Using proportional-intensities models to schedule preventive-maintenance intervals

This paper is concerned with developing realistic models todetermine preventive-maintenance (PM) schedules for complexsystems when data have been collected on failure times and PMinterventions along with down times and man-hours expended oneach. The aim is to develop procedures for analysing such datain order to identify relevant cost-availability models for optimallyscheduling PM, while allowing for system deterioration or improvement.We propose the use of a proportional-intensities model and considerthe addition of Bayesian methods to incorporate prior knowledgeabout regression coefficients. The paper discusses further aspectsof this approach, which include extending the model to covertime-dependent explanatory variables, and presents an applicationof the proportional-intensities model to actual reliabilitydata from major industrial plants.     

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.     

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.     

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.     

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.     

多部件系统故障预防工作的组合优化

研究了实践中常见的四种预防工作组合类型,即定时维修、功能检测、使用检查及检查与定时维修的组合策略,以单位时间维修费用最小为目标建立了多部件故障预防工作的组合优化模型.在此基础上,提出了两个故障预防工作的组合优化算法,并给出了计算示例以说明模型和算法的性能.     

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.     

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     

Economic production lot-sizing for an unreliable machine under imperfect age-based maintenance policy

This paper is concerned with the joint determination of both economic production quantity and preventive maintenance (PM) schedules under the realistic assumption that the production facility is subject to random failure and the maintenance is imperfect. The manufacturing system is assumed to deteriorate while in operation, with an increasing failure rate. The system undergoes PM either upon failure or after having reached a predetermined age, whichever of them occurs first. As is often the case in real manufacturing applications, maintenance activities are imperfect and unable to restore the system to its original healthy state. In this work, we propose a model that could be used to determine the optimal number of production runs and the sequence of PM schedules that minimizes the long-term average cost. Some useful properties of the cost function are developed to characterize the optimal policy. An algorithm is also proposed to find the optimal solutions to the problem at hand. Numerical results are provided to illustrate both the use of the algorithm in the study of the optimal cost function and the latter’s sensitivity to different changes in cost factors.     

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.     

The impact of inspection errors,imperfect maintenance and minimal repairs on an imperfect production system

This paper develops an integrated model of production lot-sizing, maintenance and quality for considering the possibilities of inspection errors, preventive maintenance (PM) errors and minimal repairs for an imperfect production system with increasing hazard rates. In this study, a PM activity is imperfect in that a production system cannot be recovered as good as new and might cause the production system to shift to the out-of-control state with a certain probability. Numerical analyses are used to simulate the effect of changes in various parameters on the optimal solution for which the time that the process remains in the in-control state is assumed to follow a Weibull distribution. In addition, we investigate the effects of inspection errors and PM errors on the minimum total cost of the optimal inspection interval, inspection frequency and production quantity.     

Model for imperfect age-based preventive maintenance with age reduction

The effect of ageing on the deterioration rate of most repairable systems cannot be ignored. Preventive maintenance (PM) is performed in the hope of restoring fully the performance of these systems. However, in most practical cases, PM activities will be only able to restore part of the performance. Bridging the gap between theory and practice in this area requires realistic modelling of the effect of PM activities on the failure characteristics of maintainable systems. Several sequential PM models have been developed for predetermined PM interval policies but much less effort has been devoted to age-based ones. The purpose of this paper is to develop an age-based model for imperfect PM. The proposed model incorporates adjustment factor in the effective age of the system. The system undergoes PM either at failure or after a predetermined time interval whichever of them occurs first. After a certain number of such PMs, the system is replaced. The problem is to determine both the optimal number of PMs and the optimal PM's schedule that minimize the total long-term expected cost rate. Model analysis relating to the existence and uniqueness of the optimal solutions is provided. Numerical examples are presented to study the sensitivity of the model to different cost function's factors and to illustrate the use of the algorithm.     

Integrated production and quality model under various preventive maintenance policies

In this paper, we investigate the effect of various preventive maintenance policies on the joint optimisation of the economic production quantity (EPQ) and the economic design of control chart. This has been done for a deteriorating process where the in-control period follows a general probability distribution with increasing hazard rate. In the proposed model, preventive maintenance (PM) activities reduce the shift rate of the system to the out-of-control state proportional to the PM level. For each policy, the model determines the EPQ, the optimal design of the control chart and the optimal preventive maintenance level. The effects of the three PM policies on EPQ and quality costs are illustrated using an example of a Weibull shock model with an increasing hazard rate.     

The delay-time modelling of preventive maintenance of plant given limited PM data and Selective repair at PM

This paper presents a maintenance-modelling case study of aplant manufacturing brake linings. A delay–time modelis developed and applied to model and optimize preventive maintenance(PM). A key subsystem in the plant is used to illustrate themodelling process and management reaction. Defects identifiedat PM may not all be removed. This incomplete response to PMis a feature which has not been modelled before. The parametervalues of the delay-time process are estimated from objectivedata from maintenance records of failures, using the methodof maximum likelihood. This is aided by a theorem extendingresults on the NHPP arival rate of failures in a perfect-inspectioncase to the non-perfect-inspection case. Problems of parameterestimation given inadequate data collected at PMs are discussed,and the necessity to augment objective data with subjectiveassessments highlighted. Based upon the estimated model parametersand delay-time distribution, an inspection model is constructedto describe the relationship between the total unit downtimeand the PM interval. The response of management is discussed.     

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.     

Modelling in the absence of data: a case study of fleet maintenance in a developing country

Adequate and relevant objective data for modelling maintenance decision problems are often incomplete or not readily accessible. This is particularly true in developing countries. In this paper the experience gained between 1991–95 in conducting a maintenance study of an inter-city express bus fleet in a developing country is presented. The lack of available maintenance records and operating data rendered the study the most data-starved maintenance modelling exercise the authors have met before or since. The study required the use of subjective methods to both define the problem and to estimate parameters, and the application of recently developed concepts in maintenance modelling along with snapshot analysis and delay time modelling. This imposed a structured approach to problem recognition and problem solution. The study contributed both directly and indirectly to a change in work culture and to a reduction in bus breakdown rate. The company was re-visited 5?years later specifically to seek evidence of lasting impact. Some evidence existed and is reported in the paper.     

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.     

Optimum policy for a production system with major repair and preventive maintenance

This study applies periodic preventive maintenance (PM) to a repairable production system with major repairs conducted after a failure. This study considers failed PM due to maintenance workers incorrectly performing PM and damages occurring after PM. Therefore, three PM types are considered: imperfect PM, perfect PM and failed PM. Imperfect PM has the same failure rate as that before PM, whereas perfect PM makes restores the system perfectly. Failed PM results in system deterioration and major repairs are required. The probability that PM is perfect or failed depends on the number of imperfect maintenance operations conducted since the previous renewal cycle. Mathematical formulas for expected total production cost per unit time are generated. Optimum PM time that minimizes cost is derived. Various special cases are considered, including the maintenance learning effect. A numerical example is given.     

Optimizing Preventive Maintenance Models

We deal with the problem of scheduling preventive maintenance (PM) for a system so that, over its operating life, we minimize a performance function which reflects repair and replacement costs as well as the costs of the PM itself. It is assumed that a hazard rate model is known which predicts the frequency of system failure as a function of age. It is also assumed that each PM produces a step reduction in the effective age of the system. We consider some variations and extensions of a PM scheduling approach proposed by Lin et al. [6]. In particular we consider numerical algorithms which may be more appropriate for hazard rate models which are less simple than those used in [6] and we introduce some constraints into the problem in order to avoid the possibility of spurious solutions. We also discuss the use of automatic differentiation (AD) as a convenient tool for computing the gradients and Hessians that are needed by numerical optimization methods. The main contribution of the paper is a new problem formulation which allows the optimal number of occurrences of PM to be determined along with their optimal timings. This formulation involves the global minimization of a non-smooth performance function. In our numerical tests this is done via the algorithm DIRECT proposed by Jones et al. [19]. We show results for a number of examples, involving different hazard rate models, to give an indication of how PM schedules can vary in response to changes in relative costs of maintenance, repair and replacement. Part of this work was carried out while the first author was a Visiting Professor in the Department of Mechanical Engineering at the University of Alberta in December 2003.