Joint optimization of lot-sizing and maintenance policy for a partially observable two-unit system

In this paper, we present a new model to find the jointly optimal economic manufacturing quantity (EMQ) and preventive maintenance (PM) policy for a complex production facility. Unlike the previous joint models which dealt with EMQ and maintenance policy considering a single unit production facility and traditional maintenance approaches, we consider a production facility which consists of two modules with economic dependence. The more expensive module (unit 1) is subject to condition monitoring (CM), and only the age information of the second module (unit 2) is available, which follows a general distribution. The deterioration process of unit 1 is modeled as a continuous time hidden-Markov process. CM data is available at the end of each production run, and it provides only partial information about the hidden state of unit 1. The failure state of unit 1 is observable at any time. The objective is to develop a jointly optimal lot sizing and maintenance policy for a two-unit production facility using multivariate Bayesian control approach by minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the semi-Markov decision process (SMDP) framework. Also, a formula for the mean residual life (MRL) of the production facility is derived, which is an important statistic for practical applications. A practical example of the wind turbine CM and maintenance is provided and a comparison with other policies shows an outstanding performance of the new model and the control policy proposed in this paper.     

状态不完全可观条件下设备检修策略研究

为了诊断状态不完全可观条件下的设备状况,介绍了部分可观察的马尔可夫决策过程的基本原理和Perseus近似算法的基本流程.给出了基于设备加工次品率和某些核心组件振动信号诊断信息的设备状态评估方法.在基于设备状态的视情维修模式下,考虑了检测手段的局限性和检测结果的不确定性,并以某轿车发动机缸体生产线上的一台加工中心为例,建立了以最小化折扣费用为目标的设备检测维修的部分可观察马尔可夫决策过程模型.最后应用Perseus近似算法对模型进行了求解,得到了有限区间条件下的近似最优检测、维修策略和近似最优的折扣费用值.     

基于状态的单部件维修策略优化方法

为降低装备中关键单部件的预防性维修费用,把部件劣化过程看成离散状态变换的过程,提出了部件从投入使用开始到发生一次故障进行修复的平均时间间隔、平均检查次数、平均预防性维修次数和平均维修总费用的计算表达式.以单位时间内平均总维修费用最小化为目标,建立了基于状态的预防性维修策略优化模型及其求解方法.实验结果表明,本方法可以快速确定部件的检查时间间隔和预防性维修状态,有效地降低单位时间内平均总维修费用.     

热控(自动控制)设备开展状态检修的可行性

介绍了火电厂热控(热工过程自动控制)设备检修面临的问题和挑战,自动控制设备的特点,开展状态检修的意义、条件和方法.     

基于状态维修的开放系统研究与实现

为解决基于状态维修系统间硬件和软件组件的交互和集成、基于状态维修系统组件的快速开发、系统组件的灵活更新和维护费用的节约等问题,开发了一个基于状态维修的开放系统架构,该架构的核心是对分布式基于状态维修软件结构模型的定义。首先定义了分布式基于状态维修软件结构模型,然后论述了分布式基于状态维修网络平台的实现,最后给出了基于状态维修软件结构模型验证的实例。     

Optimal periodic replacement policy for a two-unit system with failure rate interaction

In this paper, an economic periodic replacement model for a two-unit system with failure rate interaction between units is presented. In this model, whenever unit 1 fails, it causes a certain amount of damage to unit 2 by increasing the failure rate of unit 2 by a certain degree, while each unit 2 failure induces unit 1 into instantaneous failure. Without failure interaction between units, the failure rates of two units also increase with through an ageing process. The two-unit system is all replaced at age T, or on failure, whichever occurs first. The aim of this paper is to derive the long-run expected cost per unit time by introducing relative costs as a criterion of optimality. The optimal period T^* that minimizes that cost is also discussed. A numerical example is given to illustrate the method.     

Throughput-driven condition-based maintenance for frequently reconfigured mass production equipment

Heavily utilised reconfigurable production equipment has uneven deterioration rate caused by frequently changing configurations and complex subsystem interaction. Thus, traditional maintenance approaches that rely on reliability modelling are ineffective when applied on this class of equipment. This paper introduces a throughput-driven condition-based maintenance framework intended to perform predictive maintenance for heavily utilised reconfigurable production equipments. The effectiveness of the proposed method is experimentally validated using actual production records of the semiconductor test handlers. The primary benefit of the proposed framework is its prediction efficacy while accounting for the complex subsystem interaction through Bayesian statistics. It is low cost to implement because it only uses existing resources already available in most manufacturing plants.     

An online adaptive condition-based maintenance method for mechanical systems

This paper proposes an online adaptive condition-based maintenance method with pattern discovery and fault learning capabilities for mechanical systems. The method is mainly based on a subtype of neural network techniques called self-organizing map (SOM). It is able to reduce local clusters from the same pattern and optimize the SOM architecture to further decrease the calculation cost in matching patterns in the neuron fitting process. Moreover, distance analysis and statistical pattern recognition (SPR) on neurons of the SOM are combined to establish rules and criteria for conducting and controlling the discovery and learning process so continuous process as purging prototypes on the map can be avoided. An experiment on condition monitoring of a machine tool test bed demonstrates and validates the effectiveness of the proposed approach.     

Scheduling flowshops with condition-based maintenance constraint to minimize expected makespan

Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a multi-objective genetic algorithm (MOGA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved MOGA, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The proposed algorithm is evaluated on some representative instances, and the comparison with other approaches in the latest papers validates the effectiveness of the proposed algorithm.     

Metaheuristic-based inspection policy for a one-shot system with two types of units

In this paper, we address an inspection policy problem for a one-shot system with two types of units, namely, Type 1 units that fail at random times and Type 2 units that degrade with time. Interval availability and life cycle cost are used as optimization criteria and estimated by simulation. We determine inspection intervals, preventive replacement ages of Type 1 units, and preventive maintenance thresholds of Type 2 units that have minimal life cycle cost and satisfy the target interval availability during inspection periods. A simulation-based optimization procedure using a hybrid genetic algorithm is proposed to find near-optimal solutions. Numerical examples are studied to investigate the effects of model parameters on optimal solutions and compare the hybrid genetic algorithm with the general genetic algorithm.     

Current status of machine prognostics in condition-based maintenance: a review

Condition-based maintenance (CBM) is a decision-making strategy based on real-time diagnosis of impending failures and prognosis of future equipment health. It is a proactive process that requires the development of a predictive model that can trigger the alarm for corresponding maintenance. Prognostic methodologies for CBM have only recently been introduced into the technical literature and become such a focus in the field of maintenance research and development. There are many research and development on a variety of technologies and algorithms that can be regarded as the steps toward prognostic maintenance. They are needed in order to support decision making and manage operational reliability. In this paper, recent literature that focuses on the machine prognostics has been reviewed. Generally, prognostic models can be classified into four categories: physical model, knowledge-based model, data-driven model, and combination model. Various techniques and algorithms have been developed depending on what models they usually adopt. Based on the review of some typical approaches and new introduced methods, advantages and disadvantages of these methodologies are discussed. From the literature review, some increasing trends appeared in the research field of machine prognostics are summarized. Furthermore, the future research directions have been explored.     

Optimal production run length and maintenance schedule for a deteriorating production system

This study considers manufacturing cost, inventory holding cost, system setup and maintenance costs, restoration cost, and warranty cost to a deteriorating production system whose state can be classified into in-control or out-of-control state at any time. This study develops a framework of simultaneous determination of optimal production run length and maintenance schedule and shows that there exists a unique optimal production run length and scheduled maintenance policy that minimize the expected total cost per item. The mathematical conditions to the optimal production run length and scheduled maintenance policy for a production cycle are deduced.     

A neural network application for reliability modelling and condition-based predictive maintenance

Traditionally, decisions on the use of machinery are based on previous experience, historical data and common sense. However, carrying out an effective predictive maintenance plan, information about current machine conditions must be made known to the decision-maker. In this paper, a new method of obtaining maintenance information has been proposed. By integrating traditional reliability modelling techniques with a real-time, online performance estimation model, machine reliability information such as hazard rate and mean time between failures can be calculated. Essentially, this paper presents an innovative method to synthesise low level information (such as vibration signals) with high level information (like reliability statistics) to form a rigorous theoretical base for better machine maintenance.     

带缓冲区的三设备串行生产系统动态机会维护策略研究

以采用周定周期预防维护技术并带有中间缓存区的三设备串行生产系统为研究对象,基于机会结余和组成本理论,运用动态规划方法,在组合优化的基础上,建立一种基于设备可靠性的三设备串行系统动态机会维护策略及决策优化模型.该模型克服了在当前生产系统中设备只有短期信息可用,而导致预防性维护计划制定的灵活性不足这一问题.数值试验结果表明,该模型能在控制维护成本的基础上提供有效的维护调度方案,为企业的设备维护管理决策问题提供了一种新的解决思路.     

基于RDCM的设备视情维护系统的技术研究

在全球制造业迅速扩张和激烈竞争的环境下 ,企业设备技术水平在不断提高 ,传统的设备维护方式已经很难满足企业的要求 ,发展基于远程诊断和状态监测 (RemoteDiagnosis&ConditionMonitoring———RDCM )的设备视情维护系统 ,可以为企业降低成本、提高效益并保持其在全球市场上的竞争力。在分析传统设备维护方式弊端的基础上 ,提出了基于RDCM的设备视情维护系统的三层体系结构 ,即Infranet -Intranet -Internet ,并详细地阐述了系统基于J2EE框架的开发方法以及系统实现的关键技术 ,最后介绍了系统的实施流程与步骤     

Optimal production policy for a manufacturing system with volume flexibility in a supply chain under lumpy demand

A batch production system with volume flexibility in a supply chain where a manufacturer procures raw materials and/or component parts from multiple suppliers in a lot and processes them into a single product, is considered in this research. First of all, a decision-making model is developed for an optimal production rate selection in the volume-flexibility production system, where a single type of finished products is delivered periodically in fixed quantities within a fixed interval of time. In this model, the production rate is perceived as a decision variable and unit production cost becomes a function of production rate. This model takes into account not only inventory costs of raw materials and finished goods, but production costs as well. Then, a pragmatic computation approach is presented to solve the proposed model for special unit-production-cost functions. It is used to determine an optimal production rate, and thus to decide an optimal ordering policy for procurement of raw materials from suppliers and an optimal manufacturing batch size. Some observations are made for optimal production rate analysis. Finally, a numerical study is conducted to illustrate the solution and a set of data with ten problems is tested and compared.     

工程装备保障资源优化配置与调度系统

为解决有限的工程装备保障资源优化配置与调度问题,降低运输成本等,基于全球定位系统(Global Position System,GPS)、通用无限分组业务(General Packet Radio Service,GPRS)和地理信息系统(Geography Information System,GIS)技术,以.NET网络体系为平台,结合智能优化算法等构建了工程装备保障资源优化调度系统.结果表明该系统实现了工程装备保障资源的可视化优化调度与配送,保证了保障资源的及时合理运输,降低了管理与运输成本.     

再制造系统废旧产品回收的最优控制策略

研究了再制造逆向物流系统中废旧产品库存的最优控制问题.为了能够从用户手中回收到合适数量的废旧产品,采用买回废旧产品的方式控制产品的返回强度.通过分析回收过程的特点,提出了一种新的随机库存控制模型,系统的最终目标是求取一个最优的价格策略,以达到有限周期系统总费用的期望值最小.在状态反馈控制规则下,得到了最优费用函数的递归方程,找到了具有两个状态阈值的最优反馈控制策略,并给出了求解最优策略的动态规划算法.最后,通过数值实例验证了算法的可行性,并给出了参数的敏感性分析.