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Suspension bridges are critical components of transport infrastructure around the world. Therefore, their operating conditions should be effectively monitored to ensure their safety and reliability. However, the main cables of suspension bridges inevitably deteriorate over time due to corrosion, as a result of their operational and environmental conditions. Thus, accurate annual corrosion rate predictions are crucial for maintaining reliable structures and optimal maintenance operations. However, the corrosion rate is a chaotic and complex phenomenon with highly nonlinear behavior. This paper proposes a novel predictive model for the estimation of the annual corrosion rate in the main cables of suspension bridges. This is a hybrid model based on the multilayer perceptron (MLP) technique optimized using marine predators algorithm (MPA). In addition, well-known metaheuristic approaches such as the genetic algorithm (GA) and particle swarm algorithm (PSO) are employed to optimize the MLP. In order to implement the proposed model, a comprehensive database composed of 309 sample tests on the annual corrosion rate from all around the world, including various factors related to the surrounding environmental properties, is utilized. In addition, several input combinations are proposed for investigating the trigger factors in modeling the annual corrosion rate. The performance of the proposed models is evaluated using various statistical and graphical criteria. The results of this study demonstrate that the proposed hybrid MLP-MPA model provides stable and accurate predictions, while it transcends the previously developed approaches for solving this problem. The effectiveness of the MLP-MPA model shows that it can be used for further studies on the reliability analysis of the main cables of suspension bridges.

This paper proposes an optimal maintenance and warranty policy for the repairable second-hand product incorporating a periodic preventive maintenance strategy to slow down the deterioration of the product and derives an optimal length of non-renewing warranty period. Although a number of maintenance policies for the second-hand product have been discussed in the literature, there exist very few works dealing with the periodic preventive maintenance strategy under non-renewing warranty to obtain an optimal warranty period for the second-hand product. This work considers a non-renewing warranty policy, during which a periodic preventive maintenance is adopted to diminish the degradation process of the product and determines an optimal length of warranty period by optimizing the expected cost rate incurred to the dealer during the maintenance cycle. Since the replacement of the failed second-hand product may not be practical in general, we adopt a concept of full refund instead of replacement in this paper. By pre-setting a repair time threshold, either the refund or minimal repair is provided by the dealer depending on whether the failed second-hand product can be repaired within the repair time threshold or not. The maintenance cycle of the second-hand product starts with the purchase of the product and ends when either the refund occurs within the warranty period or the warranty expires. Given a certain cost structure charged to the dealer, we formulate the expected cost rate during the maintenance cycle of the product and determine an optimal length of warranty period minimizing the objective function. Assuming the power law process for the product’s failures and a Weibull distribution for the repair times, we present a numerical example and investigate the effects of periodic preventive maintenances and other relevant parameters on the optimal solution.

An electric utility has as an objective to guarantee the continuity and quality of the service provided. In order to enforce an increase in the quality of service, the ANEEL (Portuguese for The Brazilian National Electric Energy Agency) introduced penalties in contracts with public electricity transmission utilities in case transmission facilities are taken out of service due to accidents, failures or scheduled maintenance. Traditional maintenance approaches to electric power transmission systems are based on actions taken periodically. Although these approaches aim to improve the performance of these systems, usually they do not accurately assess the impact of maintenance actions on their reliability related to the resources employed, as well as the resulting legal penalties. This paper proposes an approach for addressing this problem based on reliability/cost to find the best strategies for performing maintenance on electric power transmission equipment (assets), with a mathematical model that provides a feasible maintenance calendar while respecting the constraints of the electrical system operation. The proposed method consists of a combination of a constructive heuristic with dynamic programming, to find the best solutions for a given planning horizon.

The pavement management system (PMS) consists of several components including data collection, analysis, and reporting procedures. This system helps make decisions about the prioritization of road sections and selecting optimal maintenance strategy for the related road pavement networks. Considering the deteriorating rate of pavement sections and the limited budget and resources, it is important to find the most optimal maintenance and rehabilitation (M&R) scenarios for each pavement section. This study presents a model based on the cloud decision tree (CDT) theory for selecting the most optimal M&R strategies. A CDT system is presented for Iran’s national road network. The system includes a general decision-making model and various decision trees for every province of the country. Exclusive decision-making models were presented for freeways, highways, and main roads. Furthermore, different decision tree models are presented based on roads Annual Average Daily Traffic (AADT). Using the presented theory resulted in a general model with an accuracy of 80%. Evaluation of acquired decision trees showed that fatigue cracking and International Roughness Index (IRI) are the mo st important parameters to determine the appropriate M&R scenarios. Using these parameters provided results close to the re suits of experts’ surveys under real conditions, regardless of rank and traffic volume of the road sections.

Conventional maintenance mode for the traction power supply system (TPSS) is to perform scheduled regular maintenance activities for power supply equipment, while such maintenance mode may result in undue maintenance tasks and low efficiency due to different degradation processes of different sorts of equipment. To address this problem, this paper introduces a preventive opportunistic maintenance (POM) method for TPSS based on equipment reliability. Firstly, a POM model is established by considering the equipment reliability degradation process based on Weibull distribution. Then, by considering the total power outage time in the planned operation cycle of TPSS as the optimization objective, the optimal maintenance scheme of TPSS is formulated by iterative method of maintenance strategies. The proposed method is verified by introducing practical maintenance strategies and fault record data of the traction transformer, circuit breaker and disconnector in an actual TPSS of a railway administration. Results show that the presented method can make full use of the existing fault data to develop a POM scheme for TPSS. It can improve maintenance efficiency and reduce power outage time, providing guidance to formulate scientific maintenance strategies for TPSS.

Aiming at wind turbines, the opportunistic maintenance optimization is carried out for multi-component system, where minimal repair, imperfect repair, replacement as well as their effects on component’s effective age are considered. At each inspection point, appropriate maintenance mode is selected according to the component’s effective age and its maintenance threshold. To utilize the maintenance opportunities for the components among the wind turbines, opportunistic maintenance approach is adopted. Meanwhile, the influence of seasonal factor on the component’s failure rate and improvement factor’s decrease with the increase of repair’s times are also taken into account. The maintenance threshold is set as the decision variable, and an opportunistic maintenance optimization model is proposed to minimize wind turbine’s life-cycle maintenance cost. Moreover, genetic algorithm is adopted to solve the model, and the effectiveness is verified with a case study. The results show that based on the component’s inherent reliability and maintainability, the proposed model can provide optimal maintenance plans accordingly. Furthermore, the higher the component’s reliability and maintainability are, the less the times of repair and replacement will be.

Mechanical equipment in the process of operation appears a variety of faults due to different reasons. These faults affect the operation of machinery, causing economic losses, moreover, it may cause accidents, or even casualties. Predicting the nature, degree, development trend and position of mechanical faults is of great significance for making fault early warning, changing scheduling scheme and determining optimal maintenance time. This study aims to propose a generalized mechanical fault prediction method under the condition of short data validity. In the aspect of application, this paper hopes to combine the fault prediction with the shop dynamic scheduling, and constructs the mode of forecasting and optimizing the scheduling plan. The results show that the DGM (1,1) model based on amplitude compression is effective on the prediction of oscillation sequence. Markov chain modification could reduce the error greatly. The feasibility of fault prediction by using Grey-Markov chain has been proven by an illustrative example.

The back-calculation of layer moduli using falling weight deflectometer (FWD) data is an essential part of the pavement maintenance measures in pavement asset management systems (PAMS). It is necessary to provide the back-calculation predicted layer moduli and other pavement distresses as a part of PAMS to estimate the optimal maintenance strategy. The predicted layer moduli can be introduced as a part of PAMS if the back-calculation model can be easily integrated. In this study, the cuckoo search algorithm (CSA) was used as an optimization tool to develop a back-calculation model, BACKCSA, for predicting the pavement layer moduli using FWD data. The developed model was validated by comparing it with the laboratory-measured resilient moduli (MR) values of the field core samples obtained from five different highway sections. The variation between the laboratory MR values and the BACKCSA model predicted layer moduli was marginal. The pavement layer moduli values obtained from the BACKCSA model were also compared with the back-calculated layer moduli obtained using the BAKFAA model. The statistical hypothesis testing revealed that the predicted layer moduli from BACKCSA and BAKFAA models were similar to the laboratory-measured MR values. The mean absolute percentage error (MAPE) between the BACKCSA model predicted layer moduli and the laboratory-measured MR values was 2.49% on an average, indicating a marginal error between the predicted and the measured values. One of the most significant benefits of using the BACKCSA model over the other back-calculation models is its ability to handle deflection data from any FWD equipment, in general.

This paper presents a theoretical framework for developing a risk–cost optimised maintenance strategy for structures during their whole service life. A time-dependent reliability method is employed to determine the probability of structural failure and a generic form of stochastic model is developed for structural responses. To facilitate practical application of the proposed framework, a general algorithm is developed and programmed in a user-friendly manner. The merit of the proposed framework is that, in predicting when, where and what maintenance is required for the structure, all structural components and multi-failure modes are considered. It is found in the paper that, to ensure the safe and serviceable operation of the structure as a whole, some components need maintenance multiple times for different failure modes, whilst other components need “do nothing”. It is also found that ignorance of correlation amongst structural components and failure modes would underestimate the risk of structural failures in longer term and that the components with higher cost of structural failures require more maintenance actions. The paper concludes that the proposed framework can equip structural engineers, operators and asset managers with a tool for developing a risk–cost optimal maintenance strategy for structures under their management.

Maintenance management of a large road network with limited resources is a challenging task in developing countries like India. Road agencies expect that the condition of their road infrastructure should always above the desired level of performance even under restricted resources. This requires an integrated decision support system considering all aspects of maintenance. In this paper, a framework for optimal maintenance decisions by multi-objective approach is formulated for a road network by considering functional condition of the pavement quantified in terms of roughness and structural condition quantified in terms of rebound deflection. A multiobjective optimization model is developed using Integer Linear Programming (ILP). A novel approach to track the age of pavement is applied in the formulation of the mathematical model. The e-constraint method is adopted to generate non-dominated solutions. Budget bound optimizing model is formulated and implemented for a typical road network of four roads and optimal maintenance scheduling is arrived.

We consider systems that are operating in a random environment modeled by an external shock process. Performance of a system is characterized by a quality (output) function that is decreasing (due to degradation) in the absence of shocks. Shocks have a double impact, i.e., they affect the failure rate of a system directly and at the same time, and each shock contributes to the additional decrease in the quality function. The unconditional and conditional (on survival) expectations for the corresponding stochastic quality process are obtained. The system is replaced either on failure or on the predetermined replacement time, whichever comes first. The corresponding optimization problem is considered and illustrated by detailed numerical examples.

This paper presents a probabilistic dynamic programming algorithm to obtain the optimal cost-effective maintenance policy for a power cable. The algorithm determines the states which a cable might visit in the future and solves the functional equations of probabilistic dynamic programming by backward induction process. The optimisation model considers the probabilistic nature of cables failures. This work specifies the data needs, and presents a procedure to utilize maintenance data, failure data, cost data, and condition monitoring or diagnostic test data. The model can be used by power utility managers and regulators to assess the financial risk and schedule maintenance.

Gearbox in offshore wind turbines is a component with the highest failure rates during operation. Analysis of gearbox repair policy that includes economic considerations is important for the effective operation of offshore wind farms. From their initial perfect working states, gearboxes degrade with time, which leads to decreased working efficiency. Thus, offshore wind turbine gearboxes can be considered to be multi-state systems with the various levels of productivity for different working states. To efficiently compute the time-dependent distribution of this multi-state system and analyze its reliability, application of the non-homogeneous continuous-time Markov process (NHCTMP) is appropriate for this type of object. To determine the relationship between operation time and maintenance cost, many factors must be taken into account, including maintenance processes and vessel requirements. Finally, an optimal repair policy can be formulated based on this relationship.

With increasing application of Fused Deposition Modeling (FDM), it is important to develop a reliability model and maintenance plan strategies for FDM machines. This paper studies the impact of machine component degradations and process parameter deviations on the FDM system reliability. The contribution of this paper is to integrate both FDM machine reliability and process-dependent product quality in a system reliability model. The machine reliability is determined by component degradations. All component failures are subject to exponential distributions with time-dependent failure rates. The process-dependent product quality is used to quantify the impact of process parameters on product quality. The Flashforge Guider fabrication machine is used as a case study. The reliability model of this machine is analyzed during its operation time. Based on the reliability model, the optimal maintenance plans using multi-objective optimization method are obtained. The minimum life cycle cost and cumulative failure probability functions are taken as optimization objectives. Comparisons among the optimal maintenance plans and the solution without preventative maintenance (PM) are investigated. The implementation of PM improves the system reliability and reduces the life cycle cost. Through the Pareto frontiers, the trade-off is obtained between life cycle cost and cumulative failure probability functions. Numerical results for the optimization of FDM system operations are provided.

Various structures such as marine structures age over time. In order to always maintain safety conditions, maintenance processes including inspection and repair should be implemented on them. Corrosion and fatigue cracks are two main factors that reduce the ultimate strength of the ship’s hull girder over time and thus increase the probability and risk of failure. At the time of inspection, the structural conditions must be checked so that, if necessary, the required repairs can be done on it. The main objective of this paper is to provide optimized maintenance plans of the ship structure based on probabilistic concepts with regard to corrosion and fatigue cracks. Maintenance activities increase the operational costs of ships; therefore, it is advisable to inspect and repair in the optimal times. Optimal maintenance planning of the ship structure can be conducted by formulating and solving a multi-objective optimization problem. The use of risk as a structural performance indicator has become more common in recent years. The objective functions of the optimization problem include minimizing the structure’s lifecycle maintenance costs, including inspection and repair costs, and also minimizing the maximum risk of structural failure during the ship’s life. In the following, to achieve better responses, reliability index has been added to the problem as the third objective function. The multi-objective optimization problem is solved using genetic algorithms. The proposed risk-based approach is applied to the hull structure of a tanker ship.

In this paper, the optimal maintenance policy is investigated for a system with stochastic lead time and two types of failures. The system has two types of failures, one type is repairable, when the repairable failure occurs, the system will be repaired by repairman, and the system after repair is not “as good as new”. The other type of failure is unrepairable, and when the unrepairable failure occurs the system must be replaced by a new and identical one. The spare system for replacement is available only by order, and the lead time for delivering the spare system is stochastic. The successive survival times of the system form a stochastically decreasing geometric process, the consecutive repair times after failures of the system form a renewal process. By using the renewal process theory and geometric process theory, the explicit expression of the long-run average cost per unit time under ordering policy (N − 1) is derived, and the corresponding optimal can be found analytically. Finally, the numerical analyses are given.

Intelligent systems, that support the maintenance of production resources, offer real-time data-based approaches to optimize the maintenance effort and to reduce the usage of resources within production systems. However, unused potentials remain regarding maintenance schedules with minimal opportunity costs of the measures taken. This work provides a novel, machine-learning-based approach for the exploitation of these remaining optimization opportunities as an exemplary extension of the current state of the art. The determination of an optimal maintenance schedule for parallel working machines, is based on the data of a production system. The main result of this work is the performance of the implemented reinforcement learning algorithms, both in terms of downtime reduction, which increases the production output, and in terms of reducing maintenance costs compared to existing maintenance strategies. Hence, this work provides a holistic approach to the optimization of maintenance strategies and gives further evidence of a meaningful applicability of reinforcement learning algorithms in manufacturing processes.

Performance of machine tool tends to deteriorate in the production process. This deterioration increases the processing energy consumption and leads to more defectives and corresponding energy waste. Maintenance can be taken to restore the performance of machine tool and improve the energy efficiency, which has a significant impact on the total energy consumption and productivity. This paper proposes an approach to improve the energy efficiency of the production process through scheduling the maintenance actions of the machine tool, taking into account productivity, product quality, and energy consumption. The deteriorating machine tool is modeled as a discrete-time, discrete-state Markov process. Partially observable Markov decision process (POMDP) framework is applied to develop the maintenance decision-making model, where the joint observation of processing energy consumption and quality of manufactured workpiece is used to infer the status of the machine tool. An optimal maintenance policy maximizing the total expected reward about energy efficiency over a finite horizon is obtained, which consists of a sequence of decision rules corresponding to the optimal action for each belief vector. The characteristics of the optimal policy are illustrated through a numerical example and the effects of parameters on the policy are analyzed.

Manufacturing relies on the Internet of things and intelligent internet technology to provide users with remote monitoring services and early warning of failure, to ensure the smooth operation of the equipment. Based on this background, this paper takes equipment user (customer) and maintenance service provider (agent) consist of the two-stage maintenance service supply chain as the research object. A model is proposed for the maintenance service contracts design, optimization under decentralized decision-making and centralized decision-making. The model is developed under the assumption that there is one customer and one unique service provider who is the Original Equipment Manufacturer (OEM) and is called the agent in this paper. Besides, an extensive sensitivity analysis is performed to examine the impact on the improvement factor of the model output. Under the selective maintenance service outsourcing strategy, the agent makes the price of incomplete preventive maintenance, and the customer decides the incomplete preventive maintenance cycle and the times according to the price. Confronting the equipment failure, customer self-repair service and commitments for the loss of equipment downtime, the customer decides the optimal maintenance services prices, and the agent decides the optimal numbers and the cycles of the preventive maintenance service (equipment life).

Particle swarm optimization (PSO) is one of the most popular and successful optimization algorithms used for solving single objective and multi-objective optimization problems. It is found that the Multi objective particle swarm optimization (MOPSO) has ability to find the optimal solution quickly and more efficient than other optimization algorithms. In this paper, a discrete (binary) MOPSO with chaos methods is developed and applied to pavement maintenance management. The main objective of this research is to find optimal maintenance and rehabilitation plan for flexible pavement with minimum maintenance cost and maximum pavement performance. This research is the first attempt to combine the crossover operation with velocity and position with multi objective PSO algorithm. The results show that the improvements in pavement performance and cost objectives are 94.65 and 54.01% respectively, while the improvement in execution time is 99.9%. In addition, it is found that the developed algorithm is able to converge to the optimal solution quickly, comparing with another PSO algorithm.

The availability of specialized medical equipment plays a critical role in the delivery of healthcare services. This becomes even more critical in developing countries where few medical devices are available in public hospitals, hence do not commensurate with demand, which leads to their overutilization. This often results in frequent failure of the medical devices and as a result, low availability. In turn, this leads to delayed treatment and consequently, access to affordable and quality healthcare by the disadvantaged in the society. This rather high unavailability of critical medical equipment in public hospitals in developing countries motivates this study, hence the necessitating the need to implement optimal maintenance strategies and operational protocols for maximizing equipment availability. However, selecting such optimal maintenance and operation strategies is not straightforward, and for this reason, a risk-based maintenance approach is proposed. The proposed approach addresses the aforementioned challenge through structured steps through which maintenance practitioners identify and prioritize prevalent failure modes. Based on the prioritized failure modes, operation and maintenance protocols may be derived. Moreover, the risk-based maintenance approach is useful for among other decisions, root cause analysis. The proposed risk-based maintenance approach consists of four steps; (1) data collection and standardization process, (2) modified failure mode and effect analysis step for prioritizing critical failure modes, (3) a root cause analysis step for analyzing the causes of recurrent failure modes, and (4) formulation of operation and maintenance strategies for mitigating equipment failure risks. The proposed approach is demonstrated in application in a case of large teaching and referral hospital in Kenya.

Maintenance planning of structures constitutes a multiple stage decision-making process. Different maintenance strategies will produce various reliability profiles throughout the entire lifetime of structures and result in different maintenance costs. The decision-making process is usually a complex procedure, especially when a variety of alternative maintenance types are available. In this paper, a cooperative PSO-DP method (co-PSO-DP) is proposed for obtaining the optimal solution for the multi-stage multi-option maintenance problem, aiming at achieving minimum maintenance costs under the reliability constraints. The dynamic programming (DP), a key methodology in optimal control theory, integrated with particle swarm optimization (PSO) is developed and applied to determine the optimal maintenance strategy for deteriorating and repairable structures. Thereafter, a total life-cycle cost-based (LCC-based) RBDO model is formulated, in which the initial construction cost together with the subsequent maintenance cost is considered simultaneously and both the structural parameters and the maintenance strategy parameters, including the intervention time and options, are taken as the design variables. Again the proposed co-PSO-DP algorithm is applied to identify the optimal solution of the model. Two examples are presented to illustrate the effectiveness of the proposed method. It is shown that the proposed method can efficiently solve the complex maintenance optimization problem with multi-level intervention options. Compared with the results obtained by using the traditional PSO or GA algorithm, the proposed co-PSO-DP approach exhibits better performance with higher efficiency.

In this paper, a maintenance policy is proposed for pipelines subjected to active corrosion and residual stress, by taking into account imperfect inspection results. The degradation of the pipeline is induced by uniform corrosion, leading to losses of the pipe wall thickness. Localized corrosion is not considered herein, as neither pitting nor crevice corrosion are strongly influenced by external loading conditions and, hence, are not critical in structural strength considerations. When the corroded layers are removed, strain relaxation occurs, causing a redistribution of residual stresses. In parallel, the inspection is applied to detect the corrosion defects, namely the thickness of the corroded layer, and it has a detection threshold under which no corrosion rate can be measured. Due to uncertainties, each inspection is affected by the probability of detecting small defects and the probability of wrong assessment in terms of defect existence and size. The present work aims at integrating imperfect inspection results in the cost model for corroded pipelines, where the failure probabilities are computed by reliability methods. A numerical application on a gas pipe shows the influence of corrosion rates and residual stresses on the optimal maintenance planning.

The traditional maintenance strategies may result in maintenance shortage or overage, while deterioration and aging information of manufacturing system combined by single important equipment from prognostics models are often ignored. With the higher demand for operational efficiency and safety in industrial systems, predictive maintenance with prognostics information is developed. Predictive maintenance aims to balance corrective maintenance and preventive maintenance by observing and predicting the health status of the system. It becomes possible to integrate the deterioration and aging information into the predictive maintenance to improve the overall decisions. This paper presents an integrated decision model which considers both predictive maintenance and the resource constraint. First, based on hidden semi-Markov model, the system multi-failure states can be classified, and the transition probabilities among the multi-failure states can be generated. The upper triangular transition probability matrix is used to describe the system deterioration, and the changing of transition probability is used to denote the system aging process. Then, a dynamic programming maintenance model is proposed to obtain the optimal maintenance strategy, and the risks of maintenance actions are analyzed. Finally, a case study is used to demonstrate the implementation and potential applications of the proposed methods.

Production systems are exposed to machine failures and in most of them, buffer stocks are built between machines to guarantee that demands will be supplied during the interruptions because of breakdowns or periodic preventive maintenance (PM) actions. Also determination of the optimal maintenance policy in a production line is critical and it leads to provide a reliable system at lowest average total cost per time unit (comprising maintenance, quality and inventory costs). This is achieved by considering thresholds for nonconforming units and comparing with the proportion of produced nonconforming units and decides whether to undertake maintenance actions or not. The objective of this study is to integrating three aspects of quality, maintenance and production in operational units. In order to show the capability and effectiveness of the proposed approach, an operational unit consists of two machines is considered as a case study. The problem is modeled by simulation and the values of buffer stocks and nonconforming unit thresholds are determined to obtain the minimum average total cost using Taguchi method. Different rejection rates and buffer stocks values were given to the model as inputs in order to find the minimum average total cost which can satisfy the demand.

Network maintenance strategy selection is a multi-objective decision making topic. It mostly depends on the uncertainty and fuzziness of decision makers and conditions. In this paper, based on analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS), TOPSIS partial order method is proposed to choose the optimal maintenance strategy. This method uses AHP to determine the weights of evaluation indexes. The optimal maintenance strategy choice is given as an example to demonstrate the effectiveness of the method.

An effective maintenance schedule can largely improve productivity and reduce maintenance costs for enterprises. Many factors, such as resource, production, cost and crew, should be considered when we make maintenance plans. However, it is difficult to meet all requests of the production targets in practical engineering. The multiple attribute decision making for equipment maintenance is proposed in this paper, and the multiple objectives decision making method is utilized to solve the problem in the maintenance process. In the particular environment and resources, a case is studied to illustrate the model and methods. The model gives the optimal maintenance plan with the analytic hierarchy process method in accordance with the particular criteria. It has been proved that this model and decision methods are scientific and operable.

This paper presents a new optimal opportunistic and preventive maintenance policy for a two-unit model with economic dependency applicable to the electric power distribution systems. Unlike the previous optimal maintenance policies which considered mainly single unit systems or no opportunistic maintenance for two-unit systems, we consider a two-unit system with economic dependency, where unit 1 is subject to condition monitoring and soft failure and unit 2 is subject to hard failure. Unit 1 is gradually deteriorating and unit 2 has a general life time distribution. Condition of unit 1 is monitored periodically and it is considered as failed when its deterioration level reaches a critical level N. At the failure time of unit 2 system is considered as failed, although unit 1 is working, and unit 2 will be correctively replaced by the next inspection epoch. Units 1 or 2 are preventively replaced when deterioration level of unit 1 or age of unit 2 reaches or exceeds the related PM levels. At the time of corrective or preventive replacement of a unit, there is an opportunity to replace also the other unit. A mathematical model is derived to find the preventive and opportunistic replacement levels for units 1 and 2 that minimize the long run expected average cost per unit time. The problem is formulated and solved in the semi-Markov decision process (SMDP) framework. Numerical example is provided to illustrate the performance of the proposed model and a comparison of the proposed model with other policies is carried out.

Water supply systems (WSS), as well as other real-world systems, are characterized by complex configurations. For these systems, it is essential to ensure appropriate utility through optimal maintenance planning. The difficulties in decision-making are much increased by lack of information regarding the operation and failure conditions. When maintenance optimization is considered for systems configured as networks, comprising a large number of components, the main challenge is to model the reliability characteristics, such as availability, taking account of the interactions and dependencies between different components. The aim of this paper is to provide an optimal Preventive Maintenance (PM) plan with a view to maximizing the utility of a complex repairable system using Bayesian Networks (BNs). For each node of the BN, the optimal PM periodicity is obtained, in accordance with the policy of periodic imperfect PM with minimal repair at failure. The system availability is then computed, by Bayesian inference, for various combinations of nodes, or subsystems, periodicities and partial renewals before the complete renewal of the whole system. A utility function is then introduced to provide the maintenance plan for the system, leading to the implementation of the best policy. The methodology is illustrated by numerical application on WSS.

In the present paper, a k-out-of-n system with hidden failures is considered. The system is inspected periodically over a finite lifecycle. Hidden component failures accumulate and cause system failure when their cumulative number reaches n − k + 1. Every system failure presents an additional opportunity for inspection and, therefore, is called “opportunistic”. The objective is to find the optimal periodic inspection policy and the optimal maintenance action at each inspection for the entire system. Three types of maintenance are considered: minimal repair, preventive replacement and corrective replacement. In view of the failures being hidden, the maintenance decision is based on the optimal number of minimal repairs before replacement (of either type). Due to the unavailability of a closed-form solution, joint optimisation of inspection and maintenance policies resulting in the minimum total expected cost is performed using exhaustive search and genetic algorithm (GA), both with integer inspection period constraint, and a GA implementation with quasi-continuous inspection period. Although both exhaustive search and GA with integer inspection period provide identical results, the genetic algorithm presents a more efficient procedure and requires less computational time, which becomes more noticeable with increasing complexity of the problem, as in the case of GA with quasi-continuous inspection period. Based on the simulation results, some insights are made regarding the system’s operation and cost optimisation. Expressions are derived for the expected number of system failures in terms of the cost ratio and component failure intensity. In addition, a criterion is derived for establishing acceptable level of expected system failures over the system’s lifecycle. This can be useful when designing the system or analysing its performance.

International audience; Maintenance of offshore wind turbines is a complex and costly undertaking which acts as a barrier to the development of this source of energy. Factors such as the size of the turbines, the size of the wind farms, their distance from the coast and meteorological conditions make it difficult for the stakeholders to select the optimal maintenance strategy. With the objective of reducing costs and duration of such operations it is important that new maintenance techniques are investigated. In this paper we propose a hybrid model of maintenance that is based on multi-agent systems; this allows for the modelling of systems with dynamic interactions between multiple parts. A multi-criteria decision algorithm has been developed to allow analysis and selection of different maintenance strategies. A cost model that includes maintenance action cost, energy loss and installation of monitoring system cost has been presented. For the purposes of this research we have developed a simulator using NetLogo software and have provided experimental results. The results show that employing the proposed hybrid maintenance strategy could increase wind farm productivity and reduce maintenance cost.

Process-oriented tolerance design and maintenance policy of the fixture system are major ways to ensure the dimension quality of autobodies. Traditional maintenance policy mostly uses a fixed threshold considering the static nominal process variables, which always lead to excessive or inadequate maintenance of fixtures. Based on the analysis of dynamic information in multistation assembly process, a maintenance policy optimization methodology is proposed to integrate the updated process states and monitored incoming parts’ deviations in the assembly process. In this method, the product quality constraint function considering dynamic fixture component degradation, dimension fluctuations of parts, etc. is constructed firstly, and then, a nonlinear optimization algorithm is used to obtain the optimal maintenance schedule with a minimum maintenance cost. An engine compartment assembly case was used to illustrate the procedure and advantages of the proposed method. At last, the effect of dynamic process variables and incoming parts’ qualities on the maintenance results were examined and discussed.

In this paper, a multiobjective optimization model is formulated to determine the optimal preventive maintenance plans for a repairable multicomponent manufacturing system with increasing rate of occurrence of failure. The operational planning horizon is segmented into discrete and equally sized periods, and in each period, three possible maintenance actions (repair, replacement, or do nothing) have been considered for each component. The developed multiobjective nonlinear mixed-integer programming model is then utilized to find the Pareto-optimal preventive maintenance schedules for a computer numerical control (CNC) machine. In order to obtain the Pareto-optimal solutions, five multiobjective evolutionary algorithms are considered, and computational performance of the algorithms is evaluated using different metrics. It is found that three of these algorithms outperform the others in obtaining more and high-quality nondominated preventive maintenance and replacement schedules along with maintaining high level of diversity among generated solutions. Such a modeling approach and the comparison of algorithms would be useful for maintenance planners tasked with the problem of developing optimal maintenance plans for multicomponent manufacturing machines.

Numerous of maintenance policies were developed due to the change in the manufacturing environment and the growing of technologies in the past few decades. Due to fluctuation (oscillation, instability) phenomena of the manufacturing industry, it is difficult to identify an optimal maintenance policy that actually suit for a manufacturing system. Thus, a lot of efforts have been done in order to assist manufacturing industry in finding an optimal maintenance policy. This paper attempts to review past and current research on optimal maintenance policy selection issues associated with methods used as well as the applications. Published literatures were systematically classified based on certainty theory in operation management classification model in term of certainty, uncertainty, and risk. Furthermore, a sub family had been classified based on the approaches used in determining the optimal maintenance policy. The possible gap occurred between academic research and industrial application in maintenance policy optimization is also discussed in detail, and several possible ideas are put forward to reduce the gap. More importantly, the paper is intended to provide a different view on classifying these models and give useful references for personnel working in industrial as well as researchers.

Equipment plays an important role in open pit mining industry and its cost competence at efficient operation and maintenance techniques centered on reliability can lead to significant cost reduction. The application of optimal maintenance process was investigated for minimizing the equipment breakdowns and downtimes in Sungun Copper Mine. It results in the improved efficiency and productivity of the equipment and lowered expenses as well as the increased profit margin. The field operating data of 10 trucks are used to estimate the failure and maintenance profile for each component, and modeling and simulation are accomplished by using reliability block diagram method. Trend analysis was then conducted to select proper probabilistic model for maintenance profile. Then reliability of the system was evaluated and importance of each component was computed by weighted importance measure method. This analysis led to identify the items with critical impact on availability of overall equipment in order to prioritize improvement decisions. Later, the availability of trucks was evaluated using Monte Carlo simulation and it is revealed that the uptime of the trucks is around 11000 h at 12000 operation hours. Finally, uncertainty analysis was performed to account for the uncertainty sources in data and models.

We address the problem of determining inspection strategy and replacement policy for a deteriorating complex multi-component manufacturing system whose state is partially observable. We develop inspection and replacement scheduling models and other simple maintenance scheduling models via employing an imperfect repair model coupled with a damage process induced by operational conditions. The system state in performance of the imperfectly repaired system is modelled using a proportional intensity model incorporating a damage process and a virtual age process caused by repair. The system is monitored at periodic times and maintenance actions are carried out in response to the observed system state. Decisions to perform imperfect repair and replacement are based on the system state and crossing of a replacement threshold. The model proposed here aims at joint determination of a cost-optimal inspection and replacement policy along with an optimal level of maintenance which result in low maintenance cost and high operational performance and reliability of the system. To demonstrate the use of the model in practical applications a numerical example is provided. Solutions to optimal system parameters are obtained and the response of the model to these parameters is examined. Finally some features of the model are demonstrated. The approach presented provides a framework so that different scenario can be explored.

In this paper, we consider the multiple common due date assignment and single machine scheduling with a job-dependent aging effect and a deteriorating maintenance activity. Once the maintenance activity has been completed, the machine will revert to its initial condition and the aging effect will start anew, the maintenance duration depends on its starting time. The objective is to minimize the total of earliness, tardiness, due date costs and find the optimal due date, the optimal maintenance position. We introduce an efficient O(n4) algorithm to solve the problem. We also provide a special case of the problem and show that it remains polynomial time solvable.

The due date assignment has become a challenging issue related to the interaction between various participants of the supply chain. In this paper, we consider the total earliness and tardiness penalty scheduling simultaneously with the deterioration effects and maintenance activities on an unrelated parallel machine setting. We aim to determine jointly the optimal maintenance activity positions, the optimal common due date for all the jobs, and the optimal schedule to minimize the sum of the earliness and tardiness costs. If the number of machines is fixed, we introduce a polynomial time algorithm for solving the problem. We also propose an efficient polynomial time algorithm with a lower order time complexity for finding the optimal solution of a special case of the problem.

As an increasing number of manufacturers are beginning to realize the importance of maintaining throughput, many maintenance models have been developed to enable machines to achieve near-zero downtime. However, previous maintenance models usually ignore machine’s deterioration process. Therefore, this paper develops a novel data-driven machinery prognostic approach for machine performance assessment and prediction. With this prognostic information, a predictive maintenance model is proposed for a repairable deteriorating machine. As machine performance can be assessed, once it reaches the maintenance threshold, a maintenance operation is performed to restore the machine. Moreover, an operational cost is introduced to meet real manufacturing process. In this predictive maintenance model, the optimal maintenance threshold and maintenance cycle number are obtained with the aim to minimize the long-term average cost. Finally, a case study is presented. The computational results show the efficiency of this proposed predictive maintenance model.

In this paper, a new approach to maintenance scheduling for a multi-component production system which takes into account the real-time information from workstations including remaining reliability of equipments as well as work-in-process inventories in each workstation is proposed. To model dynamics of the system, other information like production line configuration, cycle times, buffers' capacity and mean time to repair of machines are also considered. Using factorial experiment design the problem is formulated to comprehensively monitor the effects of each possible schedule on throughput of the production system. The optimal maintenance schedule is searched by genetic algorithm-based optimization engine implemented in a simulation optimization platform. The proposed approach exploits all of makespans of planning horizon to find the best opportunity to perform maintenance actions on degrading machines in a way that maximizes the system throughput and mitigates the production losses caused by imperfect traditional maintenance strategies. Finally the proposed method is tested in a real production line to magnify the accuracy of proposed scheduling method. The experimental results indicate that the proposed approach guarantees the operational productivity and scheduling efficiency as well.

A condition-based maintenance model for periodically inspected systems subject to degradation and random shocks is addressed in this paper. Upon inspection, one will need to determine whether to perform a preventive maintenance, corrective maintenance, or to do nothing. In this paper, we present an expected maintenance cost rate model for systems subject to a degradation process and cumulative shock damage. An optimal maintenance policy consisting of two decision variables such as the preventive maintenance threshold and periodical inspection time, is determined that minimizes the expected long-run maintenance cost per unit time. To improve the search and better ways to obtain the optimum solution, we also propose a heuristic algorithm procedure based on the mean time to first failure concept in order to find the best optimum solution. A numerical example is given to illustrate the ideas of modeling and algorithms.

We consider single-machine scheduling and slack due-date assignment problems simultaneously with the position-dependent aging effect and deteriorating maintenance. In order to counteract the aging effect on the machine, we assume that at most one maintenance is allowed throughout the planning horizon and the maintenance can be performed immediately after the processing of any job is completed. The maintenance duration is dependent on its starting time. The objective is to find jointly the optimal common slack time, the optimal maintenance position, and the optimal schedule such that the sum of the total earliness, the total tardiness, and the common slack time costs is minimized. We propose polynomial time algorithms for all the problems studied.

Deteriorated water mains fail frequently causing service disruption and other inconvenience to the customers. Therefore, the utilities must conduct repair, rehabilitation and/or replacement in a timely manner to satisfy the needs of the customers. To succeed in this process the utilities must also consider the economics of the water main maintenance. The proposed methodology presents not only a method for the optimal maintenance but also a practical way of conducting it by providing the economical time period of maintenance. A method is also presented for analyzing the accuracy of proportional hazards models (PHMs) in forecasting break times and estimating the timing for economical replacement of water mains. A survival probability criterion for the forecasting of the pipe breaks was determined in order to minimize the prediction errors of the PHMs. Subsequently, the criterion was used to estimate the upper and lower bounds of future break times of a water main using the survival functions derived from the PHMs. Two General Pipe Break Prediction Models (GPBMs) for a pipe were estimated for each of the two series of the recorded and predicted, upper and lower bound break times. The threshold break rate (TBR) was coupled with the two GPBMs for each pipe and solved for time to give the upper and lower bounds of the economical replacement time period.

We consider the preventive maintenance of a production system that is deteriorated by random shocks and the production process itself. The degree of deterioration is modeled by discrete and finite states. Shocks arrive according to a Poisson process and deteriorate the system by random amounts. The system may deteriorate whenever it produces an item. The system is continuously monitored and repaired if the system state is at or above a predetermined level for maintenance. We analyze the lifetime, product quantity, average cost, and average profit considering revenue from the product and cost due to setup, operation, and repair. Assuming a structure of system parameters and costs, using numerical examples, we investigate the impact of production and shock arrivals on the average profit and the optimal maintenance level that maximizes the average profit. The proposed model is applicable to manufacturing tasks in which machines wear due to production, for example, press processes, milling, turning, punching, and drilling.

This study presents a hybrid genetic algorithm (GA) to optimize the periodic preventive maintenance model in a series-parallel system. The intrinsic properties of a repairable system, including the structure of reliability block diagrams, maintenance priority of components, and their maintenance periods, are considered in developing the proposed hybrid GA. The importance measure of components is employed to account for these properties, identify important components, and determine their maintenance priorities. The optimal maintenance periods of these important components are then determined to minimize total maintenance cost given the allowable worst reliability of a repairable system using the GA search mechanism. An elitist conservation strategy is applied to retain superior chromosomes in the iterative breeding process to accelerate the approach toward the global optimum. Furthermore, the response surface methodology is utilized to systematically determine crossover probability and mutation probability in the GA instead of using the conventional trial-and-error process. A case study demonstrates the effectiveness and practicality of the proposed hybrid GA in optimizing the periodic preventive maintenance model in a series-parallel system.

As a consequence of liberalization, electric utilities have developed different asset management strategies with the aim of reducing total maintenance costs by prolonging replacement cycles. A cost-minimizing network operator has to optimize the trade-off between costs for early replacement and quality penalties he faces in the case of outages (due to a resulting higher number of failures). The basic purpose of this paper is to identify the optimal decisions for a network operator under output-based quality regulation and to determine the main drivers for his decisions. In order to derive general insights, an analytical framework is chosen for this paper. This work builds on the foundations of optimal maintenance and replacement strategies as developed across Operations Research, Production Economics and Engineering Production Theory. As main results we are able to analytically derive optimal replacement strategies for network components depending on the characteristics of their failure distributions linked to the equipment’s cost, respective replacement cost, and quality penalties.

This paper investigates the optimal threshold values of age to perform preventive maintenance (PM) actions for leased equipment within the lease period. In this paper, we use age reduction method to describe the degree of PM and construct the maintenance cost function. For repairable leased equipment, two maintenance models are proposed: (i) maintenance policy of single-phase and (ii) maintenance policy of two-phase. During the lease period, PM actions are carried out when the age of equipment reaches a certain threshold value. Any failure of the leased equipment is rectified by a minimal repair within the lease period. Under these maintenance schemes, the mathematical models of the expected total cost for maintenance policies of single-phase and two-phase are established, and the optimal maintenance policies are derived such that the expected total cost is minimized. Finally, the features of the optimal maintenance policy are illustrated through numerical examples.

Minor maintenance actions can affect condition-monitoring measurements, which may in turn affect the accuracy of diagnostic and prognostic techniques used in condition-based maintenance (CBM). Outputs of a CBM model include the calculation of optimal maintenance decisions, conditional reliability, and the calculation of remaining useful life, among other measures. It is necessary to have a model for the manner in which the condition monitoring data changes over time to produce these output measures; many models have been developed to do so. It is also common to record minor maintenance actions carried out on critical assets, with lubricant changes being one of the most common, but it is unusual for models to consider the impact of such maintenance actions that affect the condition monitoring data. In this paper we discuss the impact of minor maintenance on CBM models. A dataset on a collection of gearboxes, consisting of reliability and oil analysis information, including data on oil changes and oil additions, is used to illustrate the benefit of modelling minor maintenance actions.

The design and manufacture of urban transportation applications has been necessarily complicated in order to improve its safety. Urban transportation systems have complex structures that consist of various electric, electronic, and mechanical components, and the maintenance costs generally take up approximately 60% of the total operational costs. Therefore, it is essential to establish a maintenance plan that takes into account both safety and cost. In considering safety and cost limitations, this research introduces an advanced reliability centered maintenance (RCM) planning method using computational techniques, and applies the method to a standard electric motor unit (EMU) subsystem. First, this research devises a maintenance cost function that can reflect the current operating conditions, and maintenance characteristics, of components by generating essential cost factors. Second, a reliability growth analysis (RGA) is performed, using the Army Material Systems Analysis Activity (AMSAA) model, to estimate reliability indexes such as failure rate, and mean time between failures (MTBF), of a standard EMU subsystem, and each individual component Third, two optimization processes are performed to ascertain the optimal maintenance reliability of each component in the standard EMU subsystem. Finally, this research presents the maintenance time of each component based on the optimal maintenance reliability provided by optimization processesand reliability indexes provided by the RGA method.

This paper considers a problem of optimal preventive maintenance and replacement schedule of equipment devoted to extracting resources from known deposits. Typical examples are oil drills, mine shovels, etc. At most one replacement of the existing machinery by a new one is allowed. The problem is formulated as an optimal control problem subject to the state constraint that the remaining deposit at any given time is nonnegative. We show that the optimal preventive maintenance, production rates, and the replacement and salvage times of the existing machinery and the new one, if required, can be obtained by solving sequentially a series of free-end-point optimal control problems. Moreover, an algorithm based on this result is developed and used to solve two illustrative examples.