Optimal maintenance of a critical infrastructure subject to dependent recurrent hazards

Joint optimization of [formula omitted] control chart and preventive maintenance policies: A discrete-time Markov chain approach

Periodic maintenance of equipment is essential for its optimum performance, thereby enabling production efficiency. In the past, studies on preventive maintenance of automated manufacturing systems (AMS) determined the optimal preventive maintenance policy under different performance indexes. Generally, most hypotheses indicate that equipment reliability can be restored to 1.0 through preventive and corrective maintenance. However, in practical application, the implementation of preventive maintenance results in partial deterioration of equipment; moreover, the reliability of equipment cannot be restored to as‐good‐as‐new. In addition, the greater the complexity of connections of the equipment, the greater is the difficulty in determining the timing for preventive maintenance. On account of these characteristics, generalized stochastic Petri nets (GSPN) are well‐suited for the implementation of preventive maintenance. Therefore, this paper applies GSPN for deciding the optimal maintenance policy and constructing models for different levels of maintenance and renewal for an AMS with a serial‐parallel layout. As a result of the application of GSPN, the following optimal maintenance policy for an AMS was obtained in this study: Preventive maintenance conducted at intervals of every 240 hours will reduce cost by 46% as opposed to the practice of replacing defective parts when necessary. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

Age-dependent production planning and maintenance strategies in unreliable manufacturing systems with lost sale

Optimal preventive maintenance policy for leased equipment using failure rate reduction

Purpose – The purpose of this paper is to study the evolution of a system stationary availability and determine the optimal preventive maintenance period, which maximises it in a context where preventive and corrective maintenance actions are imperfect and have non‐negligible durations.Design/methodology/approach – The quasi‐renewal process approach and a (p, q) rule are respectively used to model corrective and preventive maintenance. Considering the durations of the preventive and corrective maintenance actions as well as their respective efficiency extents, a mathematical model and a numerical algorithm are developed in order to compute the system stationary availability.Findings – It has been proven that for any given situation regarding the system, the repair and preventive maintenance efficiency extents, and the downtime durations for preventive and corrective maintenance, there is necessarily a finite optimal period T* of preventive maintenance which maximises the system stationary availability. A ...

Preventive maintenance (PM) plays a very important role in production process. In this paper, the optimization PM planning in finite time horizon for a single machine with minimal repair at failures is discussed. Not only two conflicting objectives of maintenance cost and maintenance time but also two constraints of the reliability and availability of the machine are simultaneously considered in the optimization problem. Three types of PM actions including replacement, repair and mechanical service are considered. In order to make the suiTABLE PM planning, flexible period PM strategy was proposed. In this strategy, the intervals between two coterminous PM actions were with a flexible range of choice. The multi-objective genetic algorithm is used to solve the flexible period PM optimization problem. Numerical case study shows that comparing with periodic PM strategy, flexible period PM strategy can save more maintenance cost and maintenance time.

PurposeThe purpose of this paper is to develop a model that incorporates the effect of rejection cost on optimal maintenance planning decisions. Such a model will help in further modelling the interrelationships between preventive maintenance and quality control policy.Design/methodology/approachIn this paper a model is developed for obtaining optimal preventive maintenance interval based on block replacement policy to incorporate the effect of rejection cost. An illustrative example is presented to compare economic performance of the proposed model (M2) and the conventional model (M1). Model M1 stands for optimal preventive maintenance interval without considering the rejection cost and model M2 stands for optimal preventive maintenance interval considering the rejection cost. The comparison is done for different production rates, costs of rejection and cost of lost production. The impact of control chart parameters on preventive maintenance decision is also studied.FindingsIn this paper it is found that model M2 gives better results as compared to model M1. The improvements are more significant at higher production rate, lower cost of lost production and higher rejection cost. The impact of control chart parameters on preventive maintenance planning decision becomes significant as the cost of rejection increases.Research limitations/implicationsConventionally only the down time cost and the cost of repair/replacement are considered for optimal maintenance interval determination. However in the case of machine tools, failure may not always bring the system immediately under complete breakdown but may lead to the functioning of system with degraded performance like process shift from in‐control state to out‐of‐control state. It results into poor quality and thus may lead to higher rejection cost. The cost of rejections may be significantly high in some production systems and, if not incorporated properly during maintenance planning decision may adversely affect the effectiveness of the maintenance planning. Hence the approach presented in this paper gives a better way of maintenance planning. Though the work presented here is illustrated through a simple example considering a single component operating as a part of machine, the approach can be extended to multi‐component system.Originality/valueThe outcome is of significant value as it opens up a new perspective into the development of integrated model for maintenance planning and quality control decisions for reducing the operating costs associated with the manufacturing processes.

In this research, we are concerned with the modeling of optimal maintenance actions in multi-state systems. Most of the imperfect maintenance models that have been investigated in literature use either imperfect preventive maintenance actions or imperfect corrective maintenance actions. In this paper we consider a model with both imperfect preventive and imperfect corrective maintenance actions. A sequential failure limit preventive maintenance (PM) policy with infinite planning horizon and with imperfect preventive and imperfect corrective maintenance actions is used to formulate a cost optimization problem. Different cost functions for PM actions, as well as several discrete lifetime distributions are introduced. The solution of the cost optimization problem is illustrated by an example.

Preventive maintenance (PM) can slow the deterioration process of a repairable system and restore the system to a younger age or state. Many researchers focus on studying PM models for the cases of age or failure rate reduction and developing optimal PM policies. However, the PM actions, such as cleaning, adjustment, alignment, and lubrication work, may not always reduce system's age or failure rate. Instead, it may only reduce the degradation rate of the system to a certain level. Furthermore, most of the existing optimal PM policies result in very low reliability at the time of preventive replacement (PR). In practice, however, high reliability is usually required for a system to avoid failures being occurred. This paper is to develop an optimal periodic PM policy over an infinite time span by minimizing the expected cost rate with the consideration of reliability limit (R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> ,) for Weibull-failure-time systems with degradation rate reduction after each PM. The proposed optimal periodic PM policy consists of two PM models. One model has fully periodic PM time-interval for every preventive replacement cycle; the other model has partially periodic PM time-interval where the time interval between the final PM and the PR is not a constant. For specified reliability limit, the proper model is chosen by using the algorithm provided in this paper. Examples are demonstrated and the sensitivity analysis is also presented for the proposed PM models.

This paper deals with imperfect preventive maintenance (PM) optimisation problem. The system to be maintained is typically a production system assumed to be continuously monitored and subject to stochastic degradation. To assess such degradation, the proposed maintenance model takes into account both corrective maintenance (CM) and PM. The system undergoes PM whenever its reliability reaches an appropriate value, while CM is performed at system failure. After a given number of maintenance actions, the system is preventively replaced by a new one. Both CM as well as PM are considered imperfect, i.e. they bring the system to an operating state which lies between two extreme states, namely the as bad as old state and as good as new state. The imperfect effect of CM and PM is modelled on the basis of the hybrid hazard rate model. The objective of the proposed PM optimisation model consists on finding the optimal reliability threshold together with the optimal number of PM actions to maximise the average availability of the system. A mathematical model is then proposed. To solve this problem an algorithm is provided. A numerical example is presented to illustrate the proposed maintenance optimisation model.

Optimal maintenance strategy under renewable warranty with repair time threshold

A problem of optimal machine maintenance policy is studied. The value of the machine's output is independent of age, but the natural probability of machine failure increases with age. Preventive maintenance can be applied to reduce the probability of machine failure (but a failed machine can only be junked). The machine may be sold at any time providing it is still effective. The problem is to select an optimal preventive maintenance policy for the period of ownership and a planned sale date at which the machine will be sold providing it has not yet failed. The optimal policy calls for a continuous nonincreasing maintenance rate, although not necessarily a decreasing rate of maintenance expenditure. The necessary condition for machine age at sale is critical in the determination of optimal maintenance policy. The necessary conditions for an optimal maintenance policy are shown to be sufficient as well. The novelty of the approach lies in the explicit distinction between the machine's “natural” failure rate and the actual failure rate resulting from selection of preventive maintenance policy.

A deterministic approach to a single‐machine maintenance problem is presented which is solved forward in real‐time. The importance of real‐time solutions is particularly evident in new manufacturing environments, where fast decisions reflecting the most current information are vital to obtain high utilization of expensive equipment and to avoid bottlenecks in the production process. A sequential decision‐making process in introduced. First, the model presented is solved at the initial time when the machine is purchased. Then, each time a breakdown occurs, the model is solved again using the updated breakdown information. The initial solution contains the optimal preventive maintenance policy and the optimal resale time of the machine assuming it remains functional. At each breakdown time, the optimal breakdown‐repair policy is obtained indicating whether a repair should be made or whether the broken machine should be junked. If a repair is made, then the optimal preventive maintenance policy and resale time of the machine are derived. It is shown that if a breakdown occurs following the interval in which preventive maintenance is optimally applied, then a repair is not advocated and the machine is junked. In addition, a method for deriving the future expected times of machine failures is presented. Therefore, in addition to the optimal decisions corresponding to the realized behaviour to the machine, optimal policies are derived which consider its future expected performance.

Predictive maintenance, as a form of pro-active maintenance, has increasing usage and shows significant superiority over the corrective and preventive maintenance. However, conventional methods of predictive maintenance have noteworthy limitations in maintenance optimization and reliability improvement. In the last two decades, machine learning has flourished and overcome many inherent flaws of conventional maintenance prediction methods. Meanwhile, machine learning displays unprecedented predictive power in maintenance prediction and optimization. This paper compares the features of corrective, preventive, and predictive maintenance, examines the conventional approaches to predictive maintenance, and analyzes their drawbacks. Subsequently, this paper explores the driving forces, and advantages of machine learning over conventional solutions in predictive maintenance. Specifically, this paper reviews popular supervised learning and reinforcement learning algorithms and the associated typical applications in predictive maintenance. Furthermore, this paper summarizes the four critical steps of machine learning applications in maintenance prediction. Finally, the author proposes the future researches concerning how to utilize machine learning to optimize maintenance prediction and planning, improve equipment reliability, and achieve the best possible benefit.

Most research on preventive maintenance (PM) assumed that a PM interval is a fixed value. However, practical PM actions usually are affected by some internal and external random factors, which make the PM interval become a limited random value rather then a fixed value. Few of the existing research consider this situation for two-unit system in their models. In this paper, a quasi-periodic PM policy for one type of two-unit series system with a dynamic maintenance plan is proposed, in which PM for U 1 is perfect whereas for U 2 is imperfect, and the dynamic PM plan is executed in each implemented period, and PM activities are performed through competing results of the dynamic PM plan and a catastrophic failure of U 2. The optimal implemented period of PM and PM number is obtained which minimizes the excepted cost per unit time of the system in long run. Finally, a numerical example is given to illustrate the proposed model.KeywordsQuasi-periodic preventive maintenance policyImplemented periodTwo-unit series systemDynamic preventive maintenance plan