Opportunistic Maintenance Strategy Research Articles

Opportunistic maintenance strategy for wind turbine systems based on fault correlation and multi-level maintenance theory

Opportunistic maintenance strategy for wind turbine systems based on fault correlation and multi-level maintenance theory

Holistic opportunistic maintenance scheduling and routing for offshore wind farms

Despite the high growth of the offshore wind market, the economic benefits of wind energy sources are still being undermined by its high operation and maintenance expenses. On the one hand, high maintenance expenses are a direct result of offshore-specific challenges such as complex ocean meteorology, varying vessel accessibility, and shifting transportation requirements. Besides, component degradation and weather conditions largely limit the ability of turbine operators to estimate a workable maintenance time window. This study aims to develop a holistic opportunistic maintenance strategy to address the practical challenges of offshore wind farms. The proposed strategy starts by deriving the preventive maintenance interval of each turbine component based on its degradation trend and predictable cost rates. Then, each turbine is maintained in groups based on the maintenance opportunities arising from preventive maintenance, unexpected failures, and cable damage. Following that, given weather condition forecasts of key parameters (wind speed and wave height), the proposed strategy optimizes the daily allocations and vessel routes to maintain turbines in a timely and cost-effective manner. Finally, experimental results based on real-world data from an actual offshore wind farm demonstrate that the proposed strategy outperforms several universal maintenance strategies in key metrics. Compared to the simple, widely employed, and easy-to-implement strategies, it can help reduce the total cost by 85.9 %, 65.9 %, and 41.6 %, respectively. This work helps turbine operators implement comprehensive and cost-effective maintenance schemes, which can lead to tariff duction and wind farm promotion benefits in the long term.

Opportunistic maintenance for a novel load-sharing system with dependent degradation rate and volatility

For traditional degrading load-sharing systems, a typical assumption lies in the only effect of unit load on its degradation rate. However, motivated by a laser system example with positive correlation between degradation rate and volatility, we present a novel load-sharing system with general degradation rate-volatility-load correlation. Furthermore, to take account for unit economic dependence caused by sharable maintenance set-up cost, and the window period generated by the maintenance on some unit, opportunistic maintenance (OM) strategy is introduced to offer the opportunity of simultaneously maintaining another working unit under control-limit criteria. Based on the semi-regenerative process, the long-run average cost rate is minimized to obtain the optimal inspection interval, preventive maintenance and OM thresholds. Compared to the traditional condition-based maintenance strategy separately for each unit, the effectiveness of OM strategy is validated through numerical examples and case studies, offering broad managerial insights for engineers to reduce maintenance expenses.

Digital twin-enhanced opportunistic maintenance of smart microgrids based on the risk importance measure

Smart microgrids face more diverse and frequent risks than traditional grids due to their complexity and reliance on distributed generation. Ensuring the reliable operation of smart microgrids requires effective maintenance. Traditional maintenance methods, based on periodic inspections and fault diagnosis, struggle to adapt to the dynamics and complexity of microgrid systems. The introduction of digital twin technology provides a new solution for the opportunistic maintenance of microgrid systems. This paper presents a digital twin microgrid architecture for real-time monitoring and decision-making in opportunistic maintenance. Meanwhile, this paper introduces a risk importance measure to aid to optimize opportunistic maintenance strategies when resources are limited. Finally, a wind-solar-storage microgrid is used to illustrate the proposed method. Experimental results show that the proposed method significantly reduces maintenance costs and improves system reliability, effectively supporting microgrid maintenance.

Hybrid group opportunistic maintenance strategy for offshore wind turbines considering maintenance resource allocation in harsh working environments

The intelligent maintenance of offshore wind turbines (OWTs) is vital for sustainable energy generation and utilization, as harsh working environments generally limit their application. As offshore wind farms expand into deeper waters, operational and maintenance costs continue to increase. Therefore, devising targeted maintenance strategies for offshore wind farms is crucial. Existing strategies often overlook the role of maintenance resources in controlling the operational and maintenance costs. To address this problem, this study proposes a hybrid group opportunistic maintenance strategy to implement resource allocation-based maintenance for OWTs under dynamic conditions. First, a failure rate model based on the life decline and system coupling is proposed to capture the evolution of failures in OWTs in harsh working environments, thereby enhancing the accuracy of fault maintenance. Subsequently, a hybrid linear and nonlinear deterioration model is proposed to identify the different deterioration trends owing to the various sensitivities of diverse components to harsh working environments, which enhances the precision of degradation maintenance. In addition, a maintenance resource allocation model is proposed to allocate resources in advance, thereby increasing resource utilization and reducing maintenance costs. A multi-objective optimization approach is proposed to maximize the mean maintenance level (MML) and minimize the per unit cycle expected maintenance cost rate (PECR) by considering harsh working environments. Finally, the nutcracker optimization algorithm is introduced into the maintenance model to generate the optimal solution in a multi-objective manner. The results show that the maintenance model is feasible and effective for practical adaptive maintenance applications of OWTs in harsh working environments in practical applications.

Efficient opportunistic maintenance strategies via pruning in parallel–series systems with economic dependence

Opportunistic maintenance stands out as an effective strategy for minimizing maintenance costs and enhancing system efficiency. Despite its importance, current opportunistic maintenance models are often tailored to specific system types, particularly those with series structures, or they offer heuristic approaches for multi-component redundant systems. This paper shifts focus to the most common of such systems, the parallel–series system. We approach the optimization of opportunistic maintenance in a parallel–series system through a Markov Decision Process framework, utilizing phase-type approximations of individual component hazard functions. Addressing the challenge of the curse of dimensionality in the action space, this study conducts a thorough structural analysis of the optimal opportunistic maintenance policy, deriving a pruning procedure, which can reduce the extensive combinatorial action space into a more manageable linear complexity without sacrificing the strategy’s effectiveness in parallel–series systems. Building on this more manageable action space, we introduce a more efficient algorithm leveraging the proposed pruning procedure for determining the optimal opportunistic maintenance policy. The effectiveness and practical applicability of this approach are rigorously demonstrated through comprehensive numerical experiments.

An opportunistic joint maintenance strategy for two offshore wind farms

The expansion of offshore wind farms into deeper waters presents economic challenges due to the rising costs of operation and maintenance. To address this issue, the development of targeted opportunistic maintenance strategies for offshore wind farms is crucial. Existing strategies often overlook the interconnections between maintenance activities across wind farms, focusing solely on individual wind farms. However, joint maintenance of wind farms has been acknowledged as an effective approach to reducing maintenance costs at the operational level. This paper introduces an opportunistic maintenance strategy that considers the joint maintenance of two wind farms and establishes reliability thresholds to guide maintenance decisions. Furthermore, the developed maintenance model is improved by incorporating more realistic vessel models and considering various types of maintenance costs. Conclusions can be drawn from a comprehensive analysis of optimization results across multiple scenarios involving maintenance costs, wind farm parameters, and uncertainties. The comparative analysis demonstrates that, in the farthest offshore distance case, the proposed strategy exhibits a reduction in maintenance costs of 15.67% and 4.87% when compared to two traditional strategies, respectively. This effect is further amplified in scenarios where offshore wind farms are located in close proximity to each other and encounter substantial uncertainties.

Condition-based opportunistic maintenance strategy for multi-component wind turbines by using stochastic differential equations

The components of wind turbines are complex in structure and the working environment is harsh, which makes wind turbines face problems such as high failure rates and high maintenance costs. In this paper, the stochastic differential equation model has been established for the harsh operating environment of wind turbines, and used Brownian motion to simulate random disturbances; aiming at the problem of high failure rate of wind turbines, based on Weibull distribution, a new model has been established by combining operating time and equipment state to calculate the failure rate; in the analysis of monitoring data, the Higher-Order Moment method and Bayesian method were used to solve the parameters. The opportunity maintenance threshold curve and preventive maintenance threshold curve were obtained by analyzing Time-Based Maintenance and Condition-Based Maintenance. Therefore, the Condition-Based Opportunistic Maintenance strategy was obtained. The effectiveness of the proposed method was finally verified by arithmetic examples.

Opportunistic maintenance strategies for PV power systems considering the structural correlation

Opportunistic maintenance strategies for PV power systems considering the structural correlation

Optimal external opportunistic maintenance for wind turbines considering wind speed

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

Opportunistic maintenance strategy optimization considering imperfect maintenance under hybrid unit-level maintenance strategy

Opportunistic maintenance is a widely adopted strategy for maintaining multi-unit systems, which has garnered significant attention from researchers. However, the current opportunistic maintenance still suffers from certain limitations, such as single preventive maintenance strategy at unit-level and lack of flexibility in optimization model. In this paper, an optimization model for scheduled maintenance (SM) and condition-based maintenance (CBM) at the unit-level is presented, and the associated maintenance costs are analyzed. Subsequently, a preventive opportunistic maintenance strategy optimization model under hybrid unit-level maintenance strategies is established by considering imperfect preventive maintenance. An optimization algorithm based on Monte Carlo simulation is designed to solve the model. Finally, the opportunistic maintenance strategy of CNC gear grinding machine is developed. Comparing the opportunistic maintenance strategy given in this paper with other maintenance strategies, the results show that the maintenance strategy presented in this paper can significantly reduce the maintenance cost of the CNC grinding machine. The opportunistic maintenance strategy optimization method proposed in this paper can provide guidance for relevant enterprises to formulate maintenance strategies.

Energy-oriented opportunistic maintenance optimization of continuous process manufacturing systems with two types of stochastic durations

For continuous process manufacturing systems (CPMSs) where the production process cannot be stopped, the “opportunities” for maintenance can only occur within the specified time intervals between two production batches. Moreover, opportunistic maintenance of CPMSs is not only to reduce downtime losses and maintenance costs, but also to improve productivity and energy efficiency. However, existing studies ignored stochastic uncertainties of production batch duration and maintenance duration, which can lead to overestimation of total benefit and reliability, and increase the risk of accidents. Therefore, an energy-oriented opportunistic maintenance (EOM) strategy for CPMSs with stochastic durations is proposed. The stochastic opportunity time window (SOTW) is introduced to characterize the uncertain “opportunity” of maintenance caused by the above-mentioned stochastic uncertainties. And, a stochastic flow manufacturing network (SFMN) is established to evaluate machine reliability and energy consumption under the internal and external uncertainties. Moreover, the optimization objective of EOM that takes into account energy, production and maintenance simultaneously is to maximize the expected system benefits by selecting appropriate maintenance actions during the SOTWs. Then, a genetic algorithm with multiple evolution strategies (GAMES) is developed to address the optimization problem. Finally, a case study is provided to verify the effectiveness of the proposed method.

Opportunistic maintenance modeling for series production systems based on bottleneck by considering energy consumption and market demand

ABSTRACT Nowadays, series production systems are broadly used in various branches of industry. In these kinds of production systems, any disruption in production flow can significantly increase production cost and time; therefore, a proper maintenance strategy considerably reduces the company’s costs and enhance organizational performance. In this research, a novel bottleneck-centered opportunistic maintenance policy for a series production line is proposed. First of all, the bottleneck is detected by the Lowest Production Rate method. Then, preventive maintenance time interval, reliability threshold, and number of maintenance cycles are determined by the particle swarm optimization algorithm. Finally, a novel opportunistic maintenance strategy is proposed based on analyzing the cost savings of preventive maintenance advancement and postponement. To demonstrate the model’s effectiveness, the proposed bottleneck-based opportunistic maintenance model is compared with two other maintenance models, and the results confirmed that the proposed strategy could efficiently eliminate unnecessary production breaks and achieve cost reduction.

Opportunistic Maintenance Strategy for Complex Equipment with a Genetic Algorithm Considering Failure Dependence: A Two-Dimensional Warranty Perspective.

Complex two-dimensional warranty equipment is usually composed of many multi-component systems, which include several key components. During the warranty period, conducting maintenance according to the preventive maintenance plan of each component will increase the warranty costs. Opportunistic maintenance is an effective approach to combine the preventive maintenance of each individual component, which can reduce the warranty cost and improve the system availability. This study explored the optimal opportunistic maintenance scheme of multi-component systems. Firstly, the failure rate model and reliability evaluation model of the multi-component system considering failure dependence were established. Secondly, the preventive maintenance plan of each individual component was determined, with the goal of obtaining the lowest warranty cost per unit time in the component life cycle. Thirdly, the preventive maintenance work of each individual component was combined, and the two-dimensional warranty cost model of the multi-component system was established according to the reliability threshold when performing opportunistic maintenance. In the experimental verification and result analysis, the genetic algorithm was used to find the optimal opportunistic maintenance scheme for the power transmission device. The comparative analysis results show that the opportunistic maintenance scheme reduced the warranty cost by 5.5% and improved the availability by 10%, which fully verified the effectiveness of the opportunistic maintenance strategy.

Real-Time Maintenance Policy Optimization in Manufacturing Systems: An Energy Efficiency and Emission-Based Approach

Within the frame of the Fourth Industrial Revolution, the application of Internet of Things technologies makes it possible to convert conventional manufacturing systems into cyber-physical systems, where the used new technologies enable the improvement of maintenance and operation processes. The aim of this work is to develop and validate a new real-time maintenance policy model and optimization algorithm based on digital twin simulation. The maintenance policy model is based on the conventional failure and operation data from ERP (enterprise resource planning) and the real-time and forecasted failure and operation data from digital twin simulation. The described maintenance policy model and its optimization algorithm represent an innovative way to manage predictive, preventive, corrective, and opportunistic maintenance strategies. The novelty of the presented method is that the real-time data generated by the digital twin solution allow the definition of a more accurate maintenance strategy. The optimization algorithm is characterized by a standard evolutionary algorithm. The impact of maintenance policy optimization on the energy efficiency and emission was analyzed in the case of both conventional and cyber-physical manufacturing systems with and without digital-twin-based simulation. The results showed that the energy consumption and the greenhouse gas emission in the real-time maintenance policy optimization scenario decreased by 21%, depending on the electricity generation source.

Joint optimization of production and maintenance for a serial–parallel hybrid two-stage production system

Effective machine maintenance and job scheduling strategies are two critical interactive tasks to upgrade the production efficiency in manufacturing industries. In this study, we mainly consider the interaction between production scheduling and maintenance, while optimizing the job scheduling problem in a serial–parallel hybrid two-stage production system. We formulate the problem by introducing the stochastic and workload-ratio degradation, and the actual job completion time and maintenance costs can be modeled according to the degradation status of each machine. To solve this problem, a two-step opportunistic maintenance (OM) strategy is developed, and finally an adaptive random-key genetic algorithm (GA) is designed by incorporating the OM coefficients. Numerical studies have been conducted to validate the proposed approach, and the results indicate the effectiveness compared to three existing methods under varying machine degradation scenarios.

A real-time inspection and opportunistic maintenance strategies for floating offshore wind turbines

This paper proposes an FMEA-BN model to determine the inspection and opportunistic maintenance strategies of floating offshore wind turbines. A mapping algorithm is proposed to establish a mirrored Bayesian Network (BN) model from a given failure mode and effect analysis (FMEA) structure to realize the FMEA-BN modelling, which is efficient to consider common cause failures. The failure probabilities of items of floating offshore wind turbines are first updated by the BN sub-model, in which, various operation scenarios are considered. The updated failure probabilities are then imported to the FMEA sub-model to determine the items of the floating offshore wind turbines that are to be inspected and to which the opportunistic maintenance action would be applied. With the FMEA-BN model, inspection and opportunistic maintenance strategies for a floating offshore wind turbine are suggested under several commonly occurring operation scenarios. The validation of the results is illustrated by the failure rate of the floating offshore wind turbine predicted by the BN sub-model, the uncertainty of which is lower than 3%. Overall, the presented FMEA-BN model supports real-time inspection and opportunistic maintenance strategies determination of complicated systems like floating offshore wind turbines.

Integrated Structural Dependence and Stochastic Dependence for Opportunistic Maintenance of Wind Turbines by Considering Carbon Emissions

Wind turbines have a wide range of applications as the main equipment for wind-power generation because of the rapid development of technology. It is very important to select a reasonable maintenance strategy to reduce the operation and maintenance costs of wind turbines. Traditional maintenance does not consider the environmental benefits. Thus, for the maintenance problems of wind turbines, an opportunistic maintenance strategy that considers structural correlations, random correlations, and carbon emissions is proposed. First, a Weibull distribution is used to describe the deterioration trend of wind turbine subsystems. The failure rates and reliability of wind turbines are described by the random correlations among all subsystems. Meanwhile, two improvement factors are introduced into the failure rate and carbon emission model to describe imperfect maintenance, including the working-age fallback factor and the failure rate increasing factor. Then, the total expected maintenance cost can be described as the objective function for the proposed opportunistic maintenance model, including the maintenance preparation cost, maintenance adjustment cost, shutdown loss cost, and operation cost. The maintenance preparation cost is related to the economic correlation, and the maintenance adjustment cost is described by using the maintenance probabilities under different maintenance activities. The shutdown loss cost is obtained by considering the structural correlation, and the operation cost is related to the energy consumption of wind turbines. Finally, a case study is provided to analyze the performance of the proposed model. The obtained optimal opportunistic maintenance duration can be used to interpret the structural correlation coefficient, random correlation coefficient, and sensitivity of carbon emissions. Compared with preventive maintenance, the proposed model provides better performance for the maintenance problems of wind turbines and can obtain relatively good solutions in a short computation time.

A new integrated strategy for optimizing the maintenance cost of Production systems using reliability importance measures

This paper presents an opportunistic condition-based maintenance strategy for multi-component systems. This strategy allows the selection of systems’ components to be replaced or to undergo maintenance repairs based on the effect of importance measure concepts (IMC). IMC models proceeds by determining the contribution of each component in terms of success or failure degrees respecting two factors: (1) the probability of components to remain in operations under various conditions, and (2) the location of such components in the system structure. However, evaluating IMC factors is not so easy for complex systems; it requires knowing their operational structure, their reliability, and the calculation of the IMC degree and ranking them for the selection. Then, the IMC results are integrated into an opportunistic maintenance strategy to constitute a maintenance system's framework. In addition, a multicomponent system composed of 14 nodes and 23 links is provided to illustrate the proposed maintenance strategy that incurs a maximum of cost saving and to reduce spare parts unnecessarily usage.

Optimizing opportunistic preventive maintenance strategy for multi-unit system of CNC lathe

Optimizing opportunistic preventive maintenance strategy for multi-unit system of CNC lathe