An optimal burn‐in policy based on a degradation model

Burn‐in tests help manufacturers detect defective items and remove them before being sold to customers. In a competitive marketplace, cost is a major consideration and not employing a burn‐in test may result in higher and needless expenses. With this in mind, we consider degradation‐based burn‐in tests in which the degradation path follows a Wiener process and weak items are identified when the process crosses a piecewise linear function. We also study linear functions as a special case of such a piecewise linear barrier. Within this setup, we apply a cost model to determine the optimal burn‐in test. Finally, we discuss an illustrative example using GaAs laser degradation data and present an optimal burn‐in test for it.     

Availability optimization of systems subject to competing risk

This paper considers a competing risk (degradation and sudden failure) maintenance situation. A maintenance model and a repair cost model are presented. The degradation state of the units is continuously monitored. When either the degradation level reaches a predetermined threshold or a sudden failure occurs before the unit reaches the degradation threshold level, the unit is immediately repaired (renewed) and restored to operation. The subsequent repair times increase with the number of renewals. This process is repeated until a predetermined time is reached for preventive maintenance to be performed. The optimal maintenance schedule that maximizes the unit availability subject to repair cost constraint is determined in terms of the degradation threshold level and the time to perform preventive maintenance.     

On a Replacement Problem of a Cumulative Damage Model

Items are assumed to fail only by degradation. An appropriate stochastic model of such items is a cumulative process in which an item can fail only when the total amount of damage exceeds a prespecified failure level. This paper introduces a replacement policy in which an item is replaced at a certain level of damage before failure or at failure, whichever occurs first. The optimum replacement level of damage which will minimize the total expected cost per unit of time for an infinite time span is obtained. A numerical example is also presented. The total expected cost for a finite time span is also discussed.     

基于马尔可夫更新过程的多阶段冲击退化系统可靠性分析

系统所遭受的冲击和退化损害过程广泛存在着多阶段特征和相互依赖关系,为了更精确建模和分析系统冲击和退化间的依赖性,论文建立了多阶段冲击和退化过程的复合模型,提出了一种更加广义的冲击和退化过程依赖关系,即二者同时对系统损害累积过程产生贡献,进而导致系统阶段的改变,而状态转移又反馈性地影响冲击和退化过程。通过构造马尔可夫更新过程,基于半马尔科夫核,得到此类冲击退化模型的可靠度解析表达。     

Analysis of the reliability and the maintenance cost for finite life cycle systems subject to degradation and shocks

This paper deals with a deteriorating system subject to two different causes of failure: internal continuous degradation and sudden shocks. The degradation process is modelled using a gamma process. It is assumed that the system fails when the deterioration level reaches a critical threshold. Furthermore, sudden shocks arrive at the system at random times following a non-homogeneous Poisson process. When a sudden shock takes place, the system fails. To control the system reliability, a condition-based maintenance is applied. Under this maintenance policy, availability measures of the system are obtained. It is shown that these measures fulfil Markov renewal equations. A recursive method is developed to compute these measures. Furthermore, the maintenance cost of this system is analysed. Traditionally, the maintenance cost is analysed assuming an infinite time span. However, most systems have a finite life cycle and the application of the asymptotic approach is questionable. In this paper, the maintenance cost is analysed considering a finite life cycle. A recursive method, which combines numerical integration and Monte Carlo simulation, is developed to obtain the expected cost rate in the finite life cycle and its associated standard deviation.     

Optimal maintenance strategy under renewable warranty with repair time threshold

In this paper, we consider a periodic preventive maintenance model, from the manufacturer's perspective, which can be implemented to reduce the maintenance cost of a repairable product during a given warranty period. The product is assumed to deteriorate with age and the warranty policy we adopt in this paper takes into account the two factors of failure time and repair time of the product when the product failure occurs. Under the proposed two-factor warranty, a repair time threshold is pre-determined and if the repair takes more time than that of the threshold, the failed product is replaced with a renewed warranty policy. Otherwise, the product is only minimally repaired to return to the operating state. During such a renewable warranty period, preventive maintenance is conducted to reduce the rate of degradation periodically while the product is in operation. By assuming certain cost structures, we formulate the expected warranty cost during the warranty period from the manufacturer's perspective when a periodic preventive maintenance strategy is adapted. Although more frequent preventive maintenance increases the warranty cost, the chance of product failures would be reduced. The main aim of this paper is to accomplish the optimal trade-off between the warranty cost and the preventive maintenance period by determining the optimal preventive maintenance period that minimizes the total expected warranty cost during the warranty period. Assuming the power law process for the product failures, we illustrate our proposed maintenance model numerically and study the impact of relevant parameters on the optimal preventive maintenance policy.     

自保护相依多部件系统的预防维修建模及策略优化

自保护技术作为自愈技术的一种,能够使系统在环境或工况条件变化的干扰下以较高可靠性运行。本文构建了一个新的具有相依主要部件和辅助部件的系统可靠性模型,其中主要部件的退化速率与工作中的辅助部件的数量有关。此外,基于定期检测和预防维修策略,本文利用半再生过程技术求解了系统的长期运行平均成本,并以长期运行平均成本最小化为目标给出了系统的最优预防维修策略。最后,以镗刀系统为例,利用所提方法给出了预防更换阈值和检测周期的最优值,以期望为实际维修行为决策提供理论参考。     

The transformed inverse Gaussian process as an age- and state-dependent degradation model

In this paper, a transformed inverse Gaussian (TIG) process is introduced as a new family of monotonic degradation models. Different from most state-of-the-art degradation models, which can only characterize age-dependent performance degradation, the TIG process model is mainly introduced for degradation modelling of industrial products with age- and state-dependent performance degradation. With this new model, promising properties include (1) the modelling capability for characterizing products observed at discrete time points with age- and state-dependent degradation, (2) the mathematical tractability for calculating the reliability function and remaining useful life distribution with high efficiency, and (3) the modelling flexibility of incorporating explanatory variables and random effects for investigating a product population with unit-to-unit heterogeneity. To facilitate the degradation modelling and analysis, methods for parameter estimation and model selection are developed under a coherent Bayesian framework. Simulation studies and real cases are presented to demonstrate the proposed degradation model and the Bayesian methods.     

基于倒箱移动路径的集装箱堆场提箱作业优化模型

集装箱堆场提箱作业优化的目标是通过对倒箱搬运过程的优化使总作业成本最小。本文分析了正面吊设备的提箱作业过程,对作业规则和约束建立数学模型,在分析倒箱移动路径的基础上,提出了提箱作业优化模型。该模型为两层嵌套的组合优化模型,外层子模型针对提箱订单实现倒箱策略优化;内层子模型针对每一步倒箱作业寻找使倒箱作业成本最小的移动路径。提出了求解该模型的算法流程。最后,通过数值算例验证了优化模型的有效性。与传统人工作业方式的比较结果表明:本优化模型能够明显降低提箱作业成本。     

基于视情维修的质保政策设计研究

随着产品同质化程度不断提升,完善的质保服务已成为厂商提升品牌形象、刺激用户购买需求和全面反馈市场信息的主要途径。本文以具有退化特性的耐用品为研究对象,建立了视情维修条件下性能退化的维纳过程模型,考虑产品价格、质保期长度和维修费用承担比例对产品需求的影响,以最大化厂商利润为目标,确定了最优的质保期长度和维修费用承担比例。结合算例,比较了无视情维修和提供视情维修两种情形下对应的厂商利润,并分析了产品退化速率、成本参数和产品价格对利润的影响。研究结果表明,在质保服务范围内提供合理的视情维修服务既可将产品可靠性维持在一个较高水平,又可显著提升厂商利润。     

A Wiener process model with truncated normal distribution for reliability analysis

The use of degradation model to perform the reliability analysis has drawn much attention due to the fact that the performance of numerous highly reliable systems degrades over time. To describe the unit-to-unit variability for a population of systems, the random effect has been incorporated into the degradation model that plays an important part in assessing the reliability of deteriorating systems. In the existing literature, the normal distribution is commonly adopted to represent the random effect, but the assumption can be unsuitable for some practical applications, such as the degradation process of train wheels. In this paper, we present a degradation modeling and reliability estimation approach by using truncated normal distribution to characterize the unit-to-unit variability. A Wiener process with truncated normal distribution is firstly applied to model the degradation process of the deteriorating system, and the analytical expressions of probability density function and reliability function are derived. Expectation maximization algorithm is then used to estimate the model parameters. The effectiveness and feasibility of the presented approach are illustrated through a numerical example and practical case studies for laser devices and train wheels. The results indicate that the presented approach can obtain better reliability estimation results by considering the truncated normal distribution when the unit-to-unit variability has significant difference.     

Reliability and availability analysis of stochastic degradation systems based on bivariate Wiener processes

It is still a challenge to study the degradation mechanisms of complex systems with multiple performance characteristics. This paper develops a two-stage stochastic degradation model. The degeneration processes of two correlated performance characteristics are described by a correlated bivariate Wiener process in the first stage, in which neither of the degradation levels of performance characteristics reaches their thresholds. When one of the degradation levels of performance characteristics reaches its threshold, the system operates defectively in the second stage, and the system degradation process is described by the other performance characteristic which is modeled by a univariate Wiener process. The system fails completely when both the degradation levels of performance characteristics reach their thresholds. Explicit expressions of the system reliability and availabilities are given based on different inspection and maintenance policies. Simulation is also presented, and it shows that the analytical results and the simulation results are in good agreement. Finally, a detailed case study of the rail track geometry degradation is given to demonstrate the results obtained in the paper.     

An EOQ model with process reliability considerations

The classical economic order quantity (EOQ) model assumes that items produced are of perfect quality and that the unit cost of production is independent of demand. However, in realistic situations, product quality is never perfect, but is directly affected by the reliability of the production process. In this paper, we consider an EOQ model with imperfect production process and the unit production cost is directly related to process reliability and inversely related to the demand rate. In addition, a numerical example is given to illustrate the developed model. Sensitivity analysis is also performed and discussed.     

Sample path optimality for a Markov optimization problem

We study a unichain Markov decision process i.e. a controlled Markov process whose state process under a stationary policy is an ergodic Markov chain. Here the state and action spaces are assumed to be either finite or countable. When the state process is uniformly ergodic and the immediate cost is bounded then a policy that minimizes the long-term expected average cost also has an nth stage sample path cost that with probability one is asymptotically less than the nth stage sample path cost under any other non-optimal stationary policy with a larger expected average cost. This is a strengthening in the Markov model case of the a.s. asymptotically optimal property frequently discussed in the literature.     

A life-cycle optimization model using semi-markov process for highway bridge maintenance

Due to a variety of risks related to aging, construction, material degradation, harsh environment, increasing traffic, and insufficient capacity, a large percentage of bridges in the U.S. highway system are deteriorating beyond acceptable standards. Although significant investments are needed to bring bridges back to acceptable condition, most highway agencies lack the appropriate funding and therefore need effective methodologies for allocating limited resources efficiently and cost-effectively. This paper presents a life-cycle optimization model using a semi-Markov process and demonstrates how the proposed method can assist highway agencies to make more quantitative and explicit decisions for bridge maintenance. The 2012 National Bridge Inventory (NBI) dataset for the State of Texas was analyzed in this study to illustrate bridge structural responses and behaviors under uncertainty and risks. The proposed method is accurate when compared to real data and customized to help highway agencies to optimize their decisions on structuring bridge maintenance, and consequently, leading to cost savings and more efficient sustainability of their bridge systems. The major contribution of this research is the low-error model and process algorithm for selecting the most appropriate maintenance strategy. If employed properly, it may allow agencies to more effectively maintain an aging infrastructure system.     

Economic design of a complete inspection plan with interactive quality improvement

Consider the monitoring of an ongoing production process subject to complete inspection. Inferior quality products are either reworked or scrapped to assure good outgoing quality. Whenever the quality characteristic of a product exceeds a predetermined action limit, remedial action is taken to restore the process to an in-control state. In addition, the decision maker has a learning opportunity to improve the process by investing in resources to identify and eliminate the causes of deviation from the target quality. Taking a learning action would improve the mean value of the quality characteristic of items produced in an out-of-control state. This paper proposes a cost model to determine the optimal number of learning actions to be taken and the optimal action limit. The model considers the trade-off between quality cost and process improvement cost.     

A sequential software release policy

Most existing studies on software release policies use models based on the non-homogeneous Poisson process. In this paper, we discuss a software release policy based on a state space model. The state space model has a Gamma-Gamma type invariant conditional distribution. A cost model that removes errors in software systems and risk cost due to software failure is used. The optimal release time to minimize the expected cost in every test-debugging stage is discussed.     

Cost estimation algorithm and decision-making model for curriculum modification in educational organization

The decision about curriculum modification usually takes place at the knowledge level, mainly with consideration of individual academic staff competences and qualifications. However, traditional approaches to cost estimation of curriculum modification are focused on material resources only. In this paper we present a cost estimation method and decision model for curriculum modification in educational organizations. The proposed method works at the knowledge level and employs competence sets as knowledge representation models in educational organizations. Authors used the theory of hierarchical, multilevel systems in order to define the model of the decision-making process of curriculum modification and its dimension. Basing on this and also using a fuzzy competence model the cost estimation algorithm in the form of a group competences expansion algorithm is proposed. The algorithm focuses on the cost of staff competence expansion caused by the knowledge development process.     

Cost–benefit modelling for reliability growth

Decisions during the reliability growth development process of engineering equipment involve trade-offs between cost and risk. However slight, there exists a chance an item of equipment will not function as planned during its specified life. Consequently the producer can incur a financial penalty. To date, reliability growth research has focussed on the development of models to estimate the rate of failure from test data. Such models are used to support decisions about the effectiveness of options to improve reliability. The extension of reliability growth models to incorporate financial costs associated with ‘unreliability’ is much neglected. In this paper, we extend a Bayesian reliability growth model to include cost analysis. The rationale of the stochastic process underpinning the growth model and the cost structures are described. The ways in which this model can be used to support cost–benefit analysis during product development are discussed and illustrated through a simple case.