Modelling maintenance practice of production plant using the delay-time concept

In this paper we present a study carried out for a copper productsmanufacturing company, developing and applying the delay-timemodelling technique to model and thus optimize preventive maintenance(PM) of the plant. A key machine in the plant is used to illustratethe modelling process and management reaction. The parameter values of the process by which faults arise andof the delay-time distribution are estimated from maintenancerecord data of failures and faults found at PM, using the methodof maximum likelihood. A test of the model fit to data is carriedout. Based upon the estimated model parameters and the failuredelay time, an inspection model is proposed to describe therelationship between the total downtime and the PM interval.     

利用故障数据和估计的检查数据建立维修优化模型

利用时间延迟概念,根据故障记录数据和估计的检查数据建立了预防维修模型.通过对故障记录数据统计分析,提出了模型的假定条件.采用最大似然估计法,估计参数,包括缺陷发生率、不完全检查概率和时间延迟分布.建立了有关预防维修间隔期和总停机时间之间关系的检查模型,并根据估计参数和检查模型,计算最佳维修间隔期.     

A stochastic model for joint spare parts inventory and planned maintenance optimisation

Spare parts demands are usually generated by the need of maintenance either preventively or at failures. These demands are difficult to predict based on historical data of past spare parts usages, and therefore, the optimal inventory control policy may be also difficult to obtain. However, it is well known that maintenance costs are related to the availability of spare parts and the penalty cost of unavailable spare parts consists of usually the cost of, for example, extended downtime for waiting the spare parts and the emergency expedition cost for acquiring the spare parts. On the other hand, proper planned maintenance intervention can reduce the number of failures and associated costs but its performance also depends on the availability of spare parts. This paper presents the joint optimisation for both the inventory control of the spare parts and the Preventive Maintenance (PM) inspection interval. The decision variables are the order interval, PM interval and order quantity. Because of the random nature of plant failures, stochastic cost models for spare parts inventory and maintenance are derived and an enumeration algorithm with stochastic dynamic programming is employed for finding the joint optimal solutions over a finite time horizon. The delay-time concept developed for inspection modelling is used to construct the probabilities of the number of failures and the number of the defective items identified at a PM epoch, which has not been used in this type of problems before. The inventory model follows a periodic review policy but with the demand governed by the need for spare parts due to maintenance. We demonstrate the developed model using a numerical example.     

Generalized stationary models for scheduling single and multiple preventive maintenance routines

This paper is concerned with the development and applicationof stationary models for scheduling single and multiple preventivemaintenance (PM) situations focusing on issues of model implementation.The first part of the paper deals with the practical implementationof the basic single PM scheduling model based on the renewalprocess. The main practical difficulty is lifetime-distributionselection for small data sets which is typical in PM situations.Thus a sensitivity analysis of optimal PM interval to selectedlife distributions following PM and failures (corrective maintenance)is carried out. It has been found that the selected pair ofdistributions using AIC criteria as well as the Weibull–Weibullfitted pair have the smallest availability loss in estimatingthe optimal PM interval. The second part of this paper is concernedwith modelling multi-PM situations—something which hasreceived very little attention in the literature despite itsfrequent implementation in real life. A multi-PM model basedon the renewal process is discussed. The model assumes a multi-PMinterval which is an integer multiple of the single PM intervalsand different renewal functions following each type of PM. Theprocedure of model implementation is discussed through numericalexample.     

Delay-time-based preventive maintenance modelling for a production plant: a case study in a steel mill

This paper presents a case study of delay-time-based preventive maintenance (PM) modelling for a production plant system. Since production stoppages caused by waiting for raw materials provide windows to inspect and maintain the system, these production stoppages can be incorporated into the PM model. Considering the nature of different defects that can cause failures, two types of defects are modelled: small and large defects. Small defects are normally dealt with during production stoppages, but both small and large defects can be dealt with over a longer duration during PM. The parameters of the model are estimated using the maximum-likelihood method based on the real data. The model aims to find the optimal PM interval by minimizing the expected total downtime within an overhaul cycle. Management suggestions are also recommended.     

A delay-time-based maintenance model of a multi-component system

There is a well established literature on delay-time modelsof regular inspection policies where inspections may or maynotbe perfect, and where the initial point u of a defect arisesas a homogeneous Poisson process. This paper extends the modellingin two ways. The first is to include the observed practice wherethe multi-component system is inspected not only on a plannedbasis, but also when a component fails. The second extensionis to use a nonhomogeneous Poisson process to describe defectarrivals in the system. An inspection–replacement modelbased upon these two extensions is then developed for a multi-componentsystem. The total expected cost per unit time is minimized withrespect to theinspection intervals and the system replacementtime. The likelihood function of the time of failures and thenumber of defects found at inspections is established, in orderto estimate model parameters based upon routinely collectedmaintenance data. As a special case of the general model, aninspection model—based upon a homogeneous Poisson processof defects arising—is also proposed, which has a relativelysimple structure. Both simulated and real-life data of failuresand defects identified at inspections are used to test the modelsand parameter-estimating procedure.     

Age-reduction models for imperfect maintenance

Maintenance of a deteriorating system is often imperfect, withthe state of the system after maintenance being at a level somewherebetween new and its prior condition.In this paper, the conceptof reduction in virtual or effective age is used to model theeffect of both imperfect corrective maintenance (CM) and imperfectpreventive maitnenance (PM). Results from counting-process theorythen produce a likelihood function necessary for parameter estimation,and the method is tested on known maintenance data. Finally,it is shown how to evaluate, by simulation, the expected numberofsystem failures up to time t under a given periodic PM strategy.This measure is incorporated into a cost rate function whichis then minimized to find the optimal length of a PM intervaland the optimal number of PMs to carry out before system replacement     

The development of a hybrid intelligent maintenance optimisation system (HIMOS)

This paper reports on the development of a hybrid intelligent maintenance optimisation system (HIMOS) for decision support. It is a follow-up to an earlier paper published in the Journal of the Operational Research Society in 1995. Both papers refer to systems where there are very many components which may break down independently. When a component breaks down, corrective action (CO) is required. The problem is to determine the optimal maintenance policy, essentially the frequency of preventive maintenance (PM) which minimises the sum of down time due to PM and CO.HIMOS, like its predecessor IMOS, uses an ‘intelligent’ decision support system to carry out an automated analysis of the maintenance history data. Maintenance data are presented to the system and the most suitable mathematical model from a model-base is identified utilising a hybrid knowledge/case based system (KBS/CBR). Thus initially a rule base is applied to select a model, as in the case of IMOS. If no model is matched, the system reverts to its historical case-base to match the current case with a similar case that has been previously modelled. This double reasoning adds to the system's true learning capabilities (intelligence) and increases the rate of success of model selection. A prototype system is written in Visual Basic® for an IBM compatible PC. The study results include optimal PM intervals for a sample of industrial data sets. The results of the validation exercise of HIMOS against expert advice has shown that the system functions satisfactorily.     

Incorporating the potential for human error in maintenance models

The mathematics of delay-time modelling of inspection maintenance is extended to incorporate the existence of human error in the form of fault injection during the inspection process. After briefly discussing the basic delay-time model, modifications are introduced to model maintenance scenarios incorporating human error injected defects within the inspection maintenance process. The effects of human error are investigated with the emphasis on its representation and on the assessment of consequences, the objective being to provide a means of determining the cost of human error and to thereby aid corrective decision-making.     

Optimal production time and number of maintenance actions for an imperfect production system under equal-interval maintenance policy

This paper deals with the optimal production/maintenance (PM) policy for a deteriorating production system which may shift from the in-control state to the out-of-control state while producing items. The process is assumed to have a general shift distribution. Under the commonly used maintenance policy, equal-interval maintenance, the joint optimizations of the PM policy are derived such that the expected total cost per unit time is minimized. Different conditions for optimality, lower and upper bounds and uniqueness properties on the optimal PM policy are provided. The implications of another commonly used policy, to perform a maintenance action only at the end of the production run, are also discussed. Structural properties for the optimal policy are established so that an efficient solution procedure is obtained. In the exponential case, some extensions of the results obtained previously in the literature are presented. A numerical example is provided to illustrate the solution procedure for the optimal production and maintenance policy.     

Preventive maintenance for homogeneous and heterogeneous systems

We consider optimal preventive maintenance for homogeneous and heterogeneous systems with major (critical) and minor (noncritical) failures. A major failure results in a replacement of a failed system, whereas minor failures can be minimally instantaneously repaired. Distinct from the homogeneous case, where the process of minimal repairs is the Poisson process, the process of minimal repairs in the heterogeneous case is the mixed Poisson process that does not possess the memoryless property. This enables considering the number of minimal repairs as the decision parameter for the corresponding optimal preventive maintenance policy. The proposed approach is theoretically justified, and the detailed illustrative numerical examples are presented.     

A practical approach for reliability prediction of pipeline systems

Pipelines play an important role in the modern society. Failures of pipelines can have great impacts on economy, environment and community. Preventive maintenance (PM) is often conducted to improve the reliability of pipelines. Modern asset management practice requires accurate predictability of the reliability of pipelines with multiple PM actions, especially when these PM actions involve imperfect repairs. To address this issue, a split system approach (SSA) based model is developed in this paper through an industrial case study. This new model enables maintenance personnel to predict the reliability of pipelines with different PM strategies and hence effectively assists them in making optimal PM decisions.     

Machine reliability and preventive maintenance planning for cellular manufacturing systems

The paper proposes a preventive maintenance (PM) planning model for the performance improvement of cellular manufacturing systems (CMS) in terms of machine reliability, and resource utilization. In a CMS, parts are processed by a group of interdependent machines, where machine reliability plays an important role in the performance improvement of the cell. Assuming that machine failure times follow a Weibull distribution, the proposed model determines a PM interval and a schedule for performing PM actions on each machine in the cell by minimizing the total maintenance cost and the overall probability of machine failures. The model uses a combined cost and reliability based approach, and optimizes maintenance costs by administering a group maintenance policy subject to a desirable machine reliability threshold. The study also proposes a CMS design model that integrates the above PM concepts into the design process. Illustrative examples are presented to demonstrate the applicability of the proposed approach.     

Modelling preventive maintenance for deteriorating repairable systems

This paper is concerned with practical methods for the analysisand modelling of data for repairable systems which are subjectto preventive maintenance (PM) and still have an increasingrate of occurrence of failures. Aspects of testing for trendand fitting a nonhomogeneous Poission process to data are discussedModels for scheduling preventive maintenance to minimize costor maximize availability are proposed. They show that the optimalPM cycle interval for these systems decreases with increasingequipment age. One-cycle and two-cycle finite-time-zone replacementmodels are also developed to decide the optimal time for replacingthe equipment in current use.     

PM2.5相关因素及分布演变的研究

以武汉市和西安市的PM2.5检测点数据及气象数据作为基础,建立了相关性分析模型,灰色关联度模型,混合回归模型,高斯烟羽模型,分期治理最优化模型等模型,通过定量与定性分析的方法,从PM2.5的相关因素、分布与演变、控制管理三个方面,对两个市的PM2.5进行了深入的研究与探讨.     

Optimal economic production quantity policy for imperfect process with imperfect repair and maintenance

This study integrates maintenance and production programs with the economic production quantity (EPQ) model for an imperfect process involving a deteriorating production system with increasing hazard rate: imperfect repair and rework upon failure (out of control state). The imperfect repair performs some restorations and restores the system to an operating state (in-control state), but leaves its failure until perfect preventive maintenance (PM) is performed. There are two types of PM, namely imperfect PM and perfect PM. The probability that perfect PM is performed depends on the number of imperfect maintenance operations performed since the last renewal cycle. Mathematical formulas are obtained for deriving the expected total cost. For the EPQ model, the optimum run time, which minimizes the total cost, is discussed. Various special cases are considered, including the maintenance learning effect. Finally, a numerical example is presented to illustrate the effects of PM, setup, breakdown and holding costs.     

A Recent Mathematical Development in Maintenance Theory

This paper introduces the mathematics of delay-time analysisand places it in context. Both the nature of and the scope fordelay-time models are indicated, and example prototype modelsdeveloped in the context of inspection policies. The essentialrole of subjective estimation is indicated, and the need toconsider revisions of both the prior delay-time distributionand the delay-time model highlighted. An unavoidable bias thatarises when estimating delay-time is discussed and the appropriatedistribution of biased estimates derived. A mechanism for correctingthis bias when estimating a delay-time distribution is proposed,based upon maximum-likelihood considerations. Finally, generalcomments are made concerning other developments and applicationsof the technique.     

空气中PM2.5问题的建模研究

是针对空气中PM2.5的相关因素分析、分布与演变及应急处理和空气质量控制管理的问题.首先,运用数理统计与分析的相关知识,建立PM2.5含量与5项指标间的相关性分析模型和多元线性回归方程模型,并采用SPSS软件和最小二乘法对其求解;然后,通过建立Shepard二维插值模型、多元线性回归方程模型以及偏微分方程模型研究了PM2.5时空分布、演变及预测评估的一般性规律;最后,引入效用函数建立了以满意度最大为目标的非线性规划模型和以投入总费用最少及PM2.5减排计划实施满意度最大为目标的多目标非线性规划模型,并结合LINGO软件求得最优解,给出了空气质量控制管理的治理计划.     

A case study of modelling preventive maintenance of a production plant using subjective data

In this paper we describe a subjective data based case study carried out at a company manufacturing copper products, which parallels a previously published objective data based study at the same plant. The purpose of this study is twofold. The first is to model the implementation of Planned Preventive Maintenance (PM) to an Extrusion Press using the delay time concept. The second is to test a method for estimation of model parameters from subjective data in the context of delay time modelling. The parameter values of the underlying fault arrival process and the delay time distribution were, unlike in a parallel objective study, initially estimated from subjective data obtained through a questionnaire survey. Since bias was present in the initial subjective estimate, a method of removing it was developed to improve the model fit. On the basis of the data analysis and delay time modelling, improved PM policy and procedures were proposed to increase the effectiveness and efficiency of PM.     

Impact of compressor failures on gas transmission network capability

National Grid, the gas operator in the United Kingdom, has experienced challenges in evaluating the capability of its gas transmission network to maintain function in the event of risks particularly to withstand the impact of compressor failures. We propose a mathematical programming model to support the operator in dealing with the problem. Several solution techniques are developed to solve the various versions of the problem efficiently. In the case of little data on compressor failure, an uncertainty theory is applied to solve this problem if the compressor failures are independent; while a robust optimisation technique is developed to solve it when they are not. Otherwise, when there are data on compressor failure, Monte Carlo simulation is applied to find the expected capability of the gas transmission network. Computational experiments, carried out on a case study at National Grid, demonstrate the efficiency of the proposed model and solution techniques. A further analysis is performed to determine the impact of compressor failures and suggest efficient maintenance policies for National Grid.