Optimal Preventive Maintenance Schedule Research Articles

Optimal Preventive Maintenance Scheduling for Wind Turbines under Condition Monitoring

Renewable energy sources, such as wind and solar, are positioned to play a pivotal role in future energy systems. In this paper, we propose a mathematical model for calculating and regularly updating the next preventive maintenance plan for a wind farm. Our optimization criterion considers various factors, including the current ages of key components, major maintenance costs, eventual energy production losses, and available data monitoring the condition of the wind turbines. Employing Cox proportional hazards analysis, we develop a comprehensive approach that accounts for the current ages of critical components, significant maintenance costs, potential energy production losses, and data collected from monitoring the condition of wind turbines. We illustrate the effectiveness of our approach through a case study based on data collected from multiple wind farms in Sweden. Our results demonstrate that preventive maintenance planning yields positive effects, particularly when the wind turbine components in question have significantly shorter lifespans than the turbine itself.

Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm

Maintenance scheduling is essential and crucial for wind turbines to avoid breakdowns and reduce maintenance costs. Many maintenance models have been developed for wind turbines' maintenance planning, such as corrective, preventive and predictive maintenance. Due to communities' dependence on wind turbines for electricity needs, preventative maintenance is the most widely used method for maintenance scheduling. The downside to using this approach is that preventive maintenance is often done in fixed intervals, which is inefficient. In this paper, a more detailed maintenance plan for a 2MW wind turbine has been developed. The paper's focus is to minimize a wind turbine's maintenance cost based on a wind turbine's reliability model. This study uses a two-layer optimization framework: Fibonacci and Genetic Algorithm (GA). The first layer in the optimization method (Fibonacci) finds the optimal number of preventive maintenance required for the system. In the second layer, the optimal times for preventative maintenance and optimal components to maintain have been determined to minimize maintenance costs. The Monte Carlo simulation estimates wind turbine component failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the overall corrective and preventive maintenance costs during the system's lifetime. Finally, an optimal preventive maintenance schedule is proposed for a 2MW wind turbine using the presented method. The method used in this paper can be expanded to a wind farm or similar engineering systems.

An exact MILP model for joint switch placement and preventive maintenance scheduling considering incentive regulation

Power delivery with high reliability is one of the main objectives of any distribution system. In this regard, system operators are adapting the traditional distribution systems with sectionalized switches and optimal preventive maintenance (PM) scheduling techniques as two effective methods for reliability improvement. However, they suffer due to the lack of a practical model in which switch placement (SP) and PM scheduling problems are considered simultaneously. Furthermore, consideration of incentive regulation schemes, such as reward-penalty scheme (RPS) is highly questionable in the separated reliability improvement problems. In this paper, a mix-integer linear programming model is presented wherein both SP and PM scheduling problems are considered. Rather than using conventional methods for linearization, an exact formulation is proposed to avoid the impact of approximations on the final decision. Moreover, RPS is also regarded as an incentive regulation scheme to balance the reliability level and financial performance. The results of test and real network implementation confirm that a higher performance is reached in terms of reliability and financial issues when the proposed model is utilized compared to the other ones. Besides, analysis demonstrates that the proposed model behaves effectively in both mid and long terms.

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

AbstractRepairable 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.

Jet Engine Optimal Preventive Maintenance Scheduling Using Golden Section Search and Genetic Algorithm

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.

A Discrete Chaotic Jaya algorithm for optimal preventive maintenance scheduling of power systems generators

The main role of the optimal Generator Maintenance Scheduling (GMS) problem in power systems is to develop an optimal preventive maintenance scheduling of the generation part units. An optimal GMS provides power systems with higher operational reliability, extends the generators lifetime and reduces the cost of generators maintenance. The GMS problem is formulated as an optimisation problem. This problem should satisfy both load’s power demand and workforce constraints with ensuring the reliability of power systems at economical operation cost. The GMS problem has been studied for many years when exact mathematical methods have been used in the past to reach exact solutions for small-scale problems. However, these conventional mathematical approaches have many limitations and they suffer from unreasonable computational efforts as system dimension increases. Traditional approximate methods have been adopted to overcome the limitations of exact methods for medium-scale power systems. However, they provide approximate solutions and they require a large computational effort for wide-area systems of big dimensions. Recently, modern methods based on metaheuristics optimisation have taken a long part to solve the GMS problem and to overcome the limitations of approximate methods. In this paper, a proposed Discrete Chaotic Jaya Optimisation (DCJO) algorithm is employed to perform the preventive maintenance scheduling of electric power systems generators. The proposed algorithm is based on a cooperation between the discrete Jaya optimisation algorithm and a proposed move rule based on Chaotic Local Search (CLS) technique to improve both exploration and exploitation phases. The GMS problem is modelled based on the reliability criterion of an objective function of a sum of the squares of the reserves of generation. The optimisation process is performed through minimising an evaluation function of a weighted sum of the objective function and the penalty function for violations of the constraints. The proposed approach has been tested in a 21-unit test system over a planned horizon of 52 weeks in which the peak load is 4739 MW and the maximum generation is 5688 MW and there is a total number of 35 workforce available per week to perform the maintenance tasks. The proposed method has been compared through several statistical tests with recent algorithms of the related works. The obtained results show the effectiveness of the proposed algorithm for solving the GMS problem as compared to other recent algorithms. This approach can be relied at the present upon to solve maintenance scheduling problems of generation units in power systems.

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

The reusable rocket engine (RRE), recognized as future propulsion system for space transportation, is composed of multiple components of which each one plays an essential role for the whole system. In rocket launches, these components are impaired by severe service conditions while the maintenance actions can only be executed in mission intervals. To program a reasonable maintenance scheme for the RRE system, an opportunistic preventive maintenance (PM) scheduling strategy is proposed: at the component-level, we evaluate the PM intervals dynamically by minimizing the maintenance cost rate over a life cycle under the reliability constraint where two imperfect maintenance actions: maintenance repair and replacement, are taken into account; at the system-level, a variable maintenance task window (VMTW) method is applied to dynamically rearrange maintenance actions when the maintenance opportunities arise. The novel strategy can effectively reduce the PM cost and avoid unnecessary depot maintenance. Its efficiency is validated by a comparison with traditional optimal preventive maintenance schedule derived from the genetic algorithm.

Economic and Reliable Preventive Maintenance Scheduling in Power Systems by Using Binary Crow Search Algorithm

The generation system is an important part of the power system. The problem of generator maintenance scheduling is provided to construct optimal generators preventive maintenance schedules. It aims to improve economic benefits and achieve reliable operation of the power system while satisfying the system and maintenance constraints. In this paper, the binary crow search algorithm is proposed for solving the scheduling problem. This model would schedule maintenance scheme and commitment status of generating units while the objective functions are achieved. The crow search algorithm is a new meta-heuristic optimizer, which has its implementation very simple and easy compared to other optimization techniques. To verify the robustness and effectiveness of the proposed binary crow search optimizer, three test systems namely 6–unit, 21–unit system, and IEEE reliability test system are considered over the planning horizon of 52 weeks. The proposed optimizer is implemented in the MATLAB programming environment. Techno-economic aspects are considered for the generator's maintenance scheduling problem as reliability enhancement economic cost-minimizing issues. The proposed binary crow search optimizer is developed for single and multi-objective frameworks. The simulation results show the proposed binary crow search technique effectiveness and feasibility compared with previously optimizer in solving the generators maintenance scheduling problem with better convergence rate.

SIMULASI MONTE CARLO DALAM OPTIMASI BIAYA PEMELIHARAAN

Unplanned maintenance can result in high costs. This study uses a Monte Carlo simulation to minimize maintenance costs, where the failure cost and the preventive cost can be balanced. This research was conducted in a medium-sized company that produces shoe soles. The object of research is focused on the critical components of a rotary injection molding machine. The research data is obtained from the company's factual data in the form of historical machine breakdowns, labor costs, component costs, and production loss costs. The results demonstrate that the optimal preventive maintenance schedule can save the rate of maintenance costs for each critical component by 24.54%, 54.63%, and 21.51% compared to actual maintenance.

Optimizing the Preventive Maintenance Scheduling Based on Dynamic Deterministic Demand in The Cement Manufacturing

Preventive maintenance is a planned and scheduled maintenance method that is carried out before a machine failure occurs. The maintenance schedule can be determined based on experience, historical data, or recommendations. Selecting the maintenance schedule greatly affects the production system. The Clin machine in cement manufacturing has an important role in the cement production process. During treatment, the client machine cannot produce clinker, so it is necessary to plan a production system to meet the demand. This paper aims to design an optimization model for determining the preventive maintenance schedule for cement manufacturing by considering the production process and inventory control. Mathematical models with binary options are used to model that system. The model supports showing the optimal preventive maintenance schedule for the ciln machines with a binary decision each period. This mathematical model describes the interaction of production planning, inventory control, and scheduling of total maintenance on a kiln machine. The goal of this system is to determine the optimal preventive maintenance schedule with minimum costs. In addition, the system's output is the optimal production and inventory decision rule for each period. Based on the analysis and simulation of the model with the deterministic and dynamic demand, the optimal preventive maintenance schedule is in the 9th and 21st periods. The kiln machines are maintained every July with minimal costs. The model scenario shows the interaction of the variables and the sensitivity of the production capacity and demand to the decision rule of the variable.

Optimising preventive maintenance schedule for a distillery plant

In today's era of automation, the maintenance of complex systems is necessary for obtaining high payback ratios. Time has changed the thinking of plant/maintenance managers from the fix-it-broken approach to the preventive maintenance approach for bringing back deteriorated components/systems to predetermined operational conditions. But since resources are limited therefore it is required to achieve an effective maintenance approach to minimise the total maintenance cost and equipment downtime. This paper presents the framework for deciding the optimal schedules for preventive maintenance under constraints of the maintenance cost, availability and revenue generation. The programming package Mathematica is used to solve the complex equations of the framework and finding the optimal preventive maintenance schedule. The practical case of a distillery plant is considered to gauge the effectiveness of this approach.

A Tri-Objective Model for Generator Maintenance Scheduling

The complexity of power systems is increasing as new generating units are added to power systems in order to supply power to the growing economies. This has resulted in further research into the generator maintenance scheduling (GMS) problem which seeks to ensure optimal preventive maintenance scheduling that is effective and reliable. This research is focused on developing a generator maintenance schedule using a tri-objective model. The GMS tri-objective model is solved using two solution methodologies. The first is an exact solution method using mathematical modelling software, Advanced Interactive Multidimensional Modelling System (AIMMS). The second solution method is a recently developed metaheuristic algorithm called Exchange Market Algorithm (EMA). Results show that the tri-objective model finds a trade-off solution of the individual solution methods. The metaheuristic algorithm gives a better solution for larger optimization problems.

Optimal preventive 'maintenance-first or -last' policies with generalized imperfect maintenance models

<p style='text-indent:20px;'>This paper presents modified preventive maintenance policies for an operating system that works at random processing times and is imperfectly maintained. The system may suffer from one of the two types of failures based on a time-dependent imperfect maintenance mechanism: type-Ⅰ (minor) failure, which can be rectified by minimal repair, and type-Ⅱ (catastrophic) failure, which can be removed by corrective maintenance. When the system needs to be maintained, two policies "preventive maintenance-first (PMF) and preventive maintenance-last (PML)" may be applied. In each maintenance interval, before any type-Ⅱ failure occurs, the system is maintained at a planned time <inline-formula><tex-math id="M1">\begin{document}$ T $\end{document}</tex-math></inline-formula> or at the completion of a working time, whichever occurs first and last, which are called PMF and PML, respectively. After any maintenance activity, the system improves but its failure characteristic is also altered. At the <inline-formula><tex-math id="M2">\begin{document}$ N $\end{document}</tex-math></inline-formula>-th maintenance, the system is replaced rather than maintained. For each policy, the optimal preventive maintenance schedule (<inline-formula><tex-math id="M3">\begin{document}$ T $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M4">\begin{document}$ N $\end{document}</tex-math></inline-formula>)<inline-formula><tex-math id="M5">\begin{document}$ ^{*} $\end{document}</tex-math></inline-formula> that minimizes the mean cost rate function is derived analytically and determined numerically in terms of its existence and uniqueness. The proposed models provide a general framework for analyzing the maintenance policies in reliability theory.

Optimal Inspection and Preventive Maintenance Scheduling of Mining Equipment

A typical mining company has three important assets: the human labor-force, the orebody, and the equipment. Trucks, excavators, drilling machines, crushers, grinders, classifiers, and concentrators represent the equipment. Mining operations that want to take advantage of economies of scale have huge equipment fleet, and the worth of the equipment could be more than a hundred million dollars. The reliability and availability of this equipment play critical roles in increasing the efficiency and productivity of a mining operation. This paper proposes an effective maintenance management approach to be used in the mining industry such that equipment availability and reliability are improved while potential failures are prevented. Using failure data of a mining truck fleet in an open-pit Canadian mining operation, a case study is conducted in two steps. The first step focuses on determining optimal inspection intervals based on the desired reliability level in such a way as to detect potential catastrophic failures that represent considerable maintenance and downtime costs. The second step develops a preventive maintenance scheduling plan that differentiates between physical and virtual age considering the degradation of systems and their rejuvenation after each repair. The research outcomes show that the proposed approach has the potential to increase the benefit obtained from the mining equipment and can be used in mining operations.

Adaptive Preventive Maintenance for Flow Shop Scheduling With Resumable Processing

In this article, we focus on a joint scheduling problem that considers the corrective maintenance (CM) due to unexpected breakdowns and the scheduled preventive maintenance (PM) in a generic $M$ -machine flow shop. The objective is to find the optimal job sequence and PM schedule such that the total of the tardiness cost, PM cost, and CM cost is minimized. Currently, most existing studies on the PM schedules are based on a fixed PM interval, which is rigid and may lead to poor performance, as the fixed strategy fails to effectively balance the trade-offs between the production scheduling and maintenance. To address this critical research issue, our novel idea is to dynamically update the PM interval based on the real-time machine age, such that the maintenance activity coordinates with the job scheduling to the maximum extent, which results in an overall cost saving. Specifically, a correction factor is introduced to dynamically update the PM interval and to help evaluate whether it is worthwhile to process the job first at the risk of the CM before performing the PM action. To demonstrate the effectiveness of the adaptive strategy, simulations and a case study on mining operations are conducted to show that the adaptive strategy outperforms the existing methods with a less total cost. Note to Practitioners —This article is motivated by the critical problem of balancing the trade-offs between production scheduling and maintenance in a flow shop production line, where jobs are processed on the machines in the same route. On the one hand, production scheduling aims to meet the customer demands on time. On the other hand, the maintenance actions help restore the machines’ reliability by reducing the machine failure rate. Most existing approaches to scheduling the PM are based on the fixed PM interval, which is rigid and may lead to poor performance. Although the nonperiodic PM interval strategy has been proposed in the literature for production scheduling, it still does not consider the adaptive PM strategy in the context of flow shop scheduling with multiple machines. To fill this literature gap, in this article, we first model a generic $M$ -machine flow shop considering both the CM and PM. Then, we suggest a new adaptive and easy-to-implement approach to dynamically update the PM interval and improve the decision-making on the PM schedules. Compared with the conventional maintenance policy, the adaptive one greatly reduces the total cost in real-time scheduling.

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

Transformer preventive maintenance scheduling—as an integral part of the power utility company’s asset–management process—if optimized properly affords considerable savings in maintenance costs and results in the increased reliability of transformer stations. However, situation is exacerbated by the considerable uncertainty involved in the assessment of the transformer health status, not necessarily associated with its age, as well as in the projected asset replacement and maintenance costs. There is also uncertainty inherent in the transformer tests as such. This paper proposes a novel transformer optimal preventive maintenance scheduling approach, with the use of the Bayesian statistical learning and a generalization of the Bayesian networks, for decision-making based on the expected utility hypothesis. Proposed approach, in essence, manages risk associated with taking different preventive actions by leveraging uncertain information regarding the transformer health and the expected costs of these actions. In this way, each prospective decision has a monetary value attached to it, which depends on the particular circumstances defined by the power utility company. Time-related effects are introduced into the decision-making process by the Bayesian updating of the prior belief concerning the transformer health with new information. Solving a decision problem amounts to determining an (optimal) strategy that maximizes the expected utility for the decision maker. On top of that, Bayesian approach, uniquely, offers explicit account for the full uncertainty associated with every step of the decision-making process. The proposed approach, and its advantages, will be demonstrated on a fleet of power transformers. It will be shown that model can predict with a 90% accuracy and a high confidence the transformer health index class from a small dataset. It will also be shown that it can predict with high expected profits which transformers in the dataset would benefit the most from the early intervention (recuperating around 25% of the projected costs and retaining over 90% of the projected savings).

An integrated model of production scheduling, maintenance and quality for a single machine

It is well known that production scheduling, maintenance and process control decisions affect each other. Hence it is both essential and prudent to optimize them jointly. This paper develops a model that integrates and optimizes production, maintenance and process control decisions simultaneously for a single machine. Both types of maintenance, preventive and corrective, are taken into consideration. The methodology starts by developing an optimal preventive maintenance schedule. Then an integrated model that determines the decision variables and optimizes the total cost per unit time, resulting from production scheduling, inventory holding and maintenance and process control is developed. The utility of the model is demonstrated using an example from the literature. Systematic sensitivity analysis demonstrated that the model results are robust. Also; the model results are compared with the results of optimizing each system separately and with two-way integration. The comparisons indicate that the developed model results in a saving that ranged from 2.62 to 6.78 percent. The model provides a viable approach for optimizing production scheduling, maintenance and quality jointly.

A Condition-based maintenance and spare parts provisioning based on markov chains

Machine is a vital tool of the company in helping the production process. Every company expects the production to run smoothly, but sometimes it is hampered by damage that happened to the machine, so that the production process is disrupted and causes losses to the company. Engine damage can be minimized by regularly evaluating the condition of the spare parts. In practice, if the spare parts inventory policy is not accurate it will cause stock outs or overstocks, which can lead to more costs for the company. The worst case is if there is no spare part stored in the warehouse when it is needed, it can make the production floor stopped, which in the end makes the company can’t fulfil their production target. This research aims to obtain an optimal preventive maintenance schedule by calculating the machine’s reliability and inventory provisioning policy for the spare parts according to predicted amount that will be needed in the future calculated using Markov chains so the company can determine the reorder point (r) and the economic order quantity (EOQ).

On optimal preventive-maintenance policy for generalized Polya process repairable products under free-repair warranty

This paper focuses on analyzing the cost behavior of a free-repair warranty (FRW) policy with a preventive maintenance (PM) schedule for a complex product. We utilize a generalized Polya process to model the more general failure and repairs during the FRW coverage period. The expected cost function of the FRW with PMs is derived. We firstly prove that a unique optimal PM schedule exists for a FRW with a finite number of PMs, and then propose a search algorithm for computing the optimal number of PMs and their implementation times for a given FRW coverage period. A certain amount of numerical analysis is conducted to gain further insights into the FRW with PMs. Owing to the significant cost-saving advantages of scheduling PMs for the FRW, our study offers an important analytical tool for marketing managers in the design of more competitive and cost-effective warranty policies for complex and durable products.

Production and maintenance strategies for parallel machines with load transfer in case of failure

PurposeThe purpose of this paper is to deal with the problem of integration of production and maintenance policies. In this context, the authors consider production systems made of parallel machines producing a single product over a finite horizon made of equal periods for which a forecasted demand is known. The authors investigate the impact of switching production in case of failure of any given machine.Design/methodology/approachA mathematical model is first developed to find an optimal production plan which minimizes the average total storage, shortage and production costs. Then, using this optimal production plan and taking into account the influence of the production rate on the degradation of each machine, optimal preventive maintenance (PM) policies are proposed for the situations with and without switching.FindingsOptimal production rates are determined for each production period and for each machine. Optimal PM periods are also computed for each machine.Practical implicationsUsually, in manufacturing systems, the production rate of a machine influences its failure rate. In case a machine fails, it takes a random time to repair it during which production is lost. The paper attempts to propose a switching policy (SP) according to which the lost production is compensated by all the other machines. The effects of the SP coupled with the PM strategy are shown through a numerical example.Originality/valueContrarily to previous works, the authors consider more realistic settings with a non-negligible random time for repairing failed machines. In order to compensate the lost production during the repair of a failed machine, a SP is proposed to transfer the load uniformly to all the other machines. As a result, those machines will produce at a higher production rate and will consequently have their failure rate increased. It will therefore be essential to determine an optimal PM schedule knowing that durations of these activities are not negligible. It is shown that the simultaneous implementation of periodic PM and load transfer in case of failure is the most economical integrated strategy.