The influence of condition-based maintenance on workforce planning and maintenance scheduling

Condition-based maintenance (CBM) is generally considered an attractive maintenance policy for a single component: it uses the operating condition of the component to predict a failure event and therefore tries to avoid any unplanned downtime and unnecessary maintenance activities. However, operations managers tend to be much more interested in optimising the performance of the entire asset-system, where the grouping of maintenance activities and the availability of maintenance workers may play a role. Therefore, this paper focuses on the impact of using either CBM or age-based replacement (ABR) in serial and parallel multi-component systems (1) without worker constraints, (2) with a single internal maintenance worker, and (3) with external maintenance workers with a significant response time. With an internal maintenance worker, the sequential execution of maintenance activities prevents efficiency gains in the serial configuration and here CBM performs better. Also in the parallel configurations, the efficiency under CBM is generally better than under ABR. However, with external maintenance workers, CBM is not able to group maintenance activities as well as ABR, which results in a lower efficiency in the serial configuration. CBM performs better than ABR with respect to total maintenance costs, while ABR results in a smoother maintenance plan.     

Condition based maintenance optimization for multi-component systems using proportional hazards model

The objective of condition based maintenance (CBM) is typically to determine an optimal maintenance policy to minimize the overall maintenance cost based on condition monitoring information. The existing work reported in the literature only focuses on determining the optimal CBM policy for a single unit. In this paper, we investigate CBM of multi-component systems, where economic dependency exists among different components subject to condition monitoring. The fixed preventive replacement cost, such as sending a maintenance team to the site, is incurred once a preventive replacement is performed on one component. As a result, it would be more economical to preventively replace multiple components at the same time. In this work, we propose a multi-component system CBM policy based on proportional hazards model (PHM). The cost evaluation of such a CBM policy becomes much more complex when we extend the PHM based CBM policy from a single unit to a multi-component system. A numerical algorithm is developed in this paper for the exact cost evaluation of the PHM based multi-component CBM policy. Examples using real-world condition monitoring data are provided to demonstrate the proposed methods.     

基于机会维修策略的预防性维修优化模型研究

为了利用系统内故障部件维修的机会,考虑部件之间在维修方面存在的相关性情况,提出了将机会维修信息融入系统预防性维修的优化模型．该模型在比较按机会预防性维修和按计划预防性维修期望效益的基础上,以系统内故障时机为优化变量、部件维修费用率最小化为目标进行系统预防性维修优化,使确定部件在不同的故障来源时机可提前预防性维修的阈值．最后通过算例,表明不同故障的机会维修阈值对部件预防性维修效果的影响差异,为系统预防性维修决策提供有力的支持．     

Optimizing preventive maintenance for mechanical components using genetic algorithms

This paper presents periodic preventive maintenance (PM) of a system with deteriorated components. Two activities, simple preventive maintenance and preventive replacement, are simultaneously considered to arrange the PM schedule of a system. A simple PM is to recover the degraded component to some level of the original condition according to an improvement factor which is determined by a quantitative assessment process. A preventive replacement is to restore the aged component by a new one. The degraded behavior of components is modeled by a dynamic reliability equation, and the effect of PM activities to reliability and failure rate of components is formulated based on age reduction model. While scheduling the PM policy, the PM components within a system are first identified. The maintenance cost and the extended life of the system under any activities-combination, which represents what kind of activities taken for these chosen components, are analyzed for evaluating the unit-cost life of the system. The optimal activities-combination at each PM stage is decided by using genetic algorithm in maximizing the system unit-cost life. Repeatedly, the PM scheduling is progressed to the next stage until the system's unit-cost life is less than its discarded life. Appropriately a mechatronic system is used as an example to demonstrate the proposed algorithm.     

Optimal replacement policy and inspection interval for condition-based maintenance

In condition-based maintenance (CBM), replacement policy is often defined as a rule for replacement or leaving an item (or a system) in operation until the next inspection, depending on monitoring results. The criterion for determining the optimal threshold for replacement, also known as optimal control limit, is to minimise the average maintenance costs per unit time due to preventive and failure replacements over a long time horizon. On the one hand, higher frequency of inspections provides more information about the condition of the system and, thus, maintenance actions are performed more effectively, namely, unnecessary preventive replacements are avoided and the number of replacements due to failure is reduced. Consequently, the cost associated to failure and preventive replacements are decreased. On the other hand, in many real cases, inspections require labour, specific test devices, and sometimes suspension of the operations and, thus, as the number of inspections increase, the inspection cost also increases. In this paper, preventive and failure replacement costs as well as inspection cost are taken into account to determine the optimal control limit and the optimal inspection interval simultaneously. The proposed approach is illustrated through a numerical example.     

An integrated preventive maintenance and production planning model with sequence‐dependent setup costs and times

This paper considers the integration of preventive maintenance and tactical production planning in a multiproduct production system, where setup costs and times are sequence dependent. A set of products needs to be produced in lots during a finite planning horizon, where preventive maintenance is conducted periodically at the end of some production periods and corrective maintenance is always performed when there is a failure. The system downtime—as caused by maintenance and setup—affects the system's available production capacity. We use a sequence‐oriented method to search for the optimal setup sequence and develop some steps to prune the searching tree. Our objective is to find the optimal preventive maintenance interval, production lot size, and production sequence in every period, by minimizing the sum of maintenance, production, inventory, and setup costs within the planning horizon. Numerical examples are presented to illustrate our model.     

Condition-based inspection scheme for condition-based maintenance

In condition-based maintenance (CBM) with periodic inspection, the item is preventively replaced if failure risk, which is calculated based on the information obtained from inspection, exceeds a pre-determined threshold. The determination of optimal replacement threshold is often based on minimisation of long-run average maintenance costs per unit time due to preventive and failure replacements. It is assumed that inspections are performed at equal time intervals and that the corresponding cost is negligible. However, in many practical situations where CBM is implemented, e.g. manufacturing processes, inspections require labours, specific test devices, and sometimes suspension of operations. Thus, when inspection cost is considerable, it is reasonable to inspect less frequently during the time the item is in healthier states, and, more frequently as time passes and/or the item degrades, namely, a condition-based inspection scheme. This paper proposes a novel two-phase approach for determination of replacement threshold and a condition-based inspection scheme for CBM. First, it takes into account failure and preventive replacement costs to determine the optimal replacement threshold assuming that inspections are performed at equal time intervals with no cost. This assumption is, then, relaxed and its consequences on total average cost are evaluated using a proposed iterative procedure to obtain a cost-effective condition-based inspection scheme. The proposed approach can be utilised in many CBM applications. For the sake of simplicity of presentation, the approach is illustrated through a simplified case study already reported by some researchers referenced in the paper.     

Preventive maintenance of a batch production system under time-varying operational condition

This paper proposes a dynamic opportunistic preventive maintenance (PM) strategy for a production system with a time-varying batch production pattern. The operation of such a system is generic in that the operational condition (OC) varies from batch to batch and the information about the next batch can be confirmed only upon the completion of the current batch. To accommodate time-varying OC, a modified imperfect maintenance model is developed to optimise the performance of maintenance actions that can only be performed at batch-shift points. The first study presents a PM policy for a single machine with short-term production plans. Then, a multi-machine system is studied with a goal of developing an optimum dynamic opportunistic PM strategy for a group of machines at batch-shift points. Numerical examples are proceeded to illustrate the proposed maintenance strategy in practice. The result reveals that more cost will be incurred if OC is ignored. Moreover, the proposed opportunistic PM strategy achieves the lowest total cost comparing with other strategies as the system downtime cost and maintenance cost has been jointly minimised.     

Optimal age-based inspection scheme for condition-based maintenance using A* search algorithm

In condition-based maintenance (CBM) with periodic inspection, the system is preventively replaced if failure risk, which is calculated based on the information obtained from inspection, exceeds a pre-determined threshold. The determination of optimal replacement threshold is often based on the minimisation of average maintenance costs per unit time due to preventive and failure replacements over a long time horizon. It is often assumed that inspections are performed at equal time intervals with no cost. However, in practice, inspections require labour, specific test devices, and sometimes suspension of operations and, thus, it is reasonable to inspect less frequently during the time the system is in its early age and/or in a healthier state and to perform inspections more frequently as time passes and/or as the system degrades. In other words, an age-based inspection scheme.

This paper proposes a novel two-phase approach for the determination of an optimal replacement threshold and an optimal age-based inspection scheme for CBM such that the total long-run average costs of replacements and inspections are minimised. First, it takes into account failure and preventive replacement costs to determine the optimal replacement threshold assuming that inspections are performed at equal time intervals with no cost. This assumption is, subsequently, relaxed and its consequences on total average cost are evaluated using a proposed iterative procedure based on A* search algorithm to obtain the optimal age-based inspection scheme. The proposed approach is illustrated through a numerical example.     

考虑预防维修时间及相关性的多部件系统维护策略

针对动车组关键系统维修过程中涉及部件数量多、维修时间长、维修费用高的特点，提出了预防维修时间、故障相关性、经济相关性3重因素影响下的多部件系统机会维护策略。首先对多部件建立部件故障率模型。在此基础上，考虑预防维修时间，将部件层预防维修成本分为独立时间成本和依赖时间成本，对经济相关性进行建模，再以系统维修费用率最小为优化目标建立系统层维护模型，并应用遗传算法求解。最后通过算例表明，相较于考虑单一因素的维修策略，所提维修策略可降低系统维修成本10%及以上，验证了考虑维修时间和部件联合相关性的必要性和有效性。     

A variable neighbourhood search for integrated production and preventive maintenance planning in multi-state systems

This paper presents a variable neighbourhood search (VNS) to the integrated production and maintenance planning problem in multi-state systems. VNS is one of the most recent meta-heuristics used for problem solving in which a systematic change of neighbourhood within a local search is carried out. In the studied problem, production and maintenance decisions are co-ordinated, so that the total expected cost is minimised. We are given a set of products that must be produced in lots on a multi-state production system during a specified finite planning horizon. Planned preventive maintenance and unplanned corrective maintenance can be performed on each component of the multi-state system. The maintenance policy suggests cyclical preventive replacements of components, and a minimal repair on failed components. The objective is to determine an integrated lot-sizing and preventive maintenance strategy of the system that will minimise the sum of preventive and corrective maintenance costs, setup costs, holding costs, backorder costs and production costs, while satisfying the demand for all products over the entire horizon. We model the production system as a multi-state system with binary-state components. The formulated problem can be solved by comparing the results of several multi-product capacitated lot-sizing problems. The proposed VNS deals with the preventive maintenance selection task. Results on test instances show that the VNS method provides a competitive solution quality at economically computational expense in comparison with genetic algorithms.     

Maintenance Scheduling for Multicomponent Equipment

The problem investigated is the scheduling of preventive and opportunistic maintenance or replacement for equipment with more than one component under conditions of positive failure costs and increasing failure rates for all components. Dynamic programming models are presented for determining optimal policies for two and three component equipment. The resulting optimal policies are compared to commonly used policies and the often substantial diseconomies of such policies are illustrated. The (n,N) policies are found to be near-optimal under a wide range of opportunistic/preventive maintenance costs and failure rates for two and three component equipment. An approach for developing simple (n,N) policies for many component equipment is suggested.     

Opportunistic maintenance of multi‐equipment system: a case study

A case study on preventive maintenance (PM) of a multi‐equipment system is presented in this paper. Each equipment of the system consists of many components/subsystems connected in series. Because of the series structure, opportunistic maintenance (OM) policies are more effective for the components of the equipment. A new OM policy based on the classification of opportunities has been proposed. Various OM policies have been evaluated using simulation modeling, and the new policy has been found to be more effective than the existing OM policies. The impact of this policy on the overall system has also been simulated. Copyright © 2000 John Wiley & Sons, Ltd.     

Optimization of maintenance policy using the proportional hazard model

The evolution of system reliability depends on its structure as well as on the evolution of its components reliability. The latter is a function of component age during a system's operating life. Component aging is strongly affected by maintenance activities performed on the system. In this work, we consider two categories of maintenance activities: corrective maintenance (CM) and preventive maintenance (PM). Maintenance actions are characterized by their ability to reduce this age. PM consists of actions applied on components while they are operating, whereas CM actions occur when the component breaks down. In this paper, we expound a new method to integrate the effect of CM while planning for the PM policy. The proportional hazard function was used as a modeling tool for that purpose. Interesting results were obtained when comparison between policies that take into consideration the CM effect and those that do not is established.     

A study of availability-centered preventive maintenance for multi-component systems

This paper studies preventive maintenance (PM) in simultaneously considering three actions, mechanical service, repair and replacement for a multi-components system based on availability. Mechanical service denotes the activities including lubricating, cleaning, checking and adjusting, etc. which is set to alleviate strength degradation. Repair is defined on that not only slow down the degraded velocity but also restore the degraded strength partly. Replacement is settled to recover a component to its original condition. According to the definitions, the degradation of components is analyzed from its failure mechanisms and the improvements of various actions to it in reliability were measured by using two improved factors. Following the proposed model of reliability, the mean-up and mean-down times of each component are also investigated and the replacement intervals of components are determined based on availability maximization. Here, the minimum one among the intervals is chosen as the PM interval of system for programming the periodical PM policy. The selection of action for the components on every PM stage is decided by maximizing system benefit in maintenance. Repeatedly, the scheduling is progressed step by step and is terminated until the system extended life reaching to its expected life. The complete schedule provides the information, the actions adopted for the components, the availability and the total cost of system on each stage. Validly, a multi-components system is used as an example to describe the proposed algorithm.     

A Joint Reliability and Imperfect Opportunistic Maintenance Optimization for a Multi‐State Weighted k‐out‐of‐n System Considering Economic Dependence and Periodic Inspection

This paper formulates a model to simultaneously optimize the redundancy and imperfect opportunistic maintenance of a multi‐state weighted k‐out‐of‐n system. Different from existing approaches that consider binary or multi‐state elements, our approach considers modular redundancy in which each module/subsystem is composed of several multi‐state components in series. The status of each component is considered to degrade with use. Therefore, a new condition‐based opportunistic maintenance approach using three different thresholds for a component health state is developed. The objective is to determine 1) the minimal‐cost of k‐out‐of‐n system structure, 2) optimal imperfect opportunistic maintenance strategy, 3) optimal maintenance capacity, and 4) optimal inspection interval subject to an availability constraint. System availability is defined as the ability to satisfy consumer demand. Based on the three‐phase approach, a simulation procedure is used to evaluate the expected multi‐state system availability and life cycle costs. Also, a multi‐seed Tabu search heuristic algorithm with a proper neighborhood generation mechanism is proposed to solve the formulated problem. An application to the optimal design of a wind farm is provided to illustrate the proposed approach. Sensitivity analysis is conducted to discuss the influence of the different parameters of the simulation model. Copyright © 2017 John Wiley & Sons, Ltd.     

The use of lifetime functions in the optimization of interventions on existing bridges considering maintenance and failure costs

In the last decade, it became clear that life-cycle cost analysis of existing civil infrastructure must be used to optimally manage the growing number of aging and deteriorating structures. The uncertainties associated with deteriorating structures require the use of probabilistic methods to properly evaluate their lifetime performance. In this paper, the deterioration and the effect of maintenance actions are analyzed considering the performance of existing structures characterized by lifetime functions. These functions allow, in a simple manner, the consideration of the effect of aging on the decrease of the probability of survival of a structure, as well as the effect of maintenance actions. Models for the effects of proactive and reactive preventive maintenance, and essential maintenance actions are presented. Since the probability of failure is different from zero during the entire service life of a deteriorating structure and depends strongly on the maintenance strategy, the cost of failure is included in this analysis. The failure of one component in a structure does not usually lead to failure of the structure and, as a result, the safety of existing structures must be analyzed using a system reliability framework. The optimization consists of minimizing the sum of the cumulative maintenance and expected failure cost during the prescribed time horizon. Two examples of application of the proposed methodology are presented. In the first example, the sum of the maintenance and failure costs of a bridge in Colorado is minimized considering essential maintenance only and a fixed minimum acceptable probability of failure. In the second example, the expected lifetime cost, including maintenance and expected failure costs, of a multi-girder bridge is minimized considering reactive preventive maintenance actions.     

Optimum policies for a system with general imperfect maintenance

This study considers periodic preventive maintenance policies, which maximizes the availability of a repairable system with major repair at failure. Three types of preventive maintenance are performed, namely: imperfect preventive maintenance (IPM), perfect preventive maintenance (PPM) and failed preventive maintenance (FPM). The probability that preventive maintenance is perfect depends on the number of imperfect maintenances conducted since the previous renewal cycle, and the probability that preventive maintenance remains imperfect is not increasing. The optimum preventive maintenance time that maximizes availability is derived. Various special cases are considered. A numerical example is given.     

Optimal maintenance plan for two-level assembly system and risk study of machine failure

This paper deals with the optimisation of two-levels assembly system planning. This system is composed of a single machine, inventories at levels 1 and 2 for stock keeping components to assembly and final assembled product. It assumed that the machine processes all assembly operations and is subject to random failure. A mathematical model is developed to incorporate a supply planning for two-level assembly systems under stochastic lead times and breakdowns machine. A preventive maintenance plan is carried out to reduce the frequency of the corrective maintenance actions. This work has double goals. The first one is to find the optimal order release dates for the different components at level 2 and the optimal preventive maintenance plan. The second one is to quantify the risk due to machine failures which have an impact on the lead-time of the finished product. To consider the maintenance actions, preventive maintenance actions are perfectly performed to restore the machine to state “as good as new”, minimal repair is considered at failure. The model minimises the total cost, which is the sum of inventory holding cost for components at levels 1 and 2, backlogging and inventory holding cost for the finished products and maintenance costs. To illustrate the effectiveness of the proposed model, different meta-heuristics are applied; the genetic algorithm shows the most suited to our analytical model, the optimal release date founded by this algorithm allows finding the optimal preventive maintenance plan. The obtained optimal maintenance planning is used in the risk assessment in order to find the threshold repair period that avoids lost profit.     

A genetic algorithm approach for production scheduling with mould maintenance consideration

The traditional approach for maintenance scheduling concerns single-resource (machine) maintenance during production which may not be sufficient to improve production system reliability as a whole. Besides, in the literature many researchers schedule maintenance activities periodically with fixed maintenance duration. However, in a real manufacturing system maintenance activities can be executed earlier and the maintenance duration will become shorter since less time and effort are required. A practical example is that in a plastic production system, the proportion of machine-related downtime is even lower than mould-related downtime. The planned production operations are usually interrupted seriously because of the mismatch among the maintenance periods between injection machine and mould. In this connection, this paper proposes to jointly schedule production and maintenance tasks of multi-resources in order to improve production system reliability by reducing the mismatch among various processes. To integrate machine and mould maintenance tasks in production, this paper attempts to model the production scheduling with mould scheduling (PS-MS) problem with time-dependent deteriorating maintenance schemes. The objective of this paper is to propose a genetic algorithm approach to schedule maintenance tasks jointly with production jobs for the PS-MS problem, so as to minimise the makespan of production jobs.