Linear and Nonlinear Preventive Maintenance Models

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.

Stress-reducing preventive maintenance model for a unit under stressful environment

Most preventive maintenance (PM) models require, as inputs, information on the behaviour of the equipment under failure-only maintenance (FM). Once a schedule of PM has been applied, data arising from failures are affected by the PM. Over life the behaviour that would occur under FM changes. But PM also tends to delay aging processes. This paper examines: 1) how data collected under four kinds of PM policy can be modified to re-assess the FM characteristics, and 2) how to tell whether an apparent change in them is important. The approach takes account of engineering as well as statistical factors. It concludes that: 1. Estimation of distributions and costs as they would be under FM from data collected under PM cannot be accurate but a suboptimal policy can be worse. 2. A different type of PM policy might be required following changes to costs and/or distribution. The optimum under one model might not be the overall optimum. 3. The solutions suggested in the paper remain unproved due to the ``data problem''. Mathematical research alone can never produce workable procedures. A viable methodology will need years of experimentation with real systems. 4. The potential savings are huge.

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.

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 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.

Q: Due to constraints in our biomed budget, I was wondering: Is there anyway to reduce the rotations for sterilizers in our preventive maintenance (PM) program?A: Sure. In fact, several years ago, we reduced the rotation and the items we change on sterilizers. Our facility has 11 operating rooms (OR) and two labor-and-delivery rooms. Our sterilizers get fairly heavy use between 6 a.m. and 11 p.m. But we're definitely not as busy as facilities that run 24 hours a day, 365 days a year. So we reduced the PM schedule for some of them to a semiannual basis. This process has worked very well. We have not had any more failures than we had when we did them quarterly. We saved ourselves time and the materials we would have had in replacing those quarterly parts. Certain sterilizers, like the washer-sterilizers, still undergo quarterly PM due to their fairly heavy use and the fact that we only have two in the OR. If one goes down, the impact to our customer is significant.I am making my suggestions based on the assumption that your facility has a good preventive maintenance program and some sort of a program that enables you to track equipment. By tracking equipment repairs, you can monitor the results if you reduce your facility's PM schedule. Our facility continuously monitors work requests through a program in our database. This program allows use to “write” almost any report one can imagine. For example, one of our performance indicators is repeat work requests. We have a report that compiles all such requests during a given period—three months, in our case. Each technician is given the piece of the report related to the equipment that he or she is responsible for. The technician then checks the work requests for that equipment, looking for anything that can be incorporated into a regular PM schedule and prevent a failure. This allows your facility to “hold all the cards,” so to speak. If someone at your facility periodically monitors your success, you will be able to adjust your PM program accordingly.As for what parts can be reduced, that depends on several factors such as use, steam quality, water quality, and maintenance program effectiveness. Our water here tends to be higher in calcium than most parts can tolerate. So we keep water traps and water solenoids on a semiannual schedule. That said, our steam quality is fairly good, and we push our steam parts as long as possible. When we looked at ways to reduce cost, we looked along the line of steam—not water.Another key factor is staff care with equipment. For example, we once had someone in the instrument room who would slam the doors of the sterilizers so hard that the machines shook. We talked to him on several occasions, but he slammed the door right up until his last day at our facility. If that was not bad enough, after slamming it one time, this now-former employee cranked hard on the handle and then brought it to me to report, “It's broke.” Ha, ha! While he worked at our facility, we stepped up the PM frequency on the doors. We would check the handles for tightness, the arms that wind out to make sure they were not missing their holders, the door seal which would snap if brittle and the door was slammed, and the door cover to make sure it had not been bent.But let me end by returning to the big picture. You can and should alter your PM schedule to maximize your resources. Working with a good database, a good PM program can save you time and money. Remember: Garbage in, garbage out.

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

Optimal preventive maintenance policy for leased equipment using failure rate reduction

ABSTRACTA framework for reliability and maintenance analysis of job shop manufacturing systems is proposed in this paper. An efficient preventive maintenance (PM) policy in terms of failure effects analysis (FEA) is proposed. Subsequently, reliability evaluation and component importance measure based on FEA are performed under the PM policy. A job shop manufacturing system is applied to validate the reliability evaluation and dynamic maintenance policy. Obtained results are compared with existed methods and the effectiveness is validated. Some vague understandings for issues such as network modelling, vulnerabilities identification, the evaluation criteria of repairable systems, as well as PM policy during manufacturing system reliability analysis are elaborated. This framework can help for reliability optimisation and rational maintenance resources allocation of job shop manufacturing systems.

PurposeWhile steady-state analysis is useful, it does not consider the inherent transient characteristics of repairable systems' behavior, especially in systems that have relatively short life spans, or when their transient behavior is of special concern such as the motivating example used in this paper, military systems. Therefore, a maintenance policy that considers both transient and steady-state availability and aims to achieve the best trade-off between high steady-state availability and rapid stabilization is essential.Design/methodology/approachThis paper studies the transient behavior of system availability under the Kijima Type II virtual age model. While such systems achieve steady-state availability, and it has been proved that deploying preventive maintenance (PM) can significantly improve its steady-state availability, this improvement often comes at the price of longer and increased fluctuating transient behavior, which affects overall system performance. The authors present a methodology that identifies the optimal PM policy that achieves the best trade-off between high steady-state availability and rapid stabilization based on cost-availability analysis.FindingsWhen the proposed simulation-based optimization and cost analysis methodology is applied to the motivating example, it produces an optimal PM policy that achieves an availability–variability balance between transient and steady-state system behaviors. The optimal PM policy produces a notably lower availability coefficient of variation (by 11.5%), while at the same time suffering a negligible limiting availability loss of only 0.3%. The new optimal PM policy also provides cost savings of about 5% in total maintenance cost. The performed sensitivity analysis shows that the system's optimal maintenance cost is sensitive to the repair time, the shape parameter of the Weibull distribution and the downtime cost, but is robust with respect to changes in the remaining parameters.Originality/valueMost of the current maintenance models emphasize the steady-state behavior of availability and neglect its transient behavior. For some systems, using steady-state availability as the sole metric for performance is not adequate, especially in systems that have relatively short life spans or when their transient behavior affects the overall performance. However, little work has been done on the transient analysis of such systems. In this paper, the authors aim to fill this gap by emphasizing such systems and applications where transient behavior is of critical importance to efficiently optimize system performance. The authors use military systems as a motivating example.

A special game against nature is examined in this paper, in which nature controls timings of repairable and non-repairable failures of a system of equipment, and preventive maintenance, repairs and preventive replacement policies are the countermeasures. We optimize the imperfect preventive maintenance policy for a multi-unit system with different initial virtual age units. We develop a binary integer programming problem, where the management decides on the optimal preventive maintenance policy to minimize the total expected maintenance cost not to exceed a given budget. The mathematical formulation is developed for a multi-unit system, considering different preventive maintenance levels for each unit. Numerical examples with sensitivity analysis are performed to illustrate the performance and efficiency of the proposed model. Also, the model is examined for a real case study from railway industry. The results determine the optimal preventive maintenance policy and provide managerial insights based on computational analysis.

We discuss models of preventive maintenance (PM) under different assumptions on the failure/repair processes. The main emphasis is on the PM modeling under the Brown‐Proschan model for minimal/perfect repairs. We also present the generalization of this model for the failure/repair process governed by the generalized Polya process. Some comparisons with conventional PM models are discussed (Nonhomogeneous Poisson process, renewal process, and geometric process of failure/repairs). Several practical examples are considered.

Security constrained Preventive Maintenance (PM) scheduling is addressed as a crucial issue especially coping with new challenges of smart grids. Under the smart grid environment, Demand Response Resources (DRRs) are considered as virtual power plants in energy policy decisions which affect the controllability of power system ranging from short-term to long-term. Here, DRRs are regarded as a virtual resource of capacity reserve acquisition. Furthermore, under new environment, multifarious objectives will be imposed to PM scheduling which can be replicated into a single objective problem via a weighted approach with consideration of arbitrary weighting coefficients. In this paper, a new structure is proposed for determining the efficient weighting factors for reserve cost, operation cost, and generated emissions in PM scheduling associated with DR Programs (DRPs) the so-called PMDRPs. Utilizing Entropy method as one of the Multi Attribute Decision Making (MADM) techniques, the aforementioned factors are determined in a two phase hierarchy. Using the nominated structure, the maintenance scheme, commitment status, energy scheduling, and demand-as well as supply-side reserve scheduling are determined along the scheduling horizon. The numerical studies are carried out on the IEEE-RTS to confirm the capability of the proposed framework.

Jet engines are critical assets in aircraft, and their availability is crucial in the modern aircraft industry. Therefore, their maintenance scheduling is one of the major tasks an airline has to make during an engine’s lifetime. A proper engine maintenance schedule can significantly reduce maintenance costs without compromising the aircraft's reliability and safety. Different maintenance scheduling approaches have been used for jet engines, such as corrective, preventive, and predictive maintenance strategies. Regarding the safety demands in aircraft industries, preventive maintenance is a frequent maintenance method for jet engines. However, preventive maintenance schedules are often use fixed maintenance intervals, which is usually suboptimal. This paper focuses on minimizing a jet engine's overall maintenance cost by optimizing its preventive maintenance schedule based on an engine’s comprehensive reliability model. A hierarchical optimization framework including the golden section search and genetic algorithms is applied to find the optimal set of preventive maintenance number and their times and the components to be replaced at those times during the jet engine's overall lifetime. The Monte Carlo simulation is used to estimate the engine’s failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the engine's overall corrective and preventive maintenance costs during its lifetime. Finally, an optimal preventive maintenance schedule is proposed for an RB 211 jet engine using the presented method. In the end, comparing the proposed method's overall maintenance cost with two other maintenance methods demonstrates the proposed schedule's effectiveness. The method presented in this paper is generic, and it can be used for other similar engineering systems.