Optimal Maintenance Plan Research Articles

Power Routing: A New Paradigm for Maintenance Scheduling

Currently, the necessity of efficient and reliable power systems is also increasing because of the strict requirements that standards and regulations impose, but still costs have to remain low. The monitoring and control of the components' lifetime can lead to reduce maintenance costs.However, overcoming the related challenges is not a straightforward task, as it involves knowledge of power device physics, smart management of electrical quantities, and optimal maintenance planning and scheduling. It represents a multidisciplinary issue being faced in the last decade.

An ISO-based security constrained generation maintenance coordination considering long-term SCOPF

Generation maintenance scheduling plays a crucial role in power system operation and planning; as it makes a significant effect on the outage time of generating units, and subsequently, to have a more secure and reliable power system with an optimal maintenance plan. Conventional maintenance scheduling methods are no longer applicable when the restructured power systems introduced. It is thus essential to develop novel approaches for the maintenance coordination. In this paper, an ISO-based security-constrained generation maintenance coordination approach is proposed. The approach comprises two main phases, the security-based maintenance scheduling, and the bidding based maintenance coordination. Moreover, the N−1 contingency analysis is considered in this study. The network security constraints are also incorporated in the investigation, using the security-constrained optimal power flow model. The model is solved by a binary-coded genetic algorithm which is combined with a primal-dual interior-point method. For systematic analysis, the IEEE 30-Bus and the IEEE-118 test systems are employed. The presented results emphasize the effectiveness and applicability of the proposed approach.

Risk analysis for lot-sizing and maintenance optimization problem under energy constraint with subcontractor solution

PurposeThe main contribution of this manuscript is to suggest new approaches in order to deal with dynamic lot-sizing and maintenance problem under aspect energetic and risk analysis. The authors introduce a new maintenance strategy based on the centroid approach to determine a common preventive maintenance plan for all machines to minimize the total maintenance cost. Thereafter, the authors suggest a risk analysis study further to unforeseen disruption of availability machines with the aim of helping the production stakeholders to achieve the obtained forecasting lot-size plan.Design/methodology/approachThe authors tackle the dynamic lot-sizing problem using an efficient hybrid approach based on random exploration and branch and bound method to generate possible solutions. Indeed, the feasible solutions of random exploration method are used as input for branch and bound to determine the near-optimal solution of lot-size plan. In addition, our contribution to the maintenance part is to determine the optimal common maintenance plan for M machines based on a new algorithm called preventive maintenance (PM) periods means.FindingsFirst, the authors have funded the optimal lot-size plan that should satisfy the random demand under service level requirement and energy constraint while minimizing the costs of production and inventory. Indeed, establishing a best lot-size plan is to determine the appropriate number of available machines and manufactured units per period. Second, for risk analysis study, the solution of subcontracting is proposed by specifying a maximum cost of subcontractor in the context of a calling of tenders.Originality/valueFor maintenance problem, the originality consists in regrouping the maintenance plans of M machines into only one plan. This approach lets us to minimize the total maintenance cost and reduces the frequent breaks of production. As a second part, this paper contributed to the development of a new risk analysis study further to unforeseen disruption of availability machines. This risk analysis developed a decision-making system, for production stakeholders, in order to achieve the forecasting lot-size plan and keeps its profitability, by specifying the unit cost threshold of subcontractor in the context of a calling of tender.

A holistic approach to risk-based decision on inspection and design of fatigue-sensitive structures

Design and operation of large welded structural systems (e.g. ship and offshore structures) are challenging due to numerous fatigue-sensitive details, limited available budgets, uncertainties in fatigue damages, inspection & maintenance activities, etc. Traditionally, fatigue design and maintenance planning have been almost disconnected, which restricts coherent decision-making and optimum safety management. Structural design optimization, without quantitatively incorporating the effects of operational maintenance, can hardly result in a structural plan that is optimum in terms of life cycle costs. Also, if the design of a structure is not optimum, maintenance optimization alone cannot really yield a optimum maintenance plan. As operational inspections and maintenance are essential, there are merits to utilize their effects on structural design and meanwhile optimize them at the initial design stage when impacts of decisions are greater. This paper proposes a risk-based approach to holistic decision-making enveloping decisions and uncertainties affecting design, inspection and maintenance of fatigue-sensitive components. Decisions variables in structural scantling and operational maintenance are obtained holistically at the structural design stage by risk-based optimization, based on quantitative assessment of the effectiveness of both structural scantling and maintenance interventions. Optimum fatigue reliability level is also obtained, informed by the effects of uncertainties and failure consequences. The method captures combined benefits of structural scantling and operational maintenance to fatigue reliability and risk mitigation and achieves optimum resource utilization and life cycle cost reduction. Advantages of the proposed method have been demonstrated via a numerical example, in comparison to alternative methods.

Joint optimization of budget allocation and maintenance planning of multi-facility transportation infrastructure systems

Transportation infrastructure, such as pavements and bridges, is critical to a nation’s economy. However, a large number of transportation infrastructure is underperforming and structurally deficient and must be repaired or reconstructed. Maintenance of deteriorating transportation infrastructure often requires multiple types/levels of actions with complex effects. Maintenance management becomes more intriguing when considering facilities at the network level, which represents more challenges on modeling interdependencies among various facilities. This research considers an integrated budget allocation and preventive maintenance optimization problem for multi-facility deteriorating transportation infrastructure systems. We first develop a general integer programming formulation for this problem. In order to solve large-scale problems, we reformulate the problem and decompose it into multiple Markov decision process models. A priority-based two-stage method is developed to find optimal maintenance decisions. Computational studies are conducted to evaluate the performance of the proposed algorithms. Our results show that the proposed algorithms are efficient and effective in finding satisfactory maintenance decisions for multi-facility systems. We also investigate the properties of the optimal maintenance decisions and make several important observations, which provide helpful decision guidance for real-world problems.

Research on Optimal Nonperiodic Inspection Strategy for Traction Power Supply Equipment of Urban Rail Transit Considering the Influence of Traction Impact Load

The inspection and maintenance work of traction power supply equipment (TPSE) is growing heavy with the construction and operation of a large number of urban rail transits (URTs). In order to improve the existing regular preventive maintenance mode and implement a “high-efficiency with low-cost” maintenance strategy, this article proposes an optimal nonperiodic inspection decision-making method for TPSE of URT considering the influence of impact load. First, the comprehensive degradation model of TPSE is established based on the linear Wiener process and the compound Poisson process. Second, the nonperiodic inspection model of TPSE in a single-maintenance period is established by introducing inspection planning functions for three types of degradation processes. With the lowest operating cost per unit time as the decision goal, the optimal maintenance plan is, thus, achieved. Finally, the proposed decision-making method is applied to the benchmark data taken from a URT administration in China related to a dry-type traction transformer in five years. The results show that the proposed method can help formulate efficient inspection strategies with the optimal cost. In addition, sensitivity analysis reflects the influence of different parameters on the optimization results, which helps to identify which parameter exerts the largest influence.

Optimization Method of Annual Power Generation and Transmission Maintenance Plan for New Energy Consumption

Recently, with the rising proportion of new energy installed capacity in China, the problem of new energy consumption in the northwest region is particularly prominent, and the phenomenon of abandoning wind power and solar power is serious. In order to alleviate this situation, this paper proposes an annual maintenance plan optimization method for new energy consumption. Under the premise of considering the new energy output, the goal is to minimize the limited power of new energy generation and the minimum cost of maintenance. Transmission maintenance planning model is mixed integer linear programming model and can be solved by a solver such as CPLEX. The feasibility of the method is verified by a provincial power grid example, which effectively reduces the limited power of new energy generation.

An exact optimization approach to the principal-agent problem in infrastructure projects via PPPs

PPPs are a compelling mechanism for infrastructure procurement but are vulnerable to the principal-agent problem due to the divergence in the parties’ utility functions. This research considers PPPs in infrastructure maintenance problems, in which the principal pursues high performance levels, whereas the agent pursues cost-efficiency. This paper proposes an optimization model that describes infrastructure performance as a result of deterioration and maintenance processes, and computes the associated benefits and costs for the principal and the agent, combining game theoretical notions with life-cycle analysis. Illustrative examples show how the organizational conditions surrounding PPPs shape the parties’ decisions and outcomes by running the model under different scenarios. For instance, the model can anticipate the agent’s optimal reaction to specific contractual conditions; i.e., what maintenance plan can be expected from a contractor depending on whether certain performance-related constraints or incentives are in place. Finally, the model can find the optimal maintenance plan for one party conditioned on respecting the other party’s optimum within a specified tolerance. This allows to explore a variety of middle-ground solutions and choose an alternative in which one party’s gain exceeds the other’s loss, providing evidence for incentive design. This research provides a quantitative tool to support decisions and policy on PPPs by integrating physical, financial, and organizational aspects of infrastructure management.

Maintenance optimization incorporating lumen degradation failure for energy-efficient lighting retrofit projects

This study presents an optimal lighting maintenance plan that takes into account lumen degradation failure. In lighting retrofit projects, retrofitted lights fail over time mainly owing to burnout and lumen degradation failures. These failures result in a reduced illumination level and lower project savings if proper maintenance is not performed. Previous studies developed lighting maintenance plans by modeling lamp population decay due to burnout failure. In this study, we present an optimal lighting maintenance plan based on lumen degradation failure. The lumen degradation failure is modeled based on the statistical properties of degradation rates. By using the Kaplan–Meier method, the formulated lumen degradation failure is used to model the surviving population. The surviving population model is used to design an optimal lighting maintenance plan, which maximizes energy savings and minimizes maintenance costs. The effectiveness of the formulated maintenance plan is demonstrated by an actual residential energy-efficient lighting retrofit project implemented in South Africa. Results show that the proposed maintenance plan is more cost-effective than full maintenance.

The Stochastic Annuity Method for Supporting Maintenance Costs Planning and Durability in the Construction Sector: A Simulation on a Building Component

Service life estimate is crucial for evaluating the economic and environmental sustainability of projects, by means—adopting a life cycle perspective—of the Life Cycle Cost Analysis (LCCA). Service life, in turn, is strictly correlated to maintenance investment and planning activities, in view of building/building component/system/infrastructure products’ durability requirements, and in line with the environmental-energy policies, transposed into EU guidelines and regulations. Focusing on the use-maintenance-adaptation stage in the constructions’ life cycle, the aim of this work is to propose a methodology for supporting the “optimal maintenance planning” in function of life cycle costs, assuming the presence of financial constraints. A first research step is proposed for testing the economic sustainability of different project options, at the component scale, which imply different cost items and different maintenance-replacement interventions over time. The methodology is based on the Annuity Method, or Equivalent Annual Cost approach, as defined by the norm EN 15459-1:2017. The method, poorly explored in the literature, is proposed here as an alternative to the Global Cost approach (illustrated in the norm as well). Due to the presence of uncertainty correlated to deterioration processes and market variability, the stochastic Annuity Method is modeled by introducing flexibility in input data. Thus, with the support of Probability Analysis and the Monte Carlo Method (MCM), the stochastic LCCA, solved through the stochastic Equivalent Annual Cost, is used for the economic assessment of different maintenance scenarios. Two different components of an office building project (a timber and an aluminum frame), are assumed as a case for the simulation. The methodology intends to support decisions not only in the design phases, but also in the post-construction ones. Furthermore, it opens to potential applications in reinforced concrete infrastructures’ stock, which is approaching, as a considerable portion of the building stock, its end-of-life stage.

Optimal Periodic Replacement Policy Under Discrete Time Frame

Systems such as database and socal network systems have been broadly used, and their unexpected failure, with great losses and sometimes a social confusion, has received attention in recent years. Therefore, it is an important issue to find optimal maintenance plans for such kind of systems from the points of system reliability and maintaining cost. However, it is difficult to maintain a system during its working cycle, since stopping works might incur users some troubles. From the above viewpoint, this paper discusses minimal repair maintenance policy with periodic replacement, while considering the random working cycles. The random working cycle and periodic replacement policies with minimal repair has been discussed in traditional literatures by usually analyzing cases for the nonstopping works. However, maintenance can be more conveniently done at discrete time and even during the working cycle in real applications. So, we propose that periodic replacement is planned at discrete times while considering the random working cycle, and moreover provide a model in which system, with a minimal repair at failures between replacements, is replaced at the minimum of discrete times KT and random cycles Y. The average cost rate model is used to determine the optimal number of periodic replacement.

Optimal maintenance planning in building retrofitting with interacting energy effects

SummaryThis paper extends a control system framework for the maintenance planning investment decision for building energy retrofitting. The interacting energy and reliability effects that are ignored by previous models are incorporated in the current study. A set of energy efficiency and population decay models with interacting parameters and decision variables are established, and a state‐space model with coupled nonlinear equations is obtained. The control objectives are maximizing the energy savings and financial paybacks with limited budget during a finite time period. An model predictive control (MPC) controller is designed for the problem. The interacting effects and effectiveness of the proposed approach are verified by the case study, where improvements from the modeling considering interaction are revealed.

Joint optimization of train scheduling and maintenance planning in a railway network: A heuristic algorithm using Lagrangian relaxation

Train scheduling and maintenance planning compete for the resources in a railway network. A commonly used way is dealing with maintenance planning first and then train scheduling, or vice versa. In this paper, we propose a joint optimization model for the two problems in a railway network with double-track, where the upstream and downstream trains are independent and a maintenance task on a section cannot be split or disrupted. In order to solve the model, a heuristic algorithm using Lagrangian relaxation is developed. Due to the large number of constraints, we use a dynamic constraint-generation technique in the iterations of the sub-gradient optimization procedure. We apply the model and algorithm to a practical problem in the Chinese railway network, in which some additional trains are inserted into a fixed existing timetable and the maintenance plan on the involved high-speed railway sections is adjusted. The computational results illustrate the effectiveness and efficiency of the proposed model and algorithm.

Design for maintenance: new algorithmic approach

PurposeThis paper proposes a new simultaneous optimization model of the industrial systems design and maintenance. This model aims to help the designer in searching for technical solutions and the product architecture by integrating the maintenance issues from the design stage. The goal is to reduce the life-cycle cost (LCC) of the studied system.Design/methodology/approachLiterature indicates that the different approaches used in the design for maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and the maintainability of a multicomponent system as well as the modeling of the dynamic maintenance. This article proposes to go further in the optimization of the product, by simultaneously characterizing the design, in terms of reliability and maintainability, as well as the dynamic planning of the maintenance operations. This combinatorial characterization is performed by a two-level hybrid algorithm based on the genetic algorithms.FindingsThe proposed tool offers, depending on the life-cycle expectation, the desired availability, the desired business model (sales or rental), simulations in terms of the LCCs, and so an optimal product architecture.Research limitations/implicationsIn this article, the term “design” is limited to reliability properties, possible redundancies, component accessibility (maintainability), and levels of monitoring information.Originality/valueThis work is distinguished by the use of a hybrid optimization algorithm (two-level computation) using genetic algorithms. The first level is to identify an optimal design configuration that takes into account the LCC criterion. The second level consists in proposing a dynamic and optimal maintenance plan based on the maintenance-free operating period (MFOP) concept that takes into account certain criteria, such as replacement costs or the reliability of the system.

Hybrid inspection-monitoring approach for optimal maintenance planning

Optimal planning of Inspection, Maintenance and Rehabilitation (IM&R) of civil engineering structures has been the focus of specialised literature during the last decades. More recently, permanent structural health monitoring started to gain increasing attention. In this article, a methodology is proposed to optimally combine conventional inspection techniques and permanent monitoring. The proposed methodology integrates dynamic Bayesian update of the belief state of the structure, based on the sensor readings, in a decision analysis framework. Uncertainties resulting from the model, the measurements, the imperfect inspections and the imperfect maintenance actions are taken explicitly into account. A numerical application of the proposed methodology, for a steel offshore structure, is presented. A sensitivity analysis of the optimal cost to relevant parameters, such as sensor configurations and inspection result uncertainties, highlights the potential benefits of the proposed approach.

A bi‐objective optimization approach for the maintenance planning of networked systems

AbstractNetworked systems, such as telecommunications, transportation, and power transmission, are critical for the economic development and social well‐being of a society and are required to keep the prescribed demand continuously satisfied. Therefore, when planning maintenance actions for such a system, the adverse influence on its reliability caused by the unavailability of the components in the process of being maintained needs to be taken into account. To deal with this problem, we propose a new bi‐objective optimization approach to determine the Pareto optimal maintenance plans for a networked system, simultaneously maximizing its reliability within the concerned planning horizon and minimizing the total maintenance cost. Both perfect and imperfect maintenance actions are considered. The proposed approach can well balance the influence of maintenance actions on a networked system's reliability during their implementation and after being completed and, thus, can help ensure its reliability of continuously satisfying the prescribed demand.

Multi-component opportunistic maintenance optimization for wind turbines with consideration of seasonal factor

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.

Multi-year maintenance planning framework using multi-attribute utility theory and genetic algorithms

This paper introduces a comprehensive framework for the development of optimal multi-year maintenance plans for a large number of bridges. A maintenance plan is said to be optimal when, within the given budget, a maximum number of bridges can be maintained in the best possible year, achieving maximum performance with minimum socio-economic impact. The framework incorporates heuristic rules, multi-attribute utility theory, discrete Markov chain process, and genetic algorithms to find an optimal balance between limited budgets and performance requirements. The applicability of the proposed framework is illustrated on an extensive case study of highway bridges. The framework enables asset owners to execute various planning scenarios under different budget and performance requirements, where each resulting plan is optimal. The focus of this study has mainly been on highway bridges, however the framework is general and can be applied to any other infrastructure asset type.

Energy-maintenance optimization for retrofitted lighting system incorporating luminous flux degradation to enhance visual comfort

Retrofitting existing buildings with energy-efficient lighting systems is an effective way to reduce their lighting energy consumption and improve lighting quality. In a lighting system, the illumination level decreases over time owing to the luminous flux degradation of lights. This degradation may cause visual discomfort if proper maintenance is not carried out. Also, energy savings decrease with the degradation of retrofitted lights. Maintenance of lighting devices requires investments for the purchase and installation of new lamps. Therefore, optimum balance is required between maintenance costs and the performance of the lighting system. This study presents an energy-maintenance optimization model, which takes into account the luminous flux degradation. Luminous flux degradation is modeled based on users’ lighting level requirements. Based on the proposed luminous flux degradation model, the energy-maintenance optimization model is formulated to find the optimal number of lamps to be replaced, maintenance schedules, and brightness dimming level, while taking into account users’ lighting level requirements, maintenance budget, and energy savings. An open-plan office is taken as a case study to illustrate the effectiveness of the proposed optimal energy-maintenance plan. It is found that the optimal energy-maintenance plan yields to 31.27 MWh energy savings in 10 years. Compared to the full maintenance, the optimal maintenance plan developed in this study reduces the total maintenance cost by 30%.

Life-cycle management of deteriorating bridge networks with network-level risk bounds and system reliability analysis

Structural deterioration poses a substantial threat to the safety, serviceability, and functionality of bridges. Since bridges are connected in transportation networks, their failure can dramatically alter traffic flow, causing immense social consequences such as large-scale traffic delay and additional vehicle operating cost. In this paper, a novel method is proposed for life-cycle management of deteriorating bridge networks. The proposed method aims at minimizing network-level risks associated with deterioration-induced bridge failure. To combat the deficiencies of existing Monte Carlo simulation-based risk assessment methods, the proposed method adopts a non-simulation approach that relies on risk bounds of deteriorating bridge networks. In particular, this method uses system reliability analysis to determine the occurrence probabilities of various bridge failure scenarios in a network. For each failure scenario considered, traffic assignment is conducted to predict the traffic flow in the damaged network and the network-level consequences. The upper and lower bounds of the network-level risk are then formulated, allowing for efficient and accurate risk assessment with only a few traffic assignment operations. Estimated by the average value of its upper and lower bounds, the network-level risk is used as the optimization objective in a metaheuristic search procedure to obtain optimal life-cycle maintenance plans including the maintenance schedule and the investment on each maintenance action. The proposed method excels in its ability to account for the impacts of unlikely, yet high-impact events, as well as its ability to obtain optimal or near-optimal life-cycle maintenance plans that can more effectively reduce risks than those obtained using simulation-based methods.