Optimal Policy Study on Reliability-Centered Preventive Maintenance for a Single-Equipment System

Repairable systems deteriorate with age and usage. In order to maintain acceptable reliability and prevent sudden failures, preventive maintenance (PM) is often applied to such systems. Scheduled PM actions can improve system availability and minimize losses due to breakdowns and failures. However, condition‐based PM is considered more relevant to system status than age‐based PM for deteriorating repairable systems. In this study, we consider the production yield rate as the condition variable when determining the optimal PM schedule. A non‐homogeneous Poisson process is used to describe the system deterioration, and the concept of system effective (virtual) age is also considered to make the proposed model more realistic. Three different condition‐based PM strategies are presented to provide more diverse choices for decision makers in terms of solving problems based on the situation at hand. The results show that the optimal yield rate threshold can reduce system failure while maintaining acceptable system availability and product quality with affordable costs due to a proper PM schedule. Furthermore, the results of the numerical application showed that the maintenance and penalty costs are the most sensitive to the total cost, where the adoption of the proposed PM strategies are more profitable when related costs are low.

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

“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.

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.

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.

PurposeThis paper aims to propose an approach for the optimization of imperfect preventive maintenance and corrective actions performed on a single machine. After maintenance, the machine returns to an age between “as good as new” and “as bad as old”.Design/methodology/approachThe approach is based on fuzzy logic and simulation‐based optimization. Fuzzy logic is preferred over crisp logic because it is relatively easy to implement in this situation considering that the human factor is hardly interpreted by analytical methods because of its unpredictable nature. Simulation‐based optimization is used to have a more reactive and accurate tool for practitioners.FindingsTaking into account the impact of the imperfections due to human factors, the period for preventive maintenance, which minimizes the expected cost rate per unit of time or maximizes the availability of the system, is evaluated by the simulation‐based optimization.Research limitations/implicationsDifferent and more realistic maintenance levels must be considered and the traceability of a specific system could be used to determine the most appropriate failure law. For this study, cost reduction was considered as the priority, but the model can be adjusted according to the user's preferences.Practical implicationsThis paper considers a single repairable machine as a system that undergoes periodic preventive and corrective maintenance actions. Considering maintenance imperfections, rule‐based fuzzy logic can be integrated into the maintenance program to determine the times for the periodic preventive maintenance actions.Originality/valueConsidering human factors in maintenance programs is indispensable to assure more accurate and realistic results. However, due to the difficulty engendered by their modeling, most theoretical maintenance models do not consider these factors. Therefore, the proposed fuzzy model in the paper can be an important tool to include them.

Optimal preventive maintenance policy for leased equipment using failure rate reduction

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.

In view of the lack of consideration of equipment maintenance in the production scheduling of prefabricated components, a production scheduling optimization problem is proposed to meet the needs of prefabricated component production planning and preventive maintenance planning. A joint optimization model of aperiodic preventive maintenance production scheduling is established by considering the production process characteristics of prefabricated components and reliability-centered maintenance. The model is solved with the goal of minimizing the total cost, and finally the optimal production scheduling plan and preventive maintenance plan can be determined. The results of numerical analysis show the feasibility and effectiveness of the optimization model, and it is significantly better than the periodic preventive maintenance production scheduling model.

PurposeThis study focuses on the application of reliability-centered maintenance (RCM) to a textile industry steam boiler. The study aims to demonstrate the development and application of RCM to a steam boiler used in the textile industry.Design/methodology/approachRCM is a structured process that develops maintenance activities needed on physical resources in their operational environment to realize their inherent reliability by logically incorporating an appropriate mixture of reactive, preventive, condition-based and proactive maintenance methods. A detailed analysis of the RCM approach is presented to develop preventive maintenance (PM) program and improve the reliability and availability of the steam boiler system.FindingsThe research reveals that the identification of PM tasks is a good indicator of the PM program's efficiency and can serve as an important maintenance-related downtime source. It is also discovered that the majority of maintenance programs that claim to be proactive are, in fact, reactive. This article also shows how RCM may be successfully implemented to any system, resulting in increased system reliability.Research limitations/implicationsThe paper focuses on a pilot study of the development and implementation of the RCM technique to a textile industry steam boiler. It is suggested that the developed RCM model can be applied to the entire plant.Originality/valueThe paper presents a comprehensive RCM model framework as well as an RCM decision framework, providing maintenance managers and engineers with a step-by-step approach to RCM implementation. The proposed framework is significant in that it may be utilized for both qualitative and quantitative analysis at the same time.

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.

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

Application of RCM to a medium scale industry