Preventive Maintenance of Consecutive Multi-unit Systems

This paper considers replacement and maintenance policies for an operating unit which works at random times for jobs. The unit undergoes minimal repairs at failures and is replaced at a planned time T or at a number N of working times, whichever occurs first. The expected cost rate is obtained, and an optimal policy which minimizes it is derived analytically. The imperfect preventive maintenance (PM) model, where the unit is improved by PM after the completion of each working time, is analyzed. Furthermore, when the work of a job incurs some damage to the unit, the replacement model with number N is proposed. The expected cost rate is obtained by using theory of cumulative processes. Two modified models, where the unit is replaced at number N or at the first completion of the working time over time T, and it is replaced at T or number N, whichever occurs last, are also proposed. Finally, when the unit is replaced at time T, number N or Kth failure, whichever occurs first, the expected cost rate is also obtained.

In this chapter maintenance concept is defined and maintenance types are classified with regard to implementing maintenance policies toward preventive maintenance. Especially achieving planned maintenance policies toward preventive (planned) maintenance and condition based maintenance policies toward predictive maintenance is taken into consideration primarily. Maintenance concept is defined and maintenance types are classified. Due care is given for ‘Preventive Maintenance’ in this chapter. In general ‘Maintenance’ term could be defined as; The integration of all possible technical and administrative actions, including planning, supervising, monitoring and controlling toward retaining an item, a system, a machine to restore their original functional state in which they can perform the intended functions. In addition maintenance, include protective and corrective actions to keep the plant operational system in intended conditions or to maintain the acceptable manufacturing conditions. Optimum maintenance policies aims to sustain system reliability and robustness within minimum cost. In line with the progress of industry, increase in the system, material and manpower costs, increasing demand for robustness and the complex structure of the machines increases also the importance of maintenance policies. Maintenance types could be divided into two main parts namely; 1. Preventive Maintenance, 2. Corrective Maintenance. Preventive Maintenance is also classified into following sub groups; 1.a- Planned Maintenance, 1.b- Predictive Maintenance, 1.c- Advanced Maintenance Implementations; 1.c.1 Reliability Centered Maintenance 1.c.2 Risk Based Maintenance. All of these maintenance types elaborated with relevant figures in the chapter. In this chapter Comparison of Planned and Unplanned (corrective) Maintenance (With regard to transaction and output) is defined with a table. Additionally, Comparison of Planned and Unplanned (corrective) Maintenance (With regard to infra structure) is also tabulated. ‘Benefits of Preventive & Predictive Maintenance’ and ‘Predictive Maintenance Methods’ are defined with relevant descriptive figures in the chapter. For Corrective Maintenance, basic definitions and corrective maintenance steps, types of corrective Maintenance, improvement strategies in corrective maintenance effectiveness are also given. In the final part of ‘Results and conclusions’ expected and verified benefits of implementing maintenance policies for planned and predictive maintenance are explained. Comparisons in some maintenance policies is given.

Purpose Making decisions on preventive maintenance (PM) policy and buffer sizing, as is often studied, may not result in overall optimization. The purpose of this paper is to propose a joint model that integrates PM and buffer sizing with consideration of quality loss for a degenerating system, which aims to minimize the average operation cost for a finite horizon. The opportunistic maintenance (OM) policy which could increase the output and decrease the cost of the system is also explored. Design/methodology/approach A joint PM and buffer size model considering quality loss is proposed. In this model, the time-based PM and the condition-based PM are taken on the upstream and the downstream machine, respectively. Further, the OM policy based on the theory of constraints (TOC) is also considered. An iterative search algorithm with Monte Carlo is developed to solve the non-linear model. A case study is conducted to illustrate the performance of the proposed PM policies. Findings The superiority of the proposed integrated policies compared with the separate PM policy is demonstrated. Effects of the policies are testified. The advantages of the proposed TOC-based OM policy is highlighted in terms of low-cost and high-output. Originality/value Few studies have been carried out to integrate decisions on PM and buffer size when taking the quality loss into consideration for degenerating systems. Most PM models treat machines equally ignoring the various roles of them. A more comprehensive and integrated model based on TOC is proposed, accompanied by an iterative search algorithm with Monte Carlo for solving it. An OM policy to further improve the performance of system is also presented.

Different type of maintenance problems have been investigated during the past decades. With the growth in complexity of modern systems, the cost of purchasing new systems becomes higher ever than before. Surprisingly, very little attention in the literature has been paid to the failure replacement in these preventive maintenance models. However, in practice most systems incur increasingly expensive down time costs when the systems suffer the unexpected failures. The major changes to the standard maintenance model in this research are the addition of a failure replacement cost for a failed system and the reliability limit for which system has to be replaced. The overall objective of this research is to propose an efficient algorithm to obtain the optimal maintenance policy. The optimal preventive maintenance policy is investigated with incorporating the costs of minimal repair, imperfect preventive maintenance, failure replacement and preventive replacement. The specific maintenance actions for a repairable system are when to perform an imperfect preventive maintenance or to replace the system while the system is in the operating condition. In another case, the specific maintenance actions for a repairable system are whether to replace the system, or to perform a minimal repair when the system fails. The number of imperfect preventive maintenance and the control limits of the optimal maintenance policy are calculated under the expected average cost rate criterion.

This study proposes a sequential Condition-Based Maintenance (CBM) policy for intelligent monitored system based on cost and reliability prioritisation. It is assumed that system's reliability could be continuously monitored, whenever it reaches the threshold R, and scheduled maintenance activity is performed to restore the system. This maintenance policy differs from other policies in taking into consideration of influences from the frequency of maintenance activities and operating time on system's failure rate function subject to a deterioration process. Meanwhile, this CBM policy firstly considers the variant operational cost and variant minimal repair cost by the effects of system's corresponding condition, which helps to construct more practical maintenance model. Finally, a numerical example is presented to test this proposed sequential predictive maintenance policy and the simulation results show its efficiency.

Preventive maintenance (PM) can slow the deterioration process of a repairable system and restore the system to a younger state. The proposed PM model of this paper focuses on the restoration effect of degradation rate reduction which can only relieve stress temporarily and slow the rate of system degradation while the hazard rate is still monotonically increased. This PM model considers a deteriorating but repairable system (or equipment) with a finite life time period. This PM model is modified based on an original degradation-rate-reduction PM model over a finite time span of which the searching range for the optimal solution of the time interval between each PM is limited. It is demonstrated that the proposed degradation-rate-reduction PM model over a finite time period can have a better optimal solution than the original PM model. The algorithm of finding the optimal solution for the modified PM model is developed. Examples are provided and are compared with the corresponding original PM model.

Optimal rearrangement and preventive maintenance policies for heterogeneous balanced systems with three failure modes

본 논문에서는 수리가 가능한 시스템에 대하여 혼합보증이 종료된 이후의 교체모형과 예방보전모형을 고려하는데, 만약 보증기간이 종료된 이후에 시스템에 고장이 발생하면 최소수리를 수행한다. 최적의 교체정책과 최적의 예방보전정책을 결정하기 위한 기준으로는 기대비용과 기대비가동시간에 근거한 총밸류함수를 사용한다. 그리고 시스템의 고장시간이 와이블분포를 할 때 수치적 예를 통해서 제안된 최적의 교체정책 및 예방보전정책을 자세히 설명하고자 한다. This paper considers the replacement model and the preventive maintenance model following the expiration of combination warranty for a repairable system. If the system fails after the combination warranty is expired, then it is minimally repaired at each failure. The criterion used to determine the optimal replacement policy and the optimal preventive maintenance policy is the overall value function based on the expected cost rate per unit time and the expected downtime per unit time. The numerical examples are presented for illustrative purpose when the failure time follows a Weibull distribution.

An Efficient Jaya Algorithm for Joint Optimization of Preventive Maintenance and Quality Policy in Production Systems

In this paper a multi-state deterioration system which experiences several states of performance degradation until it fails is studied extensively and condition-based preventive maintenance policies are examined. The optimal maintenance policy aims at maximizing system's asymptotic availability and at minimizing its total operational cost, with respect to the two different inspection intervals. For the simultaneous optimization of the aforementioned measures, multi-objective optimization methods are employed. In the current work, the inspection times are assumed to be constant, thus the system's evolution in time is modeled by a semi-Markov process. Finally, the proposed system is compared with the corresponding Markov one which is the most commonly used approximation of the original system in practice.

PurposeThe purpose of this paper is to propose a predictive maintenance (PdM) system for hybrid degradation processes with continuous degradation and sudden damage to improve maintenance effectiveness.Design/methodology/approachThe PdM system updates the degradation model using partial condition monitoring information based on degradation type judgment. In addition, an extended multi-step-ahead updating stopping condition is adopted for performance enhancement of the PdM system.FindingsAn extensive numerical investigation compares the performance of the PdM system with the corresponding preventive maintenance (PM) policy. By carefully choosing the updating stopping condition, the PdM policy performs better than the corresponding PM policy.Research limitations/implicationsThe proposed PdM system is applicable to single-unit systems. And the continuous degradation process should be well modeled by the stochastic linear degradation model (Gebraeel et al., 2009).Originality/valueIn literature, there are abundant studies on PdM policies for continuous degradation processes. However, research on hybrid degradation processes still focuses on condition-based maintenance policy and a PdM policy for a hybrid degradation process is still unreported. In this paper, a PdM system for hybrid degradation processes with continuous degradation and sudden damage is proposed. The PdM system decides PM schedules by fully utilizing the condition monitoring data of each specific product, and can hopefully improve maintenance effectiveness.

Preventive maintenance (PM) is a maintenance program with activities initiated at predetermined intervals, or according to prescribed criteria, and intended to reduce the probability of failure, or the degradation of the functioning of an item. In the literature, a number of PM models have been introduced to depict the effectiveness of PM. Based on these models, approaches to scheduling PM policies have been considerably studied. This paper attempts to review existing PM models, and investigate their inter-relationships. We then categorize these models into three classes: linear, nonlinear, and a hybrid of both. These three PM model classes depict the relationships of the hazard functions before, and after a PM. Possible extensions to these three PM models are discussed. The statistical properties for models are derived, and approaches to optimizing the PM policy are given.

PurposeThe purpose of this paper is to develop a preventive maintenance policy with continuous monitoring for a circular consecutive-k-out-of-n: F systems. A preventive maintenance policy is developed based on the system critical condition which is related to the number of working components in the minimal cut sets of the system. If there is at least one minimal cut set which consists of only one working component, the system is maintained preventively (PM) after a certain time interval and the failed components are replaced with the new ones to prevent the system failures. If the system fails prior to the preventive maintenance, the system is correctively maintained (CM) immediately by replacing the failed components.Design/methodology/approachThe mathematical function of the expected cost rate for the proposed maintenance policy is derived. The costs of PM, CM, and replacement per component are considered. The optimal maintenance parameter, which is the PM interval, is obtained by enumeration, and the numerical studies are shown with various system and cost parameters. The performance of the proposed policy is evaluated by comparing its expected cost rate to those of the no-PM and age-PM policies. The percentage of cost increase from the no-PM and age-PM policies to the proposed PM policy is calculated and this value can represents how important the continuous monitoring in this policy.FindingsThe proposed policy outperforms other policies. When the cost of CM is high and the cost of PM is low, the proposed PM policy is more suitable.Research limitations/implicationsThe system consists of identical components and the component failure times follow an exponential distribution. Continuous monitoring is considered, which means that the component states can be known at any time. Three cost parameters, cost of PM, CM, and replacement per component, are considered.Originality/valueThis paper shows a maintenance problem for circular consecutive-k-out-of-n: F systems. Many studies on this system type focused on the reliability estimation or system design problem. Previous study with this policy (Endharta and Yun, 2015) has been developed for linear systems, although the study used a simulation approach to estimate the expected cost rate. Also, Endharta et al. (2016) considered a similar method for the different types of system, which is linear consecutive-k-out-of-n: F system.

Thompson1 considers a linear control model to ascertain the optimal preventive maintenance policy for production equipment and, simultaneously, its optimal sale date. He assumes age‐dependent deterministic continuous deterioration, production rate and linear efficiency of maintenance expenditures. In the present paper we first consider a linear maintenance model under a budget constraint. The impact of such a restriction is an earlier switch in the optimal bang‐bang policy. Then a non‐linear, non‐autonomous generalization of Thompson's model is analysed by the maximum principle. The optimal maintenance policy is shown to be downward sloping provided that in each time period the production rate is greater than the sum of interest and deterioration rate. The monotonicity of the optimal rate of preventive maintenance holds also under the additional possibility of random machine failure provided the production rate is greater than the sum of the rate of interest, the rate of deterioration and the rate of stochastic failure at each age. Moreover, it is shown that the higher the failure rate the smaller the optimal expenditures for preventive maintenance at any fixed age of the machine. In particular, the optimal maintenance rate at age t in a model without random failure is greater than those in any case including stochastic failure at the same age.

Two-stage maintenance of a production system with exponentially distributed On- and Off-periods