Optimal Maintenance Time Under Imperfect Preventive Maintenance

An oil and gas company has 2,268 oil and gas wells. Well Barrier Element (WBE) is installed in a well to protect human, prevent asset damage and minimize harm to the environment. The primary WBE component is Surface Controlled Subsurface Safety Valve (SCSSV). The secondary WBE component is Christmas Tree Valves that consist of four valves i.e. Lower Master Valve (LMV), Upper Master Valve (UMV), Swab Valve (SV) and Wing Valve (WV). Current practice on WBE Preventive Maintenance (PM) program is conducted by considering the suggested schedule as stated on manual. Corrective Maintenance (CM) program is conducted when the component fails unexpectedly. Both PM and CM need cost and may cause production loss. This paper attempts to analyze the failure data and reliability based on historical data. Optimal PM interval is determined in order to minimize the total cost of maintenance per unit time. The optimal PM interval for SCSSV is 730 days, LMV is 985 days, UMV is 910 days, SV is 900 days and WV is 780 days. In average of all components, the cost reduction by implementing the suggested interval is 52%, while the reliability is improved by 4% and the availability is increased by 5%.

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.

Integration of balancing and preventive maintenance in straight and U-shaped resource-dependent assembly lines: MILP model and memetic algorithm

Group maintenance optimization of subsea Xmas trees with stochastic dependency

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.

The right frequency of preventive maintenance is essential for production availability with adequate safety and economic levels. The optimal preventive maintenance intervals are difficult to identify as production systems are becoming complex by combining electro-electronic and mechanical systems with large quantities. High rates of preventive maintenance boost quality costs, low availability, and high possibility of maintenance failures. Otherwise, it could happen an increase in unscheduled downtime and high costs by losing the production. The development of algorithms to evaluate the maintenance program performance becomes a challenge to validate when it includes continuous, discrete, and stochastic models. This paper proposes an approach to stochastic model checking for identifying the optimum frequency of preventive maintenance by mechanical equipment, simulated and verified through a network of timed automata. A case study was adopted to illustrate the effectiveness of the solution. It is useful to evaluate the optimal preventive maintenance frequency in similar circumstances.KeywordsReliabilityPreventive maintenanceStochastic model checker

“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.

An optimal dynamic interval preventive maintenance scheduling for series systems

Manufacturing costs are reduced significantly with the integrated optimization of preventive maintenance and quality control. In this paper, a new mixed integer non-linear programming model is presented. This model determines the optimal preventive maintenance interval and the optimal parameters of the ( ) control chart, including the sampling interval and the sample size and the control limit. The production system is considered in the form of a continuous time Markov chain. Formulation of the production process of a machine in the form of a continuous time Markov chain is a breakthrough in the integrated modeling of repair and quality. The goal is to reduce costs per unit time. It is assumed that preventive maintenance can be carried out at several levels either perfect or imperfect. The duration of corrective and preventive maintenance is not negligible. Considering the length of time for maintenance, this model is closer to the real production environment. A numerical example is used to illustrate this new model. Sensitivity analysis was performed to determine the effect of the model parameters on optimal decisions. This analysis further shows the relationship between preventive maintenance and statistical quality control as well as the performance of the new model.

Machine reliability and preventive maintenance planning for cellular manufacturing systems

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.

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.

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

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