Condition‐based preventive maintenance with a yield rate threshold for deteriorating repairable systems

Because of demanding higher operational efficiency and safety in industrial systems, system maintenance trends to the direction of high speed, high load and high automation. Reliability-centered preventive maintenance schedule plays an important role in production, but it is always a complex task to make such a plan. In this paper, the optimization of reliability-centered preventive maintenance (PM) for a single-equipment system can be proposed. In order to reduce downtime loss, several PM actions are performed together using the threshold for opportunity PM. By using Markov decision process method, the transition probability matrix for different PM actions can be obtained, and then the optimal combination problem on PM strategy can be developed. Finally, the optimal PM schedule which can provide the desired levels of reliability to a single-component system and minimize maintenance cost can be obtained. A case is used to demonstrate the implementation and potential applications of the proposed method.

An optimal dynamic interval preventive maintenance scheduling for series systems

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.

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

Leasing has proven to be a business model that is perfectly suited to the circular economy. It significantly contributes to sustainable development by enabling the reuse of machinery and equipment after each lease period and by including preventive maintenance and overhauls within and between lease terms. This helps to extend the life cycle of equipment, promote value recovery, and reduce waste. This paper examines an imperfect preventive maintenance (PM) strategy applied to equipment rented under the terms of “free leasing”. In free leasing, the lessor makes the equipment available to the customer for a specified period of time without charging rent. In return, the customer is required to purchase the equipment’s consumables exclusively from the lessor. The lessor is also responsible for the maintenance of the equipment at the customer’s premises. The greater the quantity of consumables used by the customer, the more the equipment will deteriorate. Consequently, the lessor must be able to determine the most effective approach to preventive maintenance, ensuring that it aligns with the customer’s planned usage rate while maximizing profit. This work proposes a PM strategy to be adopted by the lessor during the free lease period. This strategy involves the performance of imperfect PM actions just before the start of the lease period and then periodically. Different packages of preventive actions can be applied each time, with each package having a different cost depending on the level of effectiveness in terms of rejuvenating the equipment. Minimal repairs are performed in the event of equipment failure. The decision variables are the PM period to be adopted and the maintenance efficiency level to be chosen for each preventive intervention. The objective is to determine, for a given customer with an estimated consumption rate profile of consumables, the optimal values of these decision variables so that the lessor maximizes their profit. A mathematical model is developed to express the lessor’s average profit over each lease period. A solution procedure is developed for small instances of the problem, and an Artificial Bee Colony algorithm is implemented for larger instances. A numerical example and a sensitivity analysis are presented.

“Vehicle damage insurance” in China, “Protection Plus Mobile Elite” launched by Samsung, “iPhone Apple Care +” offered by Apple, and so on are special warranty services aiming at external shocks. These warranty terms, named accident insurance, exist widely in the marketplace. Considering the effect of external shocks on degradation, a two-dimensional preventive maintenance and replacement strategy for products, sold with a nonrenewing and two-dimensional rectangle warranty service, is proposed in this paper. Under this strategy, preventive maintenance actions are scheduled based on units of age or usage, which occurs first. There is a reduction in the intensity function after a preventive maintenance action. Each shock before the Nth shock causes a failure of the product or an increase in product failure rate. The product is replaced by a new one on the Nth shock if it survives the N − 1 shocks. From the view of manufacturers, the mean warranty servicing cost over the whole warranty region is obtained by using the renewal theory. Based on direct numerical Riemann–Stieltjes integration, an approximation algorithm of the cost is also given. The mean cost is minimized by the optimization of preventive maintenance interval and the number of shocks N. A numerical example is given to illustrate the feasibility of the proposed strategy. The effects of maintenance cost, the arrival rate of the shocks, and other model parameters on the optimal strategy are also investigated numerically.

Preventative maintenance (PM) measures for bridges are proactive maintenance actions which aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds since it can extend the life of the bridge and avoid the need for unplanned essential/corrective maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed a two-stage evolutionary optimization methodology based on genetic algorithm (GA) principles which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. In this paper, the sensitivity of the methodology to various key input parameters of the optimization methodology is examined in order to quantify their effects and identify possible trends in the optimum PM intervention profiles. The results of the sensitivity studies highlight the combined use of both proactive and reactive PM measures in deriving optimum strategy solutions. The precise mix and sequence of PM measures is clearly a function of the relative effectiveness and cost of the different available PM options as well as the various key parameters such as discount rate, target probability of failure, initial probability of failure and service life period examined. While the results highlight the need for more reliable data they also demonstrate the robustness and usefulness of the methodology; in the case where data is limited it can be used as a comparative tool to improve understanding of the effects of various strategies and enhance the decision making process.

The purpose of this paper is to properly plan the preventive maintenance schedule for multiple elevators and optimize the number of maintenance workers.The total amount of the maintenance cost consisting of the labor cost, the part cost, and the quality cost (the loss evaluated in terms of cost, to be incurred when an elevator breaks down) is to be minimized.The method is presented of setting up the optimal preventive maintenance schedule on a long‐term basis by rescheduling the contents of schedule dynamically and flexibly in accordance with the ever‐changing maintenance conditions, taking the possibility of the future occurrence of failure into consideration. From numerical experiments, the validity of the proposal procedure for planning the preventive maintenance schedule and the effectiveness of considering the possibility of the future occurrence of failure in planning the schedule are shown, and the optimal number of maintenance workers can be decided.

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.

The preventive maintenance (PM) is scheduled periodically to restore the system condition. However, PM activities cannot generally restore the system to as good as new condition but in an intermediate situation, which is called imperfect PM. The PM interval optimization is useful for improving system reliability. A popular approach to optimization interval is minimizing the maintenance costs. The goal of this work is to obtain optimal PM intervals that minimize expected cost considered PM imperfect, in other words we obtain the optimal PM intervals assuming that the PM action restores the system operating state to be somewhere between as good as new and as bad as old. The results are applied in a real data and the PM is maintained at a decreasing sequence of intervals, which is more practical since most systems need more frequent maintenance with increased usage and age. Furthermore consider a wrong PM perfect hypotheses increase the maintenance cost substantially.

Shutting down a power plant for any reason is a costly process. Loss of production revenues from one unit can exceed millions of dollars per day. It is therefore good practice to keep outages as short and efficient as possible. A major component of planned outages is Preventive Maintenance (PM). It is important that PM be applied only where needed. Unnecessary PM is costly, not only in terms of labor and material costs, but in additional lost time and production. Further, there are cases where PM is contraindicated by standard reliability models, i.e. components with decreasing failure rates. This can paradoxically introduce failures, incurring additional repair costs and production loss. The risk management group at the South Texas Project Electric Generating Station (STPEGS) has successfully developed software to optimize PM schedules by minimizing total costs and risks associated with corrective maintenance (CM) and PM. The optimum PM interval is determined as a multiple of outage cycles. Components are evaluated based on maintenance type, risk ranking, cost of performing CM at power, and regulatory or internal commitments. This information provided to systems engineering assists in determining whether to advance PM from an upcoming outage, or defer PM to a subsequent outage. Components with maintenance that can be performed online are outside of the scope of this paper and are not considered here.

Opportunistic preventive maintenance scheduling for multi-unit reusable rocket engine system based on the variable maintenance task window method

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.

In this paper, we deal with a preventive maintenance (PM) scheduling and spare parts problems for a rolling stock system. We determine the optimal PM interval and the optimal number of spare parts for components in the rolling stock system to minimize the system life cycle cost during satisfying the system target availability. The system availability and system life cycle cost are estimated by simulation. A genetic algorithm and a simulated annealing are proposed to find the optimal PM intervals and the optimal spare parts number of all components. Numerical examples are also studied.

Dynamic preventive maintenance strategy for MMC considering multi-term thermal cycles