Optimal Maintenance Strategy Research Articles

Importance measure-based maintenance optimization strategy for pod slewing system

The pod slewing system is a complex hydraulic system that can realize the ship's yawing or reversing. Thus, its performance directly affects the maneuverability and flexibility of the ship. Existing research on the pod slewing system has mostly focused on its design and control aspects. This paper makes contributions by modeling the reliability of the pod slewing system and performing the component importance analysis using the integrated importance measure and the griffith importance measure to identify weak or vulnerable components of the system. Based on the importance analysis results, the optimal maintenance strategy is investigated, which maximizes the system performance subject to the constraint on the total maintenance costs. Example solutions to the optimized maintenance strategies are demonstrated for different mission time and different cost constraints.

Risk assessment of FPSO topside based on generalized Stochastic Petri Net

This paper presents a Generalized Stochastic Petri Net (GSPN) approach to assess risk of FPSO topsides, considering the weather window and imperfect maintenance effect. The FPSO topside is considered as a multi-component system consisting of several assemblies. A GSPN model is developed according to system identification and the correlation between different components. The reliability, availability, and maintenance information of the system are derived using the model, based on failures collected from existing literature and database. An operation and maintenance cost model is established, and an optimized maintenance strategy is finally determined based on the GSPN outcomes. The results indicate that the proposed approach is an effective tool to evaluate the system risk and save the maintenance expenditure of FPSO topsides.

Developing An Optimization Model For Pavement Maintenance Planning And Resource Allocation In Hossana Town, Ethiopia

Pavement Management System is designed to provide objective information and useful data for analysis so that road managers can make more consistent, cost-effective, and defensible decisions related to the preservation of a pavement network. During the process of road network maintenance and rehabilitation, road authorities strive to select an optimum maintenance strategy from a number of alternatives. Mathematical optimization models, supported by suitable data, can assist decision making about allocating funds between alternative maintenance tasks and about the size of the maintenance budget. It can be done through the analysis of costs and benefits by comparing the various maintenance alternatives with the help of an optimization method known as solver. The road segment mainly included in study was road from Hosanna Menhariya to Wachemo University and other important access roads. These roads are divided into different sections in not more than 100m length. The Study involves data collection, data analysis and the selection of optimal maintenance strategy by using a method known as Solver (Add-ins in Microsoft excel). In this study, patching was selected as possible maintenance among the other alternatives. The result of solver analysis for patching indicates that as 74,574 birr allocated for the maintenance of pavement per kilometer in different three segments under the municipality having the constraint budget of 152,018.45 birr/km. The optimized solution shows that about 20962.5 birr would be saved in one year per km with in municipality.

Maintenance cost assessment for heterogeneous multi-component systems incorporating perfect inspections and waiting time to maintenance

Most existing research about complex systems maintenance assumes they consist of the same type of components. However, systems can be assembled with heterogeneous components (for example degrading and non-degrading components) that require different maintenance actions. Since industrial systems become more and more complex, more research about the maintenance of systems with heterogeneous components is needed. For this reason, in this paper, a system consisting of two groups of components: degrading and non-degrading components is analyzed. The main novelty of this paper is the evaluation of a maintenance policy at system-level coordinating condition-based maintenance for the degrading components, delay time to the maintenance and an inspection strategy for this heterogeneous system. To that end, an analytic cost model is built using the semi-regenerative processes theory. Furthermore, a safety constraint related to the reliability of the degrading components is imposed. To find the optimal maintenance strategy, meta-heuristic algorithms are used.

Condition-Based Maintenance Modeling and Reliability Assessment for Multi-Component Systems with Structural Dependence under Extended Warranty

For engineering and production systems, due to the structural dependence between components, the disassembly operation caused by the replacement of components will affect the failure and degradation processes of other components in the system. In order to optimize the extended warranty (EW) cost of the multi‐component system with structural dependence, this paper described the structural dependence and modeled the disassembly operation impact, and then the failure rate model of the component considering the impact of disassembly operation under EW was developed. Combined with the actual situation, a condition-based maintenance (CBM) strategy was employed to construct the EW cost model of the multi‐component system with structural dependence. Monte Carlo simulation was proposed to determine the optimal EW cost of the system and the optimal periodic inspection interval of the CBM strategy. Finally, a numerical example of the planetary gear train of an automobile generator is introduced to demonstrate the feasibility and advantages of the proposed model in EW cost optimization and the analysis of disassembly operation impact on the optimal maintenance strategy.

Cost-benefit analysis of calibration model maintenance strategies for process monitoring

The long-term prediction performance of spectroscopic calibration models is a critical factor to monitor or control many production processes. Over time, new variations may emerge that deteriorate prediction performance. Therefore, models have to be maintained to retain or improve their prediction performance through time, requiring considerable resources and data. Maintenance should improve relevant predictions but also needs to be resource and cost efficient. Current approaches do not consider these trade-offs. We propose a new method to quantify the effectiveness and cost of model maintenance strategies based on historical data. Model performance over time for past, imminent and future samples is evaluated as these may react differently to maintenance. The model performance and required updating resources are translated into relative cost and benefit to compare strategies and determine optimal maintenance parameters. We used this method to evaluate a maintenance strategy that combines adding incoming samples to the calibration data with re-optimization of spectral preprocessing and modelling parameters. Continuously adding samples to the calibration data is shown to improve prediction performance and leads to more robust and generic models for emerging variations in all investigated data streams. Selectively adding incoming sample variations showed a reduced prediction performance but saves considerably in resources. Comparing model performance on the different sampling windows can also be used to determine an optimal updating frequency. This novel strategy to evaluate the expected performance and determine an optimal maintenance strategy is generally applicable and should lead to robust and consistently high prospective and/or retrospective model performance through time, which can be crucial for optimal operation and fault detection in industrial processes.

Multi-Objective Optimum Design and Maintenance of Safety Systems: An In-Depth Comparison Study Including Encoding and Scheduling Aspects with NSGA-II

Maximising profit is an important target for industries in a competitive world and it is possible to achieve this by improving the system availability. Engineers have employed many techniques to improve systems availability, such as adding redundant devices or scheduling maintenance strategies. However, the idea of using such techniques simultaneously has not received enough attention. The authors of the present paper recently studied the simultaneous optimisation of system design and maintenance strategy in order to achieve both maximum availability and minimum cost: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was coupled with Discrete Event Simulation in a real encoding environment in order to achieve a set of non-dominated solutions. In this work, that study is extended and a thorough exploration using the above-mentioned Multi-objective Evolutionary Algorithm is developed using an industrial case study, paying attention to the possible impact on solutions as a result of different encodings, parameter configurations and chromosome lengths, which affect the accuracy levels when scheduling preventive maintenance. Non-significant differences were observed in the experimental results, which raises interesting conclusions regarding flexibility in the preventive maintenance strategy.

개선된 Fuzzy-RPN 기법을 활용한 전력설비 유지보수 우선순위 결정에 관한 연구

Due to the rapid development of industry and the introduction of renewable energy, power system has became more complex and had various problems. The operator of power utilities have made efforts in enhancing the efficiency based on the characteristics of these facilities. This paper suggests an optimal maintenance strategy for electric power system based on a reliability assessment. The reliability assessment is performed by risk priority number(RPN) developed by severity(S), occurrence(O), detection(D). The traditional RPN method for ranking and assessing the risk of potential failure mode have a crucial problem to be considered on the analysis results. The proposed RPN method using Fuzzy inference system is a method to resolve the ambiguity of the results with the traditional condition assessment method. The New Fuzzy-RPN method is tested using actual recorded data of power system.

Fuzzy-Grey Relational Analysis Applied to Optimal Maintenance Strategy Selection: The Case of Egbin Thermal Power Station, Nigeria

Fuzzy-Grey Relational Analysis Applied to Optimal Maintenance Strategy Selection: The Case of Egbin Thermal Power Station, Nigeria

A Novel Decision Support System for Long-Term Management of Bridge Networks

Developing a bridge management strategy at the network level with efficient use of capital is very important for optimal infrastructure remediation. This paper introduces a novel decision support system that considers many aspects of bridge management and successfully implements the investigated methodology in a web-based platform. The proposed decision support system uses advanced prediction models, decision trees, and incremental machine learning algorithms to generate an optimal decision strategy. The system aims to achieve adaptive and flexible decision making while entailing powerful utilization of nondestructive evaluation (NDE) methods. The NDE data integration and visualization allow automatic retrieval of inspection results and overlaying the defects on a 3D bridge model. Furthermore, a deep learning-based damage growth prediction model estimates the future condition of the bridge elements and utilizes this information in the decision-making process. The decision ranking takes into account a wide range of factors including structural safety, serviceability, rehabilitation cost, life cycle cost, and societal and political factors to generate optimal maintenance strategies with multiple decision alternatives. This study aims to bring a complementary solution to currently in-use systems with the utilization of advanced machine-learning models and NDE data integration while still equipped with main bridge management functions of bridge management systems and capable of transferring data to other systems.

Maintenance Strategy Optimization for a Hose Pulling Machine in a Medical Plant Producing the Hose Circuits of Breathing Devices

Dünya’da yaşanan COVID-19 küresel salgını bütün sektörler arasında rekabeti günden güne arttırmaktadır. Artan bu rekabette özellikle sağlık malzemeleri üretimi yapan işletmelerin, üretimde devamlılığını sağlamak ve verimliliği arttırmak ana hedefidir. Arıza durumlarını beklemeden doğru bakım faaliyetlerinin gerçekleştirilmesi makine ve ekipmanların etkin çalışma ömürlerinin uzatılmasına, üretimin sürdürülebilir olması ve verimliliğinin arttırılması açısından kritik öneme sahiptir. Bu sebeple bakım faaliyetlerinin planlı bir şekilde gerçekleştirilmesi amacıyla en uygun bakım stratejiisinin seçilmesi gerekmektedir. Bu çalışmanın amacı salgında önemi artan solunum cihazları için hortum devreleri üreten bir medikal işletmesinin sürekliliğini ve verimliliğini arttırmak amacıyla hortum çekme makinesi için en uygun bakım stratejisinin seçilmesidir. İlk olarak hortum çekme makinesi için tüm kriterler firma yetkilisiyle görüşülerek ve literatürden yararlanılarak belirlenmiş, uzman kişiler tarafından değerlendirilmiş ve ANP yöntemi kullanılarak ağırlıklandırılmıştır. Bulunan kriter ağırlıkları SuperDecision programına manuel olarak girilerek arızi, periyodik, duruma dayalı ve revizyon bakımdan oluşan dört strateji arasından en uygun bakım stratejisi seçilmiştir. Bulanık ANP sonucunun kabul edilebilir olduğundan emin olmak için TOPSIS ve ELECTRE yöntemleri de uygulanarak sıralaması yapılmıştır.

Optimal maintenance and warranty strategy for second-hand product with periodic preventive maintenance action

Optimal maintenance and warranty strategy for second-hand product with periodic preventive maintenance action

Predictive Maintenance and Sensitivity Analysis for Equipment with Multiple Quality States

This paper discusses the predictive maintenance (PM) problem of a single equipment system. It is assumed that the equipment has deteriorating quality states as it operates, resulting in multiple yield levels represented as system observation states. We cast the equipment deterioration as discrete-state and continuous-time semi-Markov decision process (SMDP) model and solve the SMDP problem in reinforcement learning (RL) framework using the strategy-based method. In doing so, the goal is to maximize the system average reward rate (SARR) and generate the optimal maintenance strategy for given observation states. Further, the PM time is capable of being produced by a simulation method. In order to prove the advantage of our proposed method, we introduce the standard sequential preventive maintenance algorithm with unequal time interval. Our proposed method is compared with the sequential preventive maintenance algorithm in a test objective of SARR, and the results tell us that our proposed method can outperform the sequential preventive maintenance algorithm. In the end, the sensitivity analysis of some parameters on the PM time is given.

The Semi-Markov model of operation and maintenance of gas analytical system

Gas analytical systems are one of the key elements for the safe operation of airports and ground airspace infrastructure facilities where there is a risk to life or property caused by the possible combustible gases leaks. The gas analytical systems provides safety operation of airspace infrastructure facilities through the early detection of combustible gases and the issuance of warning signal; therefor the gas analytical systems is imposed high operational reliability requirements ensured by an optimal maintenance strategy. Known methods of maintenance management could not consider operation features and gas analytical systems structure complexity therefore increase cost of maintenance of airspace infrastructure facilities. The developed semi-Markov model of operation and maintenance of gas analytical systems, which takes into account the frequency and duration of control, the intensity of operation, the reliability of components, quantity of the spare parts and the gas analytical systems complexity makes it possible to design optimal maintenance strategy and therefore to reduce the cost of maintenance of ground airspace infrastructure facilities by reducing insupportable wear out of gas analytical systems and to reduce capital expenditures by excluding an excessive amount of the spare parts.

A fuzzy DEMATEL-ANP-VIKOR analytical model for maintenance strategy selection of safety critical assets

In most industries, such as aerospace, manufacturing, transport and energy sectors, maintenance plays a vital role in improving the performance of safety critical equipment and facilities. It also helps industries achieve the largest possible efficiency, ensure workplace and environmental safety, and reduce unnecessary breakdowns and costs. Therefore, it is crucial for industries to adopt an optimal maintenance strategy for their critical systems and infrastructure. In this study, we aim to propose a novel analytical multi-criteria decision-making (MCDM) methodology for selecting the most suitable maintenance strategy in distillation units of oil refinery plants. The alternative maintenance strategies include run-to-failure (RTF), preventive maintenance (PM), condition-based maintenance (CBM), and reliability centered maintenance (RCM), which are evaluated with respect to 12 sub-criteria in three categories of economical, safety, and sustainability issues. The MCDM methodology consists of a DEMATEL-based analytic network process (ANP) method to determine the importance weights of decision criteria and a VIKOR method to rank the maintenance strategies. Also, interval type-2 fuzzy sets are used to capture uncertainty in experts’ individual judgments. Finally, a real case study is provided to show the applicability of the proposed methodology to an oil refinery plant. The results show that, thanks to advances in degradation modeling, sensor technology, and data analytics platforms, the RCM and CBM are the superior maintenance strategy for crude oil distillation systems.

Bayesian maintenance decision optimisation based on computing the information value from condition inspections

A challenge in marine and offshore engineering is structural integrity management (SIM) of assets such as ships, offshore structures, mooring systems, etc. Due to harsh marine environments, fatigue cracking and corrosion present persistent threats to structural integrity. SIM for such assets is complicated because of a very large number of rewelded plates and joints, for which condition inspections and maintenance are difficult and expensive tasks. Marine SIM needs to take into account uncertainty in material properties, loading characteristics, fatigue models, detection capacities of inspection methods, etc. Optimising inspection and maintenance strategies under uncertainty is therefore vital for effective SIM and cost reductions. This paper proposes a value of information (VoI) computation and Bayesian decision optimisation (BDO) approach to optimal maintenance planning of typical fatigue-prone structural systems under uncertainty. It is shown that the approach can yield optimal maintenance strategies reliably in various maintenance decision making problems or contexts, which are characterized by different cost ratios. It is also shown that there are decision making contexts where inspection information doesn’t add value, and condition based maintenance (CBM) is not cost-effective. The CBM strategy is optimal only in the decision making contexts where VoI > 0. The proposed approach overcomes the limitation of CBM strategy and highlights the importance of VoI computation (to confirm VoI > 0) before adopting inspections and CBM.

Reliability-Based Lifetime Fatigue Damage Assessment of Offshore Composite Wind Turbine Blades

This paper presents a method for stochastic deterioration modeling and fatigue damage assessment for composite wind turbine blades operating in offshore environments. The fatigue damage of the composite blades is analyzed and assessed based on the estimates for the applied loads along the blade span, stress analysis, fatigue crack evolution, and lifetime probability of fatigue failure. The complex stress states of the blade are mainly caused by the aerodynamic loads generated by corrected blade element momentum theory, gravity loads, and centrifugal loads. The fatigue of the wind turbine blade is then investigated on the basis of the actual fatigue damage propagation process. The stochastic gamma process is introduced to calculate the probability of fatigue failure of the blade for various critical limits, and these results together with lifecycle cost analysis are employed to determine the optimum maintenance strategy. Finally, a numerical example for a National Renewable Energy Laboratory 5-MW wind turbine blade is adopted to demonstrate the applicability of the proposed method. The numerical results show that the proposed approach can provide a reliable tool for estimating stress states, evaluating fatigue damage, analyzing lifetime fatigue failure probability, and optimizing repair time of the composite wind turbine blade.

A novel approach to optimize the maintenance strategies: a case in the hydroelectric power plant

Countries need to develop sustainable energy policies based on the principles of environmental sensitivity, reliability, efficiency, economy and uninterrupted service and to maintain their energy supply in order to increase their global competitiveness. In addition to this impact of sustainable energy supply on the global world, maintenance processes in power plants require high costs due to allocated time, materials and labor, and generation loss. Thus, the maintenance needs to be managed within a system. This makes analytical and feasible maintenance planning a necessity in power plants. In this context, this study focuses on maintenance strategy optimization which is the first phase of maintenance planning for one of the large-scale hydroelectric power plants with a direct effect on Turkey's energy supply security with its one fifth share in total generation. In this study, a new model is proposed for the maintenance strategy optimization problem considering the multi-objective and multicriteria structure of hydroelectric power plants with hundreds of complex equipment and the direct effect of these equipment on uninterrupted and cost-effective electricity generation. In the model, two multi-criteria decision-making methods, AHP and COPRAS methods, are integrated with integer programming method and optimal maintenance strategies are obtained for 571 equipment.

Highway pavement maintenance optimisation using HDM-4: a case study of Morocco's arterial network

ABSTRACT One of the key objectives of a pavement management system (PMS) is to find the optimal maintenance strategy, which minimises the sum of a road agency and user costs by maximising the net benefit to society. The PMS helps road agencies to better apply a selection of the right pavement treatments. The objective of the present paper is to show how the HDM-4 (Highway Development and Management 4) approach allows to develop a policy road leading to the optimal and robust selection of maintenance strategies under budgetary constraints, which minimises the sum of agency costs and road user costs in present value or maximises the net benefit to society over an analysis period. The findings could help road agencies to better apply the right pavement treatments. The present study is based on the exploitation of road network data, obtained from pavement structural condition survey and performed by the National Studies and Road Research Center. The study applies a long-term simulation (20 years) of maintenance budget needs under different budget scenarios.

Optimal maintenance strategy for multi-state systems with single maintenance capacity and arbitrarily distributed maintenance time

In engineering scenarios, failures of some components in a system may not always lead to the failure of an entire system. In such cases, the system can continuously operate while some components are being repaired. On the other hand, due to limited maintenance capacity, such as manpower and/or repair facility, maintenance actions can only be executed serially rather than in parallel. In this study, a new maintenance optimization problem for multi-state systems with single maintenance capacity is studied. The homogeneous continuous-time Markov process is used to characterize the deterioration of multi-state components in a system. In contrast to the exponential assumption for the distribution of maintenance time in most reported works, the time for each maintenance task can be arbitrarily distributed in our study. The embedded Markov chain is constructed to model the state transition process of a system by introducing decision epochs. Two optimization problems are formulated by treating either the stationary availability or the expected performance capacity of a system as an objective under the constraint of the average maintenance cost per unit time. The genetic algorithm is customized to resolve the resulting optimization problems. An illustrative example is given to demonstrate the effectiveness of the proposed method.