed whereupon a model is developed by MATLAB software, (5) Finally, the model is applied to a real-life one-shot system.FindingsThe finding of this study indicates that the proposed fuzzy expert system can determine the parameters affecting the choice of the appropriate one-shot RCM strategies, and a fuzzy inference system can help for effective decision making.Originality/valueThe developed model can be used as a fast and reliable method for determining an appropriate one-shot RCM strategy, whose results can be relied upon with a suitable approximation in respect of the behavior test. To the best authors’ knowledge, this problem is not addressed yet.

A new approach to select the reliable suppliers for one-shot devices

A one-shot device is referred to as a unit that performs its function only once, hence a one-shot device cannot be used for testing more than once. Missiles, airbags of automobiles, magnetorheological fluids, and thermal batteries are some examples of one-shot devices. Usually, one-shot devices are kept in storage or in the stand-by condition and taken into use when required. As a matter of fact, proper operation of such devices at designated times becomes an important issue. Thus, choosing appropriate and reliable suppliers of one-shot devices in preventive maintenance periods can be regarded as a critical measure. This paper presents a new approach to provide resilience in supplier selection for one-shot devices and its subsystems in response to risks. In this research: (1) an expert team is set up having experience and expertise in one-shot systems, (2) risks of suppliers are estimated using satisfying and simple additive weighting (SAW) methods, (3) risk related to each equipment are determined by a cause and effect matrix, (4) after presenting a novel integrated approach, the best combination for one-shot equipment suppliers is selected by using fault tree analysis, (5) finally, to evaluate the performance of the proposed approach, a real case study is also used. This approach can therefore be used for determining faults in the entire system evaluated along with allocating suppliers to each equipment by considering suppliers and equipment risks as well as making and selecting scenarios.

A sampling plan for one-shot systems considering destructive inspection

The paper is concerned with an acceptance sampling problem under destructive inspections for one-shot systems. The systems may fail at random times while they are operating (as the systems are considered to be operating when storage begins), and these failures can only be identified by inspection. Thus, n samples are randomly selected from N one-shot systems for periodic destructive inspection. After storage time T, the N systems are replaced if the number of working systems is less than a pre-specified threshold k. The primary purpose of this study is to determine the optimal number of samples n*, extracted from the N for destructive detection and the optimal acceptance number k*, in the sample under the constraint of the system interval availability, to minimize the expected cost rate. Numerical experiments are studied to investigate the effect of the parameters in sampling inspection on the optimal solutions.

Optimal Inspection Policy for One-Shot Systems Considering Reliability Goal

Optimal Inspection Policy for One-Shot Systems Considering Reliability Goal

Determining the inspection intervals for one-shot systems with support equipment

This paper considers systems that comprise one-shot devices and support equipment. One-shot devices are stored for long periods of time, and failures are detected only upon inspection. The support equipment needed to operate one-shot devices is maintained immediately upon failure. This paper addresses the inspection schedule problem for such systems with limited maintenance resources. The interval availability and life cycle cost are used as optimization criteria. The aim is to determine near-optimal inspection intervals for one-shot systems to minimize the expected life cycle cost and satisfy the target interval availability between inspection periods. An estimation of distribution algorithm (EDA) and a heuristic method are proposed to find the near-optimal solutions, and numerical examples are given to demonstrate the effects of the various model parameters to the near-optimal inspection intervals.

Storage reliability estimation of one-shot systems using accelerated destructive degradation data

A gamma process model is widely used to estimate reliability of general systems using non-destructive degradation data. However, the daunting challenge is the application of the model to one-shot systems given that recursive measurements are unavailable and degradation trends over time are nonlinear. This study shows a storage reliability estimation method of one-shot systems using accelerated destructive degradation data when information of either chemical reaction or degradation trend is unknown. An n-th kinetic model, which is a Physics-based model, was used to evaluate the degradation phenomenon of one-shot systems using destructive degradation data. The accelerated degradation model was constructed to estimate storage reliability for normal storage temperature. The proposed method was applied to estimate storage reliability of IR flare using the stabilizer contents that were destructively measured over time at three temperature levels. The real application results show applicability of the proposed method.

주기적 검사가 실시되는 원샷 시스템의 신뢰도 분석

This paper considers one-shot systems such as missiles, air bags of automobiles that are stored for a long time and are used at most once. One shot systems are inspected periodically to detect system failures and repair the system because we do not know whether the system will work or not on demand. Thus, we can keep high availability of the system by periodic inspection. Martinez (1984) obtained the system reliability approximately. In this paper, we obtain the exact system reliability under periodic inspection. Finally, we compare the system reliability from our formula with one of Martinez (1984) by numerical examples.

Optimum Maintenance Policy for a One-Shot System with Series Structure Considering Minimal Repair

One-shot systems such as missiles and extinguishers are placed in storage for a long time and used only once during their lives. Their reliability deteriorates with time even when they are in storage, and their failures are detected only through inspections for their characteristics. Thus, we need to decide an appropriate inspection policy for such systems. In this paper, we deal with a system comprising non-identical units in series, where only minimal repairs are performed when unit failures are detected by periodic inspections. The system is replaced and becomes “as good as new” when the nth failure of the system is detected. Our objective is to find the optimal inspection interval and number of failures before replacement that minimize the expected total system cost per unit of time.

다중비교를 이용한 샘플수와 샘플링 시점수의 원샷 시스템 신뢰도 추정방법 정확성에 대한 영향 분석

This paper provides simulation-based results of effect analysis of sample size and sampling periods on accuracy of reliability estimation methods using multiple comparisons with analysis of variance. Sum of squared errors in estimated reliability measures were evaluated through applying seven estimation methods for one-shot systems to simulated quantal-response data. Analysis of variance was implemented to investigate change in these errors according to variations of sample size and sampling periods for each estimation method, and then the effect analysis on accuracy in reliability estimation was performed using multiple comparisons based on sample size and sampling periods. An efficient way to allocate both sample size and sampling periods for reliability estimation tests of one-shot systems is proposed in this paper from the effect analysis results.

미소한 신뢰도 감소율을 가지는 원샷 시스템의 가부반응 데이터를 이용한 저장 신뢰도 추정방법 개발

This paper proposes a new reliability estimation method for one-shot systems using quantal response data, which is based on a parametric estimation method. The proposed method considers the time-variant failure ratio of the quantal response data and it can overcome the problems in parametric estimation methods. Seven reliability estimation methods in the literature were compared with the proposed method in terms of the accuracy of reliability estimation in order to verify the proposed method. To compare the accuracy of reliability estimation, the SSEs (Sum of Squared Error) of the reliability estimation results for the different estimation methods were evaluated according to the various numbers of samples tested. The proposed method provided more accurate reliability estimation results than any of the other methods from the results of the accuracy comparison.

Designing Reliability Demonstration Tests for One-Shot Systems Under Zero Component Failures

It is difficult to estimate the confidence interval of a system's reliability with zero failures experienced. We approach this problem by proposing a hybrid model that integrates the Bayesian model with the variance propagation technique. The Bayesian model will compute the moments of component reliability estimates, and the variance propagation technique is used to estimate the system reliability variance. The confidence interval for the system reliability is then derived by matching the moments with a beta distribution. As a major contribution, the distribution for reliability estimates with zero failures is explicitly derived. The performance of the new model is compared with existing methods, and further validated by simulation data. The results show that the hybrid model generally outperforms existing methods in terms of estimation accuracy. Because the new model does not require multiple integral calculations, it can be applied to design complex systems configured in mixed series-parallel or networked components.

A Reliability Growth Projection Model for One-Shot Systems

This paper offers several contributions to the area of discrete reliability growth projection. We present a new, logically derived model for estimating the reliability growth of complex, one-shot systems (i.e., the reliability following implementation of corrective actions to known failure modes). Multiple statistical estimation procedures are utilized to approximate this exact expression. A new estimation method is derived to approximate the vector of failure probabilities associated with a complex, one-shot system. A mathematically-convenient functional form for the s -expected initial reliability of a one-shot system is derived. Monte-Carlo simulation results are presented to highlight model accuracy with respect to resulting estimates of reliability growth. This model is useful to program managers, and reliability practitioners who wish to assess one-shot system reliability growth.

An analytical framework for reliability growth of one-shot systems

In this paper, we introduce a new reliability growth methodology for one-shot systems that is applicable to the case where all corrective actions are implemented at the end of the current test phase. The methodology consists of four model equations for assessing: expected reliability, the expected number of failure modes observed in testing, the expected probability of discovering new failure modes, and the expected portion of system unreliability associated with repeat failure modes. These model equations provide an analytical framework for which reliability practitioners can estimate reliability improvement, address goodness-of-fit concerns, quantify programmatic risk, and assess reliability maturity of one-shot systems. A numerical example is given to illustrate the value and utility of the presented approach. This methodology is useful to program managers and reliability practitioners interested in applying the techniques above in their reliability growth program.

Development programs for one‐shot systems using multiple‐state design reliability models

AbstractDesign reliability at the beginning of a product development program is typically low, and development costs can account for a large proportion of total product cost. We consider how to conduct development programs (series of tests and redesigns) for one‐shot systems (which are destroyed at first use or during testing). In rough terms, our aim is to both achieve high final design reliability and spend as little of a fixed budget as possible on development. We employ multiple‐state reliability models. Dynamic programming is used to identify a best test‐and‐redesign strategy and is shown to be presently computationally feasible for at least 5‐state models. Our analysis is flexible enough to allow for the accelerated stress testing needed in the case of ultra‐high reliability requirements, where testing otherwise provides little information on design reliability change. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004

Model misspecification effects within a family of alternative discrete reliability-growth models.

We describe a family of related discrete reliability-growth methodologies potentially applicable to one-shot systems undergoing a test-analyze-and-fix development process. The common feature shared by the models is their connection to Duane's renowned learning-curve property. The major difference, however, lies in their applicability in the context of two intrinsically different sampling schemes. For each model, a summary of the statistical properties of various estimators of the parameters as well as the reliability of the system, are reported. For purposes of assessing model misspecification, a particular text execution scenario conforming to a inverse sampling scheme is adopted. In reliability applications, it is not an uncommon practice to borrow inference results from models which are inappropriate in this setting. A detailed study of the potential impact of such misspecification on the estimation of system reliability is presented.

Development test programs for 1-shot systems: 2-state reliability and binary development-test results

This paper considers efficient development testing for one-shot systems (e.g., missiles) that are destroyed in testing or first normal use, where there is the possibility of reliability growth of the basic system design as a result of redesigns following failed development tests. They consider a situation where the cost of redesign is negligible, each development test produces a binary (success-failure) outcome, and there is a fixed procurement budget covering both system development and purchase. For a two-state model of system reliability, dynamic programming is used to identify test-plans that are optimal, viz, maximize the mean number of effective systems (of the final design) that can be purchased with the remaining budget when development testing is stopped. Several reasonable and easily implemented suboptimal rules are also considered, and their performances are compared to that of the optimal rule for a variety of combinations of model parameters. Optimal tests plans are easily computable, even for problems where the initial budget is large, and for some combinations of model parameters offer important improvements over more naive test heuristics. The qualitative character of the present results is anticipated to extend to more complicated and realistic models for this problem.

Minimizing the total sample size when multiple 1-shot systems are compared against a common baseline

A test design is determined for simultaneously comparing the individual reliabilities of each of multiple alternative one-shot systems against the reliability of a single baseline, with a common specification for the size and power of each alternative-vs.-baseline comparison. Using the procedure of Y.J. Lee (1984), the total sample size (baseline plus alternatives) can be minimized by setting the ratio of the number of baseline system replicates to the number of replicates for each alternative system equal to the root of the number of alternative systems. >