Optimal preventive maintenance planning for multiple elevators

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

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.

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

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

Bayesian failure-rate modeling and preventive maintenance optimization

An optimal preventive maintenance scheduling model for deteriorating structures is presented. The random initial damage and the cumulative damages are quantitatively measured with the statistical distribution of a dominant fatigue crack size in the structure. The preventive maintenance for the structure in service is assumed to be possible only at a series of discrete times. By taking into account the costs of structure failure and preventive maintenance, a minimum cost rate criterion is established to determinate the optimal time for preventive maintenance. Finally, an illustrative example is given

A number of objective criteria for optimal maintenance scheduling of thermal generators is discussed. The criteria are based on generation operating cost, reliability indices, deviations from a desired schedule and/or constraints violation penalties. A comparison of the performance of all these criteria is presented by maintenance scheduling a realistic 30 thermal-unit system. Also, the relationships between the several realibility indices are dicussed.

The importance of preventive maintenance management has been gradually recognized specially with the great attention to the role of health technology management. Finding the optimal schedule to perform preventive maintenance for medical equipment is rarely considered in the literature. This research suggests using ant colony optimization method to solve the problem of finding the optimal preventive maintenance schedule. We developed 2 versions of the algorithm, both starting from a prioritized medical equipment list and differing in the heuristic function. The experimental results indicate the effectiveness of the ant colony optimization algorithm for this kind of problems.

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.

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.

Optimal scheduling of power transformers preventive maintenance with Bayesian statistical learning and influence diagrams

Maintenance cost contributes a large portion in total life-cycle cost of offshore wind systems and improves the efficiency of maintenance activities that has emerged as an effective solution in enhancing cost-effectiveness of offshore wind energy. This study proposes a mathematical model to determine the optimal maintenance schedules, by highlighting the deterioration process of offshore wind systems and accounting the optimal vessel routing of maintenance activities, maintenance cost therefore could be shifted to an optimal level. Firstly, reliability of each component is precisely analyzed with respect to the effect of previous maintenance activities. Then, an optimal individual maintenance schedule and a grouping maintenance schedule in daily manner are established based on reliability analysis and opportunity cost consideration. Finally, an optimal maintenance schedule approach accounting vessel routing is apparently presented to optimize the transportation cost of maintenance activities. Numerous influential factors such as system reliability, weather condition, maintenance resource, maintenance duration, production loss cost, and vessel related issues are deliberately examined to enhance the robustness and efficiency of the proposed approach. The experimental results investigating four different scenarios demonstrate that by applying the proposed approach, number of maintenance activity is lowered down, maintenance duration and vessel travelling distance are also significantly reduced in the range of 43.86% - 52.77%, and 52.90% - 58.35%, respectively. Therefore, the economic benefit on maintenance cost could be achieved in an interval of 15.15% - 23.25% by selecting the optimal maintenance schedule.

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.