Maintenance Optimization Model Research Articles

Multi-mission selective maintenance modelling for multistate systems over a finite time horizon

In the life cycle of a long-life structure, multiple maintenances are often required to ensure safety or normal performance. This article developed a multi-mission selective maintenance optimization model for a system with several multistate components over a finite time horizon. The model aims to minimize the total cost by determining the optimal intervention time and the corresponding maintenance option of each component. The system reliability requirement is taken as the constraint. The degradation process of each component is characterized by a homogeneous continuous time Markov chain in this model. Considering the uncertainties that exist during the deterioration–maintenance process, the condition state of each component at any time is described by a state probability vector. In addition, the components in the system are considered to be structurally dependent during the maintenance process, and a directed graph is used to represent the precedence relations among these components. A differential evolution algorithm is utilized to solve this complicated optimization model. A numerical example and an engineering application are presented to illustrate the availability of the proposed model.

Maintenance Objective Selection Framework Applicable to Designing and Improving Maintenance Programs

Maintenance optimization is applied by organizations to develop robust maintenance programs while attempting to establish a trade-off between competing maintenance requirements and resources. For this reason, maintenance decisions derived from maintenance optimization models are adversely affected, if (a) the maintenance objective(s) applied as input to the optimization models is not dynamically reviewed as the organizations environment context changes, and (b) there is continued use of historical maintenance objectives, oftentimes the practice for organizations lacking a framework for selecting maintenance objectives (MO’s). To address this, an interactive maintenance objective selection framework for stakeholders that aligns with and considers changes in the organization’s business, operational, and technical context, where dynamic maintenance objectives are selected and prioritized for application in real-life maintenance optimization models is proposed. The framework uses an analytic network process (ANP) based methodology, for selecting the relevant MO’s in view of competing dynamic criteria, for instance, employing remanufactured spares to optimize availability and maintenance cost. The applicability of the framework is demonstrated in case studies of companies operating in diverse industries like aviation and manufacturing in Africa. The study highlights the effects of dependencies between competing maintenance objectives, where the dependencies invariably influence how organizations prioritize MO’s to use for maintenance optimization programs. The additional value of the proposed framework lies in assisting organizations select maintenance objectives applicable to the organization while considering competing objectives and evolving business context.

Short-term Power System Maintenance Scheduling with Imperfect Repair

Most of the existing research in short-term power system maintenance scheduling assumes that repair is minimal when equipment fails. However, this may not always be the truth because sometimes repair after failure is imperfect. This paper investigates the impact of imperfect repair on power system maintenance scheduling. Unlike previous research which didn’t consider imperfect repair, a condition-based maintenance optimization model is proposed. The model focuses on equipment deterioration failure. Firstly, a new equipment state-space diagram under imperfect repair is given. And then, the availability of power equipment considering maintenance schedule and imperfect repair is derived using the Markov process theory. Based on that, in order to determine the optimal equipment maintenance schedules, the expression of system outage cost is given. Finally, system outage cost is minimized to optimize equipment maintenance schedules. A case study is used to illustrate the effectiveness and significance of the proposed model.

Condition-Based Maintenance Optimization for Multicomponent Systems Under Imperfect Repair—Based on RFAD Model

Condition-based maintenance has been developed as a very efficient strategy for guaranteeing multicomponent system performance and preventing unexpected failures. However, there are shortcomings in the existing condition-based maintenance optimization models. First, the existing models do not utilize the accelerated degradation testing (accelerated degradation testing) data obtained at the stage of component development. Second, most of these models assume perfect repair instead of imperfect repair. Third, the degradation models used in these condition-based maintenance models cannot consider the epistemic uncertainty. Motivated by these problems, this paper presents a new condition-based maintenance optimization model for multicomponent systems with imperfect repair. An integrated degradation prediction framework utilizing both ADT data and field data is presented to timely update the parameters in the proposed model. In order to solve the proposed multivariable, nonlinear programming model, a novel genetic algorithm with self-crossover operation and shift-mutation operation is developed. Numerical examples and comparisons are conducted to evaluate the performance of the proposed model. Results show that the proposed model can evaluate the degradation process of components accurately and achieve lower total maintenance cost.

Incorporating Dynamic Traffic Distribution into Pavement Maintenance Optimization Model

An optimal pavement maintenance strategy can keep the pavement performance at a high level under budget constraint. However, the impact of changes in traffic distribution caused by maintenance actions on user costs is rarely investigated in existing approaches. This research aims to solve the optimization of pavement maintenance strategy using a multi-stage dynamic programming model combined with the stochastic user equilibrium model, which can simulate the dynamic traffic distribution in the life cycle. To deal with the proposed model, a heuristic iterative algorithm is put forward. Ultimately, a hypothetical network is established to test the model and algorithm. The testing results prove that the proposed framework has an advantage in assessing user costs comprehensively and can provide an effective and optimal pavement maintenance strategy in a 30-year life cycle, which improves the efficiency of budget and pavement conditions. Additionally, this research provides quantitative evidence of interdependency in a road network, i.e., pavement maintenance actions on links can interfere with the user costs and traffic flow distribution in the whole network, which should be taken into account in pavement maintenance decision-making.

Reliability-oriented design of integrated model of preventive maintenance and quality control policy with time-between-events control chart

Superior manufacturing quality is prerequisite to continuously produce reliable products, and the proactive product reliability assurance is always a crucial routine task for manufacturing. Therefore, this paper presents a reliability-oriented optimization model for joint preventive maintenance (PM) and process quality control with time-between-events (TBE) control chart, where the impact of manufacturing process on product final reliability is considered to reduce the product reliability degradation originating from latent manufacturing defects. Firstly, as the foundation of reliability-oriented control, the product reliability degradation mechanism in manufacturing stage is analyzed, and the process variations of product critical-to-reliability characteristics and internal defects are considered to model the product actual reliability. On this basis, periodical PM and TBE chart are jointly applied to enhance machine performance and improve manufacturing process quality to reduce potential reliability degradation, and a three-scenario model is proposed. Then, to evaluate the performance of the integrated model, the occurrence probability, expected reliability degradation, average operational cost, and cycle time of each scenario are analyzed, and the joint policy is optimized to minimize the expected reliability degradation of batch products within a limited cost range. Finally, a case study is presented to verify the performance of the developed model by comparing it with a pure planned maintenance policy, and a sensitivity analysis is conducted to analyze the impacts of model parameters.

Operational reliability evaluation-based maintenance planning for automotive production line

ABSTRACTReliability evaluation plays a critical role in upgrading the availability and productivity of automotive manufacturing industries by adopting the well-planned maintenance. Due to the lack of operation management studies in automotive industry, this paper addresses an operational reliability evaluation through failure behavior trend in an automotive production line. The main approaches for reliability analysis in this study include statistical structure and Monte Carlo simulation model. The statistical structure consists of three steps: data acquisition and homogenization process, validity of the trend hypothesis and parameters estimation. The reliability evaluation under statistical approach identified the main bottlenecks through the recognized behavior trend of system so that needs to be considered as a priority. Besides, K–R algorithm as Monte Carlo simulation was carried out to simulate reliability regarding failure distribution function. The result of Monte Carlo simulation with different iterations provides a high prediction accuracy of reliability with the lowest error. In addition, regarding the computed reliability through the proposed approaches and total expected cost, a reliability-based maintenance optimization model was conducted. The proposed maintenance intervals could be useful for improving the operational performance of critical components in automotive system.

Mission Reliability-Oriented Selective Maintenance Optimization for Intelligent Multistate Manufacturing Systems With Uncertain Maintenance Quality

Selective maintenance is widely used as a reliability-centered maintenance strategy due to the limited maintenance resources. However, existing selective maintenance studies only consider basic reliability, which cannot systematically describe the operating mechanism of a multistate system, thereby resulting in the inability to obtain an optimal maintenance strategy. Moreover, intelligent manufacturing systems are highly representative of typical multistate industrial systems. In this study, a mission reliability-oriented selective maintenance optimization model for intelligent manufacturing systems that considers the uncertain maintenance effect was proposed. First, a new connotation and modeling method for mission reliability based on multistate system theory was presented to comprehensively characterize the operating mechanism of intelligent manufacturing systems. Second, a quantitative model between maintenance resources and quality based on real-time data was established to reflect the uncertain characteristics caused by repairmen and tools. Third, a selective maintenance decision model of a multistate manufacturing system was developed under the constraints of maintenance cost and time. This constraint combination optimization problem was solved using the particle swarm optimization algorithm. Finally, a case study of selective maintenance optimization for a cylinder head manufacturing system was presented to verify the proposed method.

Selective Maintenance Optimization for a Multi-State System With Degradation Interaction

This paper studies the selective maintenance problem for a multi-state system (MSS) performing consecutive production missions with scheduled intermission breaks. To improve the reliability of the system successfully performing the next mission, all maintenance actions need to be carried out during maintenance breaks. However, it may not be feasible to repair all components due to the limited maintenance resources (such as time, costs, and manpower). Hence, a selective maintenance model was established to identify a subset of maintenance actions to perform on the repairable components. We extend the original model in several ways. First, we consider the role of degradation interaction in determining the state transition probability of each component. Back-propagation (BP) neural network is employed to predict the transition matrix since it is not practicable to analyze the degradation processes of all components using the traditional probability model. Second, a selective maintenance optimization model for an MSS is established based on the prediction results of the BP neural network and solved by a genetic algorithm (GA). Finally, an example is illustrated to verify the effectiveness and superiority of the proposed method.

Developing a bi-objective imperfect selective maintenance optimization model for multicomponent systems

This paper develops a bi-objective imperfect selective maintenance optimization model for a multicomponent system, which carries out missions interspersed with scheduled breaks. Imperfect maintenance (IM) actions are performed on the components during the break to increase the system reliability during the following mission. The level of maintenance performed determines the improvement of the component’s health. A mathematical model with two objective functions is developed to optimize the tradeoffs between the total maintenance cost and the system reliability based on the decision maker’s preferences. Numerical examples are provided to show that the proposed model reaches valid maintenance decisions. Furthermore, it is shown that when high system reliability is required, the optimal decision is not significantly affected by the decision-maker’s preference for one objective or the other.

Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes

AbstractMost maintenance optimization models of gear systems have considered single failure mode. There have been very few papers dealing with multiple failure modes, considering mostly independent failure modes. In this paper, we present an optimal Bayesian control scheme for early fault detection of the gear system with dependent competing risks. The system failures include degradation failure and catastrophic failure. A three‐state continuous‐time–homogeneous hidden Markov model (HMM), namely the model with unobservable healthy and unhealthy states, and an observable failure state, describes the deterioration process of the gear system. The condition monitoring information as well as the age of the system are considered in the proposed optimal Bayesian maintenance policy. The objective is to maximize the long‐run expected average system availability per unit time. The maintenance optimization model is formulated and solved in a semi‐Markov decision process (SMDP) framework. The posterior probability that the system is in the warning state is used for the residual life estimation and Bayesian control chart development. The prediction results show that the mean residual lives obtained in this paper are much closer to the actual values than previously published results. A comparison with the Bayesian control chart based on the previously published HMM and the age‐based replacement policy is given to illustrate the superiority of the proposed approach. The results demonstrate that the Bayesian control scheme with two dependent failure modes can detect the gear fault earlier and improve the availability of the system.

Road Maintenance Optimization Model Based on Dynamic Programming in Urban Traffic Network

Urban road maintenance is an important part of urban traffic management. However, in modern cities, road maintenance work needs to occupy some traffic resources; therefore, unreasonable road maintenance schemes often lead traffic networks to unexpected large-scale congestion. In this paper, a dynamic programming model is proposed in order to minimize the delay caused by road maintenance scheme. This model can obtain a globally optimal maintenance scheme which contains the decisions and sequence for every stage of maintenance. Each stage of this model can be boiled down to a discrete network design problem. This model helps make suggestions for the traffic managers with the request of minimizing the delay caused by the maintenance scheme. This paper uses two examples to illustrate this method, one is a small-scale Nguyen-Dupuis network, and the other one is a larger scale Sioux-Falls network.

Maintenance Optimization of Continuous State Systems Based on Performance Improvement

The continuous state system is a special kind of a system in which the states of the system and its components have continuous values, ranging from perfect functioning to complete failure. This paper introduces the performance improvement for a continuous state system, which can be used to measure the improvement of systems performance comparing pre- and postmaintenance time. The probabilistic characteristics of performance improvement are discussed in detail. Then, the performance improvement for multicomponent maintenance and corresponding calculation method are also put forward to establish the objective function for maintenance optimization. Third, a maintenance optimization model for such a system is studied, and corresponding performance improvement based genetic algorithm is provided to search a near global optimal solution. Finally, two numerical examples and an oil transportation system application case study are implemented to verify the effectiveness of the proposed method.

Maintenance optimization in failure-prone systems under imperfect preventive maintenance

In the majority of the existing preventive optimization models only costs related to maintenance actions are accounted for, while breakdown and operational costs are usually ignored. Liao et al. (J Intell Manuf 21(6):875–884, 2010) proposed a preventive maintenance model to deal with this shortcoming. In the present paper, we revisit and discuss the results provided in Liao et al. (2010) and point out some inconsistencies in the maintenance optimization model proposed therein. Accordingly, we develop a new maintenance optimization model and discuss some of its main cost components. Furthermore, optimality conditions are also formally investigated and a solution method is provided. Numerical experiments are conducted to illustrate the validity of the proposed approach and results are compared with those provided in the original paper by Liao et al. (2010).

Data mining applied for national road maintenance decision support system

National roads are one of the main networks of a country’s transportation system. To maintain the performance level of national roads requires a well-structured pavement management system (PMS). The decision support system (DSS) is inseparable in the modern PMS, which required the development of a new approach for the DSS in support of national road network maintenance. The proposed model integrates data mining (DM) and geographical information system (GIS) to construct a simple DSS. DM is used to developed road maintenance optimization models, and then integrated with DSS with the help of GIS as an interface application. Historical data on the national road network in West Java, Indonesia is used as a case study. Examples show that the proposed model can determine a decision support solution efficiently. In addition, a userfriendly computer interface is developed so that PMS stakeholders can plan pavement maintenance simply and effectively.

Maintenance Optimization Model for One kind of Three-Dimensional Radar Antenna Array

For three-dimensional radar, maintenance cost is high and maintenance time is difficult to be determined. What’s more, model simulation computing is significantly complex. The subject of this paper is a new generation of meter wave active, phased array 3D radar. We put forth a concept that maintenance should be done after dividing into several regions to the asymmetrical distribution planar array antenna. First, a failure model of array elements is built to analyze the influence from the element to antenna. Second, the maintenance optimization model is established. Finally, computer simulations are conducted to verify the feasibility and effectiveness of the proposed model.

Orchestration of preventive maintenance interventions

The paper has the objective of planning the preventive maintenance of a system subject to different failure modes. The preventive maintenance is planned by means of the maximization of the system reliability. The reliability of a system depends on many factors. One of these is the arrangement of the maintenance interventions in a specified time horizon and this is an aspect that has received low attention by literature. A reliability-centered maintenance optimization model is developed in the paper and the optimization can be tackled by means of two methods, according to the fact that the concept of joint replacement is introduced or not.

Joint optimization of lot sizing and condition-based maintenance for multi-component production systems

This paper presents a joint optimization model of production lot sizing and condition-based maintenance for a multi-component production system which produces the products to meet the demand in a finite time horizon. The components in the system deteriorate gradually with usage and age. To evaluate the conditions of components, inspections are carried out after each production run. The condition-based maintenance decision-making rules are based not only on the predictive reliability but also on the structural importance of each component. Moreover, the economic dependency among components is also considered when performing corrective maintenance actions. The preventive maintenance cost, the corrective maintenance cost, the setup cost, the inspection cost, the inventory holding cost and the shortage cost are considered in this paper. The aim is to minimize the total cost by jointly optimizing two decision variables: the production lot size and the preventive maintenance threshold. The optimal joint policy is obtained by coupling simulation model and genetic algorithm. Finally, the use and advantages of our model are illustrated through a case study of a cluster tool.

A life cycle costing approach for discounting in age and interval replacement optimisation models for civil infrastructure assets

Civil infrastructure assets, such as roads, locks, bridges, treatment plants and storm surge barriers, are often characterised by long service lives and corresponding technical life cycles. When life cycles are long, the time value of money plays a role in asset management decision-making on capital investments and operation and maintenance expenditures. In this paper, a new life cycle costing (LCC) approach for discounting in two classes of maintenance optimisation models is developed. These models are the age replacement model and the interval replacement model. Three well-known LCC techniques, which are the present worth, the capital recovery and the capitalised equivalent worth, are combined and used to develop a stepwise methodology. This methodology is validated with the few case-specific mathematical equations that exist in the literature. The advantage of using this alternative LCC approach is its applicability and flexibility for reliability and maintenance engineers. The resulting LCC method builds on well-known LCC formula and enhances the understanding of the inclusion of discounting principles in reliability models. Understanding these principles makes the method flexible. Practitioners can extend or adapt the method to changing circumstances, such as additional cash flows and altering reliability modelling.

A Proactive Event-driven Decision Model for Joint Equipment Predictive Maintenance and Spare Parts Inventory Optimization

Manufacturing operations can take substantial advantage of the proactivity concept by utilising event-driven information systems, able to process the sensor data and to provide proactive recommendations. Despite the recent advances in technology and information systems and the variety of methods for prognosis, decision models for joint maintenance and inventory optimization on the basis of real-time prognostic information have not been explored. We propose a proactive event-driven decision model for joint predictive maintenance and spare parts inventory optimization which addresses the Decide phase of the “Detect- Predict- Decide- Act” model and can be embedded to an Event Driven Architecture (EDA) for real-time processing in the frame of e-maintenance concept. The proposed approach was tested in a real manufacturing scenario in automotive lighting equipment industry and proved that maintenance and inventory costs can be significantly reduced by transforming the company's maintenance strategy from time-based to Condition Based Maintenance (CBM).