Periodic Preventive Maintenance Research Articles

A study on two‐dimensional warranty with a preventive maintenance policy under burn‐in or run‐in

AbstractProducts are often offered with warranties, which have become a common marketing strategy intended to indicate that product quality is guaranteed by the firm. Warranties can attract consumer purchases, but they also incur warranty costs, especially when firms falsely claim that their products are high quality; therefore, the determination of an appropriate warranty policy is of particular importance. This study considers a two‐dimensional warranty for complex repairable products, such as a vehicle, a lathe, or industrial machinery, with consideration of (1) burn‐in (run‐in), which is a supervised accelerating testing process performed before product launch to prevent defective products from being sold on the market, and (2) periodic preventive maintenance, which is performed during the usage period to prevent the occurrence of unexpected failures under normal usage conditions. The optimal burn‐in (run‐in) time and preventive maintenance intervals are determined with the aim of minimizing warranty costs for firms. The results of the study show that given a higher repair cost and a greater infant mortality failure rate, more frequent preventive maintenance is preferable, and the repair cost has the largest effect on the total warranty cost for the firm.

Optimization of a preventive maintenance strategy for a product with limited repair times

This paper proposes a base warranty and replacement warranty strategy for repairable products. It should be emphasized that the product failure rate is low in the early stage of the base warranty. Therefore, in order to reduce the manufacturer’s maintenance cost, the manufacturer does not carry out preventive maintenance in the early stage of the base warranty, and carries out regular preventive maintenance in the later stage of the base warranty. At the same time, if the number of product failures in the base warranty exceeds the specified limit, the manufacturer must replace products for customers free of charge according to the warranty contract. After the product is replaced, if the number of product failures does not exceed the limit, the manufacturer will continue to provide customers with the remaining base warranty period of the original product, and the warranty period will continue to be extended for a period of time. By minimizing the expected cost rate, it is shown that the optimal maintenance policy for warranty products is unique. Finally, we give a numerical example and study the effects of periodic preventive maintenance and other relevant parameters on the optimal solution.

Mathematical programming formulations for the reclaimer scheduling problem with sequence-dependent setup times and availability constraints

In the context of Industry 4.0, which encompasses advanced technologies and interconnected systems, the integration of optimization techniques assumes a crucial role in addressing resource management challenges across various sectors, including manufacturing systems. In bulk ports, which are an integral part of the manufacturing and logistics chain, one of the most critical resource management challenges is the scheduling of automated or semi-automated reclaimers. These machines are responsible for reclaiming dry bulk materials for loading onto vessels using ship-loaders. The efficient operation of reclaimers directly impacts the overall efficiency of the port. However, the current works for the reclaimer scheduling problem (RSP) overlook the necessity of maintenance activities and assume continuous machine availability. Nonetheless, the inclusion of preventive maintenance activities is critical to ensuring optimal scheduling decisions that minimize production disruptions and downtime. To address these challenges, this paper proposes a novel approach that considers periodic preventive maintenance activities of the reclaimers. We consider two cases to tackle this problem. The first case involves the optimization of material handling within a system of two stockpads and one reclaimer machine. For this case, two novel Mixed Integer Programming (MIP) formulations are developed to drive efficient scheduling decisions and maximize productivity. Furthermore, for the second case representing a more complex system comprising three stockpads and two reclaimers, a novel MIP formulation is devised. The effectiveness of the proposed mathematical formulations is rigorously tested through the use of randomly generated instances based on a real-world case.

Optimal external opportunistic maintenance for wind turbines considering wind speed

ABSTRACT A novel optimal opportunistic maintenance strategy for wind turbines considering wind speed as a stochastic process is proposed in this paper. Under this strategy, periodic preventive maintenance is conducted to reduce the probability of downtime due to failure, whereas external opportunities are leveraged to reduce the cost of preventive maintenance. This combined maintenance approach can also reduce costs associated with the wind turbine lifecycle. The formulated expected costs per unit time considered two scenarios, i.e. regular operation and energy generation interruption, to obtain the optimal solution via a genetic algorithm. Furthermore, we illustrated the economic advantages of the proposed strategy by comparing various maintenance strategies, as well as a sensitivity analysis to determine the effects of the different parameters in the proposed model. Experimental results demonstrate that the proposed opportunistic maintenance strategy is more cost-effective for wind turbines than comparable methods, with an optimal solution that varies according to variations in wind speed.

Markov-chain-driven optimization of inspection-based maintenance, Part II: Numerical analysis and practical insights

These two-part paper series aim to optimize the rates of inspection-based maintenance or routine-test on power system equipment. Part I introduced a novel probabilistic model using Markov chains for the inspection-based maintenance and derived the relationships between the transition rates. Part II demonstrates how the proposed method can effectively address budget and data availability constraints. It also presents the derived relationships between the optimal preventive maintenance rate and risk indices. The results presented in this paper are based on an industrial project conducted to optimize the preventive maintenance period of high-voltage equipment in Iran. Additionally, this companion paper provides practical insights, including implementation remarks, and a comprehensive dataset for failure and defect analysis, which proves useful for future research and applications.

Optimal condition-based maintenance policy for leased equipment considering hybrid preventive maintenance and periodic inspection

Innovations in technology have made the structure of leased equipment more complex, which poses a challenge for lessors to develop maintenance policies. In view of the situation that the single periodic preventive maintenance (PM) policy during leasing activities may cause excessive or insufficient maintenance on the leased equipment, a condition-based maintenance (CBM) policy considering hybrid preventive maintenance (HPM) and periodic inspections is proposed. This policy contains imperfect preventive maintenance (IPM), preventive replacement (PR) and corrective maintenance (CM). The lessor can decide to take maintenance actions depending on whether the state of the equipment reaches the corresponding maintenance threshold. The warranty period of the leased equipment is also considered. To seal the deal, the lessor will give a discount on the rental to cover the CM costs and penalties out of the warranty period. To minimize the lessor's costs, we propose a CBM decision model to determine the optimal inspection period, PM threshold and PR period. The results show that the HPM policy can reduce leasing costs by about 5 –15 % for the lessor compared to that of the single PM policy.

Improvement of methods for analyzing the technical condition of electromechanical equipment of nuclear power plants

The article analyzes the problem of diagnostics of NPP electric drive valves. Failure of this type of equipment can become the initial event for design or beyond design accident. Therefore, during the period of preventive maintenance at NPPs diagnostics of up to 2000 units of valves is carried out mainly by registration and analysis of the motor current signal during opening and closing. However, the applied statistical and frequency analysis methods do not always provide the necessary quality of diagnostics. The article points out the disadvantage of the applied methods – insensitivity to the dynamics of electromechanical equipment in terms of nonlinear manifestations of defects. As a tool for analyzing these manifestations the phase-plane method is proposed and its theoretical basis is outlined. The diagnostic signal is described as a vector diagram of a harmonic series subjected to amplitude and phase modulation. The most characteristic forms of phase trajectories - cardioids, nephroids, epicycloids – are shown. The main advantages of the phase-plane method over traditional frequency methods – sensitivity, possibility to identify the type of modulation, phase shift, dynamic and chaotic manifestations – are demonstrated by examples of processing diagnostic signals of NPP equipment. An important advantage of signal representation in the form of phase portrait is the visualization of dynamic features of the signal. The result of the work is a new method that allows to extract additional information about the object state by analyzing the non-stationary component of the initial data. It is proposed to introduce the phase-plane method into the practice of processing diagnostic signals of electric drive valves and other electromechanical equipment operated at NPPs.

Simultaneous optimization of design and maintenance for systems using multi-objective evolutionary algorithms and discrete simulation

When projecting and building new industrial facilities, getting integrated design alternatives and maintenance strategies are of critical importance to achieve the physical assets optimal performance, which is needed to be competitive in the actual global markets. Coupling Evolutionary Algorithms with Discrete Event Simulation has been explored both in relation to systems design and their maintenance strategy. However, it was not simultaneously considered when both the corrective and the preventive maintenance—consisting of achieving the optimum period of time to carry out a preventive maintenance activity—are taken into account before being considered by the authors of the present paper. This work couples Multi-objective Evolutionary Algorithms with Discrete Event Simulation in order to enhance the knowledge and efficiency of the methodology presented, which consists of exploring and optimizing simultaneously systems design alternatives and their preventive maintenance strategies. The aim consists of finding the best set of non-dominated solutions by using the system availability (first maximized objective function) with taking into consideration associated operational cost (second minimized objective function), while automatically selecting the system devices. Each solution proposed by the Multi-Objective Evolutionary Algorithm is analyzed by using Discrete Event Simulation in a procedure that looks at the effect of including periodic preventive maintenance activities all along the mission time. An industrial application case study is solved, and a comparison of the performance of five state-of-the-art and three more recently developed Multi-objective Evolutionary Algorithms is handled; moreover, the gap in the literature reviewed about the analysis regarding the effect of the discrete event simulation sampling size is faced with useful insights about the synergies of Multi-objective Evolutionary Algorithms and Discrete Event Simulation. Finally, the methodology is expanded to more complex systems which are successfully solved.

Unrelated parallel machine scheduling models with machine availability and eligibility constraints

In this paper, we address the unrelated parallel machine scheduling problem with calendar, which consists of a generic intermittent availability scheme with non-regular active and inactive time windows in sequence. The problem additionally considers machine eligibility and release date constraints, as well as the possibility of pausing or not the processing of the jobs between successive active time windows. This problem is quite frequent and relevant in several practical settings in which it is necessary to schedule parallel resources with different calendars, such as different weekly and daily shifts, non-regular not worked days, holidays, and preventive maintenance periods, among others, while considering the resources eligibility. We present novel mixed integer linear programming models for this problem with the aim of minimizing the makespan. Basic formulations of these models are presented as well as tight lower bounds for the objective, including enhanced formulations. To the best of our knowledge, there are no other works that tackled this problem and that proposed optimization models with the level of detail and generality here considered. The models presented are evaluated using a state-of-the-art general-purpose solver, and the computational tests are performed on a variety of instances. The results validate the models and show that they are can handle instances in realistic situations where the problem occurs. The results also show that the enhanced formulations are able to find several optimal solutions, and that within acceptable computational times they are able to sharply reduce the optimality gaps when compared to the more basic formulations.

Integrating preventive and predictive maintenance policies with system dynamics: A decision table approach

A decision table is a practical tool that helps systems planners to make operational decisions, especially when they are under stress. With the effect of recent trends, such as the use of machine learning, data mining, and reinforcement learning methods, the maintenance decision has been a dynamic issue depending on system conditions. An expert may execute the maintenance or wait for the next periodic maintenance due to lack of maintenance workers, tools or budget, resources, etc., although the intelligent method predicts a failure approaching. Even sometimes, he/she may ignore the current periodic maintenance. Our method allows making some changes in the maintenance plan systematically. It integrates the results of preventive and predictive maintenance policies, and as different from the literature, it allows ignoring some maintenance actions depending on the maintenance resource levels in a decision table. Such a strategy helps to allocate limited resources to maintenance actions reasonably. We conducted an extensive simulation study on a real-life dataset. The preventive maintenance period is determined using classical approaches such as Weibull analysis. A machine learning algorithm is utilized to predict the type of failure. We have analyzed the performance of the proposed decision table approach under a variety of scenarios and with different parameter settings. We also showed the effect of parameter settings and the marginal utility of each maintenance policy. In addition, the approach provides several choices for planners. As a result, the proposed approach improves the system’s sustainability compared to traditional policies.

A New Mathematical Model for Economic Ordering with Preventive Maintenance and Reworking in a Supply Chain

Cost reduction in production systems is one of the main concerns of most manufacturing industries. Achieving this goal requires the effective use of resources. Nowadays, due to the competitiveness of production and the need to reduce costs and deliver goods on time, equipment availability and prevention of unexpected stops are particularly important in industrial units. In recent years, equipment, machinery, and human resources have been the foundations of any organization. On the other hand, to increase the productivity and efficiency of providing services and achieve global standards, special attention should be paid to increase the efficiency of machinery and reducing downtime costs. This research conducted a mathematical formulation to find the best possible solution for the economic production quantity and inventory ordering by considering preventive maintenance. In this model, the success of organizations in providing services and increasing the quality depends on various reasons, such as maintenance and repair systems. In the proposed model, the defective items are considered, and the goal is to achieve an optimal amount of production in such a way that the costs of the entire system, including production cost, setup, maintenance, inspection, and rework costs, are minimized during a period per unit of time. The numerical results show that increasing the preventive maintenance periods leads to an upward trend in the total cost of the supply chain. Moreover, the sensitivity analysis shows that the longer the preventive maintenance on the machine, the higher reliability of the system as well as total costs will be achieved.

Optimal preventive maintenance policy for leased equipment for free with minimum quantity of consumables purchasing commitment by the lessee

PurposeThis paper deals with a particular type of leasing contracts according to which an equipment is leased for free with the condition for the lessee to purchase a predetermined minimum quantity of consumables during each leasing period. Maintenance actions are performed by the lessor and borne by him. Imperfect preventive maintenance is carried out every t time units throughout the leasing period. Minimal repairs are performed following equipment failures. At the end of the leasing period, an overhaul which restores the equipment to “as good as new” state is performed. The equipment is leased many times during its life cycle. The purpose of this paper is to determine the values of the decision variables for the lessor, which are the preventive maintenance (PM) period and the minimum quantity of consumables to be sold to ensure profit.Design/methodology/approachA mathematical model is developed to express the expected maintenance cost per time unit incurred by the lessor as well as his expected profit over the equipment life cycle. The optimal PM period minimizing the maintenance cost is determined first. Then, given the corresponding minimum maintenance cost, the minimum quantity of consumables (the lessor's break-even point) to be purchased by the lessee is computed. A numerical example and a sensitivity study are presented, and the obtained results are discussed.FindingsThe outcome of this work is supposed to help the lessors determining two key values to be included in each leasing contract, namely: (1) the periodicity according to which they will commit to perform preventive maintenance actions such that their average total cost of maintenance is minimized, (2) the minimum quantity of consumables that the lessee must commit to purchasing during the leasing period. This quantity must be between the break-even point and the maximum quantity associated with the capacity of the equipment.Practical implicationsPractically, the objective of this work is first to determine the optimal strategy to be adopted by the lessor in terms of effort relating to PM and second to determine the minimum quantity of consumables that the lessee must purchase during the leasing period such as profit is insured for the lessor.Originality/valueThis type of leasing (for free) has not been addressed in the literature particularly when considering maintenance strategies.

Mixed integer linear programming formulations for the permutation flow shop scheduling problem with preventive maintenance activities

Integrating the maintenance activities into the production process allows for keeping/restoring the good functional state of machines and improving the system productivity. According to the maintenance strategy adopted by a company, maintenance activities can be carried out in fixed positions/intervals or can be flexible, with an optimised maintenance approach being the key to efficient production maintenance. Consequently, the scheduling problems with maintenance activities (machine availability constraints) have received an increasing attention for both cases of fixed and flexible maintenance activities. This paper investigates the flow shop scheduling problem in the non-resumable case using an arbitrary number of preventive maintenance activities on each machine, with the optimised criterion being the makespan. Three mixed integer linear programming (MILP) models are proposed for three different problem types with respect to the considered maintenance policies within fixed-time windows, after a fixed number of finished jobs, and within an optimal preventive maintenance period. The computational results based on the randomly generated test problems demonstrate the high performance of the proposed MILP models in solving a wide range of instances, particularly, in small- and medium-scale problems.

Medical Devices Readiness in Private Outpatient Care Facilities in Riyadh within the Saudi Healthcare System

A significant transformation is observed in the Saudi healthcare system in response to the Saudi 2030 vision. Outpatient Care Facilities (OCF) are expected to carry more duties in providing healthcare services. The current study aims to analyze the readiness of medical devices in private outpatient care facilities (OCFs) in Riyadh. An on-site questionnaire-based study was conducted to assess medical equipment maintenance and its readiness in private OCFs in different regions of Riyadh, Saudi Arabia. A questionnaire was formulated following local and international guidelines and standards. The response rate to the study was 100%. Types of OCF were classified into general and specialized polyclinics, medical clinics, laboratories, and physiotherapy centers. The results of the questionnaire reveal that only 29.2% OCF have a medical device maintenance department, a periodic preventive maintenance protocol was present in 54.2% OCF, and only 12.5% OCF inspect their medical devices regularly. A lack of essential medical equipment maintenance practices was observed in the private OCF in Riyadh. Private OCF should comply with best practices in medical equipment rather than ignore Original Equipment Manufacturer (OEM) instructions, and local and international medical device maintenance guidelines. Private OCFs in Riyadh should restructure their medical equipment practices.

Bi-objective unrelated parallel machine joint scheduling of jobs and preventive maintenance with a dynamic speed-scaling technique

Nowadays, industries contribute to a substantial consumption of energy. This consumption leads to significant greenhouse gas emissions, energy dependence and ever-increasing costs for manufacturers. Controlling consumption and improving energy efficiency in industry are therefore major challenges in terms of environmental issues. In this context, this article examines an energy-efficient bi-objective unrelated parallel machine joint scheduling of jobs and preventive maintenance activities problem to minimize both makespan and total energy consumption. The parallel machines are speed-scaling. To solve the problem, we propose a bi-objective mixed integer linear programming (MILP) model. Since the problem involves assigning jobs to machines and selecting an appropriate processing speed level for each job, we characterize each individual by two vectors: a job-machine assignment vector and a speed vector. To the best of our knowledge, no papers considering both production scheduling and Preventive Maintenance periods with minimizing the bi-objective makespan and the total of energy consumption. The gap between theory and practice prevents the satisfying performance of the schedules in industry. The purpose of this paper is to establish a joint model for integrating the production and PM scheduling to optimize the bi-objective of the total of energy consumption (TEC) and makespan (Cmax) simultaneously; and to make the theory applied in practice more efficaciously. The performance of the proposed mixed binary integer programming model is evaluated based on the exact method of Branch and Bound integrated on the CPLEX solver plus analyses using MATLAB software. A study of the results proved the performance of the model developed.

Joint Maintenance Strategy Optimization for Railway Bogie Wheelset

A wheelset is one of the most severely worn components of railway bogies. Its health condition has a significant impact on the safety and comfort of railway trains. Moreover, wheelset maintenance costs account for a sizeable part of the railway operating company. Therefore, it is essential to investigate executable maintenance strategies for wheelsets. In this paper, a maintenance strategy that combines periodic inspection and preventive maintenance is proposed. The wheel flange and wheel diameter deterioration models are established by the compound Poisson process based on the cumulative shock from the failure mechanism, and the Weibull distribution is adopted for modeling wheel tread failure probability. The age reduction factor is introduced to describe the maintenance effect. Then, the joint maintenance optimization model is constructed to determine the periodic inspection interval and the reprofiling strategy, with the objective of achieving a minimum maintenance cost rate and with the wheel flange thickness failure as the failure risk constraint. Lastly, a case study is provided, and the results show that, compared with the two conventional maintenance strategies with fixed inspection periods, the maintenance strategy proposed in this paper can reduce the maintenance cost rate by 27.06% and 12.0%, respectively. Moreover, the life span is prolonged by 11.51% and 11.98%, respectively.

MATHEMATICAL MODEL OF MAINTENANCE OF SAMPLES OF WEAPONS AND MILITARY EQUIPMENT USING THE DISTRIBUTION OF TIME OF TROUBLE-FREE OPERATION OF PRODUCTS IN THE FORM OF WEIBUL'S LAW

The article considers the mathematical model of maintenance of samples of armaments and military equipment according to the state with parameter control, for which the distribution of time of trouble-free operation of weapons in the form of Weibul's law is used as a model of failures. Probable physical distributions have a certain advantage over simply probable distributions, because their parameters can be determined on the basis of both statistical characteristics of failures and analysis of failures of the physical process. In addition, diffusion distribution laws, such as diffusion-monotone and diffusion-nonmonotonic distributions, are now the most relevant. These facts have significant practical value, which determines the relevance of the research. Also, the article considers the dependences of the coefficient of technical use of the object on different characteristics of the service system and the object itself are determined. For the mathematical description of the processes inherent in the maintenance system, the apparatus of semi-Markov random processes in the classical representation is used. Weibul's distribution law is one of the least used due to its complexity. The main results of the work are obtained by numerical method. The dependences obtained as a result of the calculations show the existence of the optimal period of preventive maintenance of the object of control, in which the maximum value of its coefficient of technical use is achieved. The result of the work allows to establish the quantitative dependence of the coefficient of technical use on the reliability of control means, scale and form in the form of Weibul's distribution law in a wide range of periodicity of preventive maintenance.

A Method for Safety Evaluation of Train Braking System Considering Multiple Types of Preventive Maintenance Cycles

The train braking system with redundant components has an essential effect on driver safety. In this paper, we consider the failure of K-out-of-N brake motors during operation and the redundant recovery by preventive maintenance. Two Continuous Time Markov Chains (CTMC) are used to model the working process and the preventive maintenance process of the braking system corresponding to the real situation of the train. Considering the maintenance effect of intermittent daily and cyclical overhaul with nested relationships, a new operation risk assessment model of the K-out-of-N system is developed to evaluate the effectiveness of the preventive maintenance of the train. Some safety parameters are solved based on the two coupled preventive maintenance periods, which conveniently design the safety of the train braking system. Finally, a case study illustrates the effectiveness of the safety evaluation method. The results show that we can trade off the effects of the multiple PM intervals on train safety considering the redundancy structure of the braking system by the proposed model.

Deep Reinforcement Learning for Distributed Flow Shop Scheduling with Flexible Maintenance

A common situation arising in flow shops is that the job processing order must be the same on each machine; this is referred to as a permutation flow shop scheduling problem (PFSSP). Although many algorithms have been designed to solve PFSSPs, machine availability is typically ignored. Healthy machine conditions are essential for the production process, which can ensure productivity and quality; thus, machine deteriorating effects and periodic preventive maintenance (PM) activities are considered in this paper. Moreover, distributed production networks, which can manufacture products quickly, are of increasing interest to factories. To this end, this paper investigates an integrated optimization of the distributed PFSSP with flexible PM. With the introduction of machine maintenance constraints in multi-factory production scheduling, the complexity and computation time of solving the problem increases substantially in large-scale arithmetic cases. In order to solve it, a deep Q network-based solution framework is designed with a diminishing greedy rate in this paper. The proposed solution framework is compared to the DQN with fixed greedy rate, in addition to two well-known metaheuristic algorithms, including the genetic algorithm and the iterated greedy algorithm. Numerical studies show that the application of the proposed approach in the studied production-maintenance joint scheduling problem exhibits strong solution performance and generalization abilities. Moreover, a suitable maintenance interval is also obtained, in addition to some managerial insights.

Optimal Periods of Conducting Preventive Maintenance to Reduce Expected Downtime and Its Impact on Improving Reliability.

The present research mainly aims to use a mathematical formula to determine the optimal intervals for conducting preventive maintenance operations for machines to reduce the expected failure time when the malfunction data follow the Weibull distribution. The reliability function, failure rate, and the average time between machine failures were derived after performing preventive maintenance operations and before conducting preventive maintenance operations to state the amelioration that happens to machines. These rely on real data of performing preventive maintenance operations and the downtime required to repair machine or device faults that occur between preventive maintenance periods and the downtime necessary to perform preventive maintenance operations on the machine or device. Thus, the study concluded that preventive maintenance operations are working to increase the reliability of the machine and improve it, as well as to increase the average period of time for the machine to operate between faults.