Periodic Preventive Maintenance Research Articles

Reliability Model of MMC Considering Periodic Preventive Maintenance

Periodic preventive maintenance (PPM) is a significant measure to ensure reliable operation of the modular multilevel converter (MMC), but to date has always been neglected in the reliability evaluation. A mathematical reliability model of MMC considering PPM is proposed in this paper. The model is derived rigorously by the reliability function and finally described by the indices equivalent failure rate and the forced outage rate. Two operation modes related to redundant submodules are considered and described by the standby model and k-out-of-n model, respectively. Employing this model, we can analyze the sensitivity of reliability to redundancy and maintenance interval and provide some reliability indices for planning, which is especially valuable for the offshore cases. The necessity of the considered PPM and the effectiveness of the proposed model are verified by case studies. A method to include PPM in reliability modeling is provided.

A bi-objective model to optimize periodic preventive maintenance strategy during warranty period by considering customer satisfaction

A proper warranty policy leads to a high-quality image of the product that can be used as a powerful competitive tool in the marketing of the product. In today’s market, in addition to the warranty, controlling product failure rate plays an important role in increasing the customer satisfaction level. Therefore, manufacturers to be forced to provide servicing strategies for the customers based on a combination of preventive maintenance and warranty. However, effective customer attraction using the mentioned servicing strategies might be costly. In this regard, the existing methods mainly design servicing strategies in a way that reduce manufacturer’s costs without considering the impact of customer dissatisfaction. In this paper, an approach based on goal programming and desirability function is developed to optimize the manufacturer’s costs and the customer satisfaction level, simultaneously. Due to the complexity of the problem, the proposed model is solved using the meta-heuristic algorithm of particle swarm optimization. Finally, to evaluate the performance of the developed mathematical programming, the results are analyzed on some numerical examples.

Joint optimization of maintenance, buffers and machines in manufacturing lines

ABSTRACTThis article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.

Flexible decision models for a two-dimensional warranty policy with periodic preventive maintenance

We study the consumer's and the manufacturer's optimal strategies for items sold with periodic preventive maintenance (PM) under a two-dimensional warranty policy. The cooperative and non-cooperative interactions between the manufacturer and the consumer are investigated for both the base warranty (BW) period and the extended warranty (EW) period. Under the BW, we propose a new upgrade model where the cost of PM is shared by two sides. In the EW period, consumers are assumed to have the option to either accept or reject PM at each time based on his utility. Two upgrade models are built and the revenue functions are analyzed. Profit maximization and cost minimization for both sides are analyzed by a game-theoretic approach. It is shown that the manufacturer's and the consumer's decisions are different, so the induced hazard rate functions are also different. In static and dynamic scenarios, we derive the equilibrium number of PM, the equilibrium value of acceptance probability and the equilibrium upgrade level for both the BW and the EW cases. Numerical examples are discussed to demonstrate the applicability of the methodologies. The results about the optimal solutions of the expected profit functions for the consumers and the manufacturer are provided.

Buffer allocation problem and preventive maintenance planning in non-homogenous unreliable production lines

In the current paper, a special class of buffer allocation problem (BAP)—buffer and preventive maintenance periods allocation problems—is studied. Three objective functions including the maximization of production rate, the minimization of total buffer size, and the total number of defective units are examined in order to solve the proposed model. In addition, an integrated simulation and meta-heuristic algorithm method is used for the same purpose. The proposed model and solution approach are applied to a case study, i.e., water heater production line in Polar Saunier Duval Company. Furthermore, the model and solutions will be assessed. In this integrated approach, genetic algorithm (GA) and particle swarm optimization (PSO) algorithms are used as meta-heuristic algorithms. These algorithms are compared in terms of quality of solutions, computational time, and relative deviation of final solutions in 0.95 confidence level. Numerical results reveal that GA is superior to PSO in terms of the quality of obtained solutions and relative deviation from optimal solutions (RPD). Furthermore, PSO converges in shorter computational time. Finally, based on outputs of the integrated solution approach, some important points about the parameters of the production line are presented. Computational results show that production rate is the most influential objective function in optimal solutions. However, drawing from the characteristics of production line, the number of defective products has less impact on optimal solutions in comparison with other objective functions. It is also concluded that there is a direct relationship between the number of defective products and time periods between preventive maintenance operations; in other words, if the time periods between preventive maintenance operations increase, the number of defective products grow.

Investigating reliability improvement of second-hand production equipment considering warranty and preventive maintenance strategies

A cost model for optimal reliability improvement of warranted second-hand production equipment is developed. The second-hand production equipment of age x is subjected to an upgrade action of a certain level u before it is sold with a Free Repair Warranty. We look at determining the optimal upgrade level when not performing and when performing periodic preventive maintenance (PM) during the warranty period. Two different PM strategies are considered: (a) periodic PM actions having the same efficiency level; (b) periodic multi-phase PM actions with a maintenance efficiency level which varies according to the phase. The proposed model aims at helping the dealer to find the optimal upgrade level to perform before selling the second-hand equipment, and to assess whether performing PM actions during the warranty period, according to a specific maintenance strategy, is worthwhile in terms of cost reduction. Numerical experimentations considering each PM scenario are performed in order to investigate how each PM strategy impacts the improvement level to be performed and the associated total expected cost. The obtained results showed that the expected total cost incurred by the dealer is governed by a sensitive trade-off between the warranty servicing cost and the costs associated with the reliability improvement, and with the PM performed during the warranty period. It is also found that the proposed new periodic multi-phase PM policy with an increasing maintenance efficiency level yields lower upgrade levels, inducing lower costs for the dealer.

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.

Joint quality control and preventive maintenance strategy: a unique taguchi approach

Production systems are exposed to machine failures and in most of them, buffer stocks are built between machines to guarantee that demands will be supplied during the interruptions because of breakdowns or periodic preventive maintenance (PM) actions. Also determination of the optimal maintenance policy in a production line is critical and it leads to provide a reliable system at lowest average total cost per time unit (comprising maintenance, quality and inventory costs). This is achieved by considering thresholds for nonconforming units and comparing with the proportion of produced nonconforming units and decides whether to undertake maintenance actions or not. The objective of this study is to integrating three aspects of quality, maintenance and production in operational units. In order to show the capability and effectiveness of the proposed approach, an operational unit consists of two machines is considered as a case study. The problem is modeled by simulation and the values of buffer stocks and nonconforming unit thresholds are determined to obtain the minimum average total cost using Taguchi method. Different rejection rates and buffer stocks values were given to the model as inputs in order to find the minimum average total cost which can satisfy the demand.

Optimization in Indonesia’s Bridge Preventive Maintenance Programme: A Proposal

This paper proposes optimisation method for the bridge preventive maintenance programme. The main focus is to predict the optimum preventive maintenance period of time - or, interval - so that bridges could be maintained better. A mathematical model has been developed in this study and is approached using the Markov Chain. Other optimisation techniques will also be discussed here and the reason for selecting the Markov Chain other than other current optimisation techniques is presented as well in a convincing argument. Development of software to help apply the devised mathematical model is in-progress and is clearly discussed.

Optimization of integrated preventive maintenance based on infinitesimal perturbation analysis

This paper considers jointly the production control and preventive maintenance problem in a manufacturing system. This system is composed of a single unreliable machine that produces one or two types of products with constant and different demand, a customer and a buffer between the machine and the customer. Machine failures are time-dependent, i.e. the machine can fail at any time, so a block-type preventive maintenance policy is proposed to increase the system life. This policy is coupled with a hedging point policy which controls the machine production speed. Between each element of the system, constant transportation delays are considered, i.e. a conveyor between the machine and its downstream buffer and a transport between the buffer and the customer. Then, a continuous-flow model is proposed to represent the overall dynamics of the system. This model allows us to take into account explicitly the transportation delays. The main objective of this work is to find the optimal preventive maintenance period by means of infinitesimal perturbation analysis technique as well as the optimal hedging point in order to minimize the expected average cost function. For this, we find the gradient estimators of the cost function from a theoretical sample path study. Then we integrate them in a simulation algorithm to find a numerical estimation of the solution. Simulation results highlight our theoretical results.

A preventive maintenance model for leased equipment subject to internal degradation and external shock damage

A periodic preventive maintenance modeling method is proposed for leased equipment with continuous internal degradation and stochastic external shock damage considered simultaneously, which can facilitate the equipment lessor to optimize the maintenance schedule for the same kind of equipment rented by different lessees. A novel interactive mechanism between the continuous internal degradation and the stochastic external shock damage is established on the hazard rate of the equipment with integrating the imperfect effect of maintenance. Two improvement factors are defined for the modeling of imperfect maintenance. The number of failures resulting from internal degradation and from external shocks are both mathematically deduced based on this interactive mechanism. The optimal preventive maintenance scheme is obtained by minimizing the cumulative maintenance cost throughout the lease period. Numerical example shows that the proposed preventive maintenance model not only can reflect the reliability status of the equipment but also can clearly distinguish between the impact from internal degradation and that from external shocks.

New stochastic models for preventive maintenance and maintenance optimization

This paper considers periodic preventive maintenance policies for a deteriorating repairable system. On each failure the system is repaired and, at the planned times, it is periodically maintained to improve its reliability performance. Most of periodic preventive maintenance (PM) models for repairable systems have been studied assuming that the failure process between two PMs follows the nonhomogeneous Poisson process (NHPP), implying the minimal repair on each failure. However, in this paper, we assume that the failure process between two PMs follows a new counting process which is a generalized version of the NHPP. We develop two types of PM models and study detailed properties of the optimal policies which minimize the long-run expected cost rates. Numerical examples are also provided.

JIT single machine scheduling problem with periodic preventive maintenance

This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, multi-objective particle swarm optimization (MOPSO) algorithm is implemented. Also, as well as MOPSO, two other optimization algorithms are used for comparing the results. Eventually, Taguchi method with metrics analysis is presented to tune the algorithms’ parameters and a multiple criterion decision making technique based on the technique for order of preference by similarity to ideal solution is applied to choose the best algorithm. Comparison results confirmed the supremacy of MOPSO to the other algorithms.

Joint reliability based design and periodic preventive maintenance policy for systems sold with warranty

Purpose – The authors consider a system which is a part of a complex equipment (e.g. aircraft, automobile, medical equipment, production machine, etc.), and which consists of N independent series subsystems. The purpose of this paper is to determine simultaneously the system design (reliability) and its preventive maintenance (PM) replacements periodicity which minimize the total average cost per time unit over the equipment useful life, taking into account a minimum required reliability level between consecutive replacements. Design/methodology/approach – The problem is tackled in the context of reliability-based design (RBD) considering at the same time the burn-in of components, the warranty commitment and the maintenance strategy to be adopted. A mathematical model is developed to express the total average cost per time unit to be minimized under a reliability constraint. The total average cost includes the cost of acquiring and assembling components, the burn-in of each component, preventive and corrective replacements performed during the warranty and post-warranty periods. A numerical procedure is proposed to solve the problem. Findings – For any given set of input data including components reliability, their cost and the costs of their preventive and corrective replacements, the system design (reliability) and the periodicity of preventive replacement during the post-warranty period is obtained such as the system’s total average cost per time unit is minimized. The obtained results clearly indicate that a decrease in the number of PM actions to be performed during the post-warranty period increases the number of components to be added at each subsystem at the design stage. Research limitations/implications – Given that the objective function (cost rate function) to be minimized is non-linear and involves several integer variables, it has not been possible to derive the optimal solution. A numerical procedure based on a heuristic approach has been proposed to solve the problem finding a nearly optimal solution for a given set of input data. Practical implications – This paper offers to manufacturers a comprehensive approach to look for the most economical combination of the reliability level to be given to their products at the design stage, on one hand, and the PM policy to be adopted, on the other hand, given the offered warranty and service for the products and reliability requirements during the life cycle. Originality/value – While the RBD problem has been largely treated, most of the published works have focussed on the development or the improvement of solving techniques used to find the optimal configuration. In this paper the authors provide a more comprehensive approach that considers simultaneously RBD, the burn-in and warranty periods, along with the maintenance policy to be adopted. The authors also consider the context of products whose component failures cannot be rectified through repair actions. They can only be fixed by replacement.

Optimal Long-Run Imperfect Maintenance With Asymptotic Virtual Age

We consider imperfect, periodic preventive maintenance of items with increasing failure rates, and minimal repairs in-between. The imperfect maintenance is performed in accordance with a Kijima II virtual age model. The obtained deterministic asymptotic value of virtual age defines the starting age of an item at the beginning of each cycle in the formulated and analyzed long-run optimization problem. A similar approach is used for the corresponding age-based preventive maintenance, where the starting age of the renewal-type cycle is already a random variable. Some meaningful examples are considered.

Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.

On the joint optimization of the periodic preventive maintenance and the spare parts inventory problem

In this paper, we propose a new model for the joint optimization of the periodic preventive maintenance and spare parts inventory problem. It consist to find an optimal maintenance schedule of M machine over a cyclic horizon composed of τ periods with taking into account the inventory management of spare parts. A computational experiment was made on a set of instances generated to show the efficiency and the limit of our model.

Scheduling Preventive Maintenance as a Function of an Imperfect Inspection Interval

This paper considers a system with periodic inspections and periodic preventive maintenance (PM) to detect and correct hidden failures that generate a penalty cost per unit time undetected. Imperfect periodic inspections (IPIs) occur at a chosen interval $t$ , and detect hidden failures with probability $p\in (0,1)$ . Both reactive maintenance (RM), performed when a hidden failure is detected by an IPI, and PM, performed at a chosen time $(n+1)t$ , renew the system. The objective is to determine the optimal frequency $t$ and quantity $n$ of imperfect inspections between PM such that the expected cost (which includes the costs of undetected failures, IPIs, PM, and RM) per unit time is minimized over an infinite horizon. We analytically establish conditions for the existence of a finite optimal $t$ for a given value of $n$ , and discuss the asymptotic behavior of the objective function for large $n$ and $t$ . These results are further exploited to describe convergence properties of a proposed approach for finding a globally optimal solution. Also, for the special case of a Weibull time-to-failure distribution, we derive conditions that guarantee the uniqueness of a locally optimal solution for a given value of $n$ .

Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning

Many studies on the integration optimization of production scheduling and preventive maintenance usually only consider one resource, i.e., machine. However, in real-world manufacturing, multiple dependent resources (e.g., human, tools and machines) are needed at the same time to avoid mismatch of multi-resource usage, which makes it highly important to jointly schedule production and maintenance tasks of multiple resources in order to improve system availability and system throughput simultaneously. In this paper, a multi-objective parallel machine scheduling problem with two kinds of resources (machines and moulds) and with flexible preventive maintenance activities on resources are investigated. The objective is to simultaneously minimize the makespan for the production aspect, the unavailability of the machine system, and the unavailability of the mould system for the maintenance aspect. A multi-objective integrated optimization method with NSGA-II adaption is proposed to solve this problem. The extensive computational experiments are conducted. The results show that the integrated optimization method of production scheduling and preventive maintenance outperforms the method with periodic preventive maintenance for this problem, in terms of multi-objective metrics, and the results also show the effects of different flexibilities of resources for job processing.

Optimal just-in-time buffer inventory for preventive maintenance with imperfect quality items

This paper deals with a just-in-time manufacturing environment which produces perfect quality items with defective items (a percentage of whole products) irrespective of the nature of the preventive maintenance. Since preventive maintenance is an essential part of production structure, performing of regular preventive maintenance results in a shutdown of the production unit for a period of time to enhance the condition of the production unit at an acceptable level. During the shutdown period just-in-time buffers for both the perfect and imperfect quality items are needed to continue the normal operation. The period of preventive maintenance depends on the nature and condition of the production unit which is random in nature. The percentage of imperfect quality item is also random. The optimal just-in-time buffer is determined to minimize the system running cost by considering the holding cost of perfect and imperfect quality items and shortage cost of perfect and imperfect quality items. A numerical example is presented to illustrate the development of the model and sensitivity of the model is analyzed.