Opportunistic Maintenance Strategy Research Articles

Maintenance cost minimization models for offshore wind farms: A systematic and critical review

Feasible and sustainable source of power is a burning question nowadays. The offshore wind farms can be a solution to power generation problem, but it is relatively more expensive. Its installation cost is more than twice of its similar size onshore counterpart. The cost that matters most is the operations and maintenance (O&M) cost. Expensive and sophisticated transfer vehicles as well as highly skilled technicians are needed to conduct O&M activities, which results in a remarkably higher O&M cost for an offshore wind farm project. Few researchers proposed some strategies that could resolve the problem nicely, although there is no universal maintenance strategy, which will fit all the conditions. Optimal maintenance strategy depends on a lot of factors such as cost of energy, level of reliability needed, weather conditions, availability of skilled technicians, availability of crew transfer vehicles, and to mention a few. Total 190 research articles related to offshore maintenance have been reviewed, and some prominent models have been discussed in detail. Some risk and reliability-based models reduced annual maintenance cost by 23%, whereas some other opportunistic maintenance strategy was able to minimize 32% of production loss and transportation cost. Compatibility and usability of different models and results are highlighted. This critical review is aimed at identifying the relevant research outcomes and comparing their critical aspects to provide a good guideline about optimal maintenance strategy to the maintenance managers.

An opportunistic maintenance strategy for wind turbines

Maintenance strategies aim at reduction of the operation and maintenance cost of wind turbines. An opportunistic maintenance strategy offers cost advantages over the traditional preventive replacement maintenance strategy by considering cost dependency among maintenance activities. A new opportunistic maintenance strategy of wind turbines is proposed here. First, the proposed strategy includes two sets of reliability thresholds to handle subassemblies those are in operational and failed wind turbines. Using the reliability thresholds, ten maintenance modes and their service conditions are presented. Second, the Weibull distribution with an age reduction factor is introduced to describe reliability variation of subassemblies under imperfect maintenance. Third, the maintenance cost in each maintenance mode is expressed by a reliability variation-based function that is dependent on the stage of maintenance activities. Fourth, due to the randomness of failures, the expected total maintenance cost is determined with Monte Carlo simulation. The proposed approach has been validated with the data provided by a wind farm located in Northern China.

Optimal State Risk Scheduling Based on Selective Maintenance Strategy

Because stochastic fault cases (i.e., opportunistic maintenance strategy) of the equipment are not considered in the condition-based maintenance decision of the system, which will deviate from the actual situation, a system condition-based maintenance scheduling model considering opportunistic maintenance strategy is proposed in this paper. To implement the system maintenance strategies, the correlation set is formulated by considering the relationship among different equipment. According to renewal process theory, the availability of the correlation set considering planned maintenance and opportunity maintenance is deduced and the maintenance strategy of the system is realized. Then, to reflect the relationship between equipment maintenance and system operation, a system state scheduling model aiming at minimizing the sum of maintenance risk and failure risk as well as considering system resource constraints is proposed, thus obtaining the optimal maintenance schedule based on system state. Finally, a simple test system and IEEE-RTS79 node system are employed to demonstrate the feasibility and effectiveness of the proposed maintenance model, and it also verified that the proposed model can be integrated into the power system condition-based maintenance theory.

Opportunistic maintenance strategy of a Heave Compensation System for expected performance degradation

In the marine industry, heave compensation systems are applied to marine equipment to compensate for the adverse effects of waves and the hydraulic system is usually used as the power system of heave compensation systems. This article introduces importance theory to the opportunistic maintenance (OM) strategy to provide guidance for the maintenance of heave compensation systems. The working principle of a semi-active heave compensation system and the specific working states of its hydraulic components are also first explained. Opportunistic maintenance is applied to the semi-active heave compensation system. Moreover, the joint integrated importance measure (JIIM) between different components at different moments is analyzed and used as the basis for the selection of components on which to perform PM, with the ultimate goal of delaying the degradation of the expected performance of the system. Finally, compared with conditional marginal reliability importance (CMRI)-based OM, the effectiveness of JIIM-based OM is verified by the Monte Carlo method.

Opportunistic maintenance for offshore wind farms with multiple-component age-based preventive dispatch

Operation & maintenance (O&M) costs account for a large portion of total life cycle cost for onshore wind energy, and the amount is estimated to be more for offshore wind energy. Developing a sound opportunistic maintenance strategy is a solution to reduce O&M costs and enhance wind energy's competitiveness. When the wind farm is located offshore, turbines are not only subject to degradation but also the impact from the harsh marine environment. However, the degradation is mainly regarded as the only cause of the failure in the existing opportunistic maintenance models for the offshore wind energy sector. At the same time, too frequent preventive dispatch of maintenance teams exists on some occasions. This paper proposes a maintenance strategy for offshore wind farms integrating three types of maintenance opportunities. In addition to the maintenance opportunities created by degradation failures and incidents, an age-based opportunity is introduced to improve the trigger of preventive dispatch. A numerical example is presented to illustrate the effectiveness of the proposed strategy. The comparative analysis shows 2.6% and 1.5% annual cost can be reduced respectively when compared with two traditional opportunistic maintenance strategies in the base scenario.

A Novel Reliability-Centered Opportunistic Maintenance Strategy for Metro Train Complex Systems

A Novel Reliability-Centered Opportunistic Maintenance Strategy for Metro Train Complex Systems

An opportunistic maintenance strategy for offshore wind turbine system considering optimal maintenance intervals of subsystems

Operation and maintenance (O&M) costs account for a large proportion of the total costs for offshore wind energy. Performing a reasonable maintenance strategy is an effective approach to reduce O&M costs and gain more profits. In this paper, an opportunistic maintenance strategy for offshore wind turbine systems considering maintenance intervals of each subsystem is proposed to minimize the total maintenance cost. First, a Non-homogeneous Continuous-Time Markov Process based state transition model is established to study degradation process of subsystems. The influence of maintenance time schedule on the maintenance cost is studied to obtain the optimal maintenance intervals of each subsystems. Then, an opportunistic maintenance model considering economic dependencies between multiple subsystems is proposed to optimize the maintenance strategy by combining maintenance activities of individual subsystems to a grouping maintenance activity. A numerical example is used to indicate the significant effectiveness of the maintenance model. The result shows that the total maintenance cost of an offshore wind turbine system will be reduced by adopting the opportunistic maintenance strategy when compared with conventional preventive maintenance strategy.

A novel opportunistic maintenance strategy for systems with dependent main and auxiliary components

Abstract The complexity of dependence between different types of components results in many challenges to estimate system reliability and to optimize maintenance plans. In this paper, we develop a reliability model to study the failure dependence of a system with a main component and several protective auxiliary components. Damage to the main component caused by random environmental shocks depends on the number of auxiliary components in operation. When the execution of inspection and maintenance actions during system operation is difficult, failures of the main component provide opportunities to inspect and replace the auxiliary components. We derive the system reliability using Laplace transforms and the matrix method. The optimization problem is solved by an enumeration method. A numerical example and sensitivity studies of cost parameters show how the evolution of the parameters influences the optimal maintenance strategy. The results show that a high replacement threshold of the auxiliary components is required when the replacement cost of the main component is high. Conversely, the threshold could be adjusted to a lower level when the replacement cost of the auxiliary components and the downtime cost increase.

An improved opportunistic group replacement maintenance strategy for wind turbines

AbstractIn traditional opportunistic group replacement maintenance (OGRM) strategy for wind turbines, the reliability thresholds of different subassemblies are set the same and its two‐step optimization for maintenance cost is diseconomy. In this paper, an improved OGRM strategy for wind turbines is proposed. A uniform formulation on maintenance cost concerning different maintenance activities is presented. On the basis of this, an integrated objective function minimizing the maintenance cost is established, and the reliability thresholds of each subassembly of wind turbines are individually severed as the optimization variables of the objective function. The coordinate rotation method is utilized for solving this optimization problem. A maintenance case based on the proposed strategy from a wind farm in North China is implemented, which verifies its superior performance than the traditional opportunistic group maintenance strategy.

Joint external and internal opportunistic optimisation for wind turbine considering wind velocity

Abstract Wind farms are subject to unavoidable energy-production stoppage periods because of the stochastic behaviour of wind velocity; however, these periods provide additional opportunities for maintenance activities. In this research, we investigated a novel joint opportunistic maintenance strategy for wind turbines considering the stochastic wind velocity. Our strategy entailed considering two types of opportunities, namely, internal opportunities provided by the general degradation of wind turbines, and external opportunities generated by the weak wind-speed periods. An expected cost-rate model was formulated by utilising the semi-regenerative process theory based on all possible maintenance scenarios. In this model, all the maintenance activities at each decision point were determined in accordance with the maintenance requirement that is dictated by the state of deterioration of the wind turbines. Furthermore, a stationary probability density function was developed by derivation of the stationary law of the wind turbine condition, influenced by opportunistic maintenance. The maintenance activities and their corresponding probabilities were all derived on this basis. Finally, the applicability and correctness of the proposed approach were demonstrated by a case study and sensitivity analysis. The results of our study indicated that the maintenance policy we proposed could be of economic benefit to wind farms.

Maintenance cost evaluation for heterogeneous complex systems under continuous monitoring

The maintenance strategy for a complex system consisting of both non-monitored and monitored components is analyzed in this paper. Non-monitored components can only be maintained correctively upon failure. Monitored components are monitored continuously and are maintained when they become too degraded, i.e., when its degradation level hits a threshold. For this complex system, an opportunistic maintenance strategy is implemented, meaning that a maintenance intervention for a component can be used as an opportunity for preventive maintenance of monitored components: If the degradation level of a monitored component exceeds a preventive threshold at the time of another maintenance intervention, this component is maintained preventively. By performing these maintenance actions, different costs are incurred. The main purpose of this paper is to evaluate the expected cost rate of the system. To that end, two methods are compared: renewal and semi-regenerative techniques. Using renewal techniques, the evaluation of the expected cost rate of this maintenance strategy is time consuming, especially for a large number of monitored components. However, using semi-regenerative techniques the required computation time is drastically shortened: For a system with ten monitored components, the computation time to evaluate the optimal maintenance strategy goes from more than a day to few seconds. Numerical examples are given to illustrate the results. The conclusion is that for a large number of monitored components, semi-regenerative techniques are more appropriate to evaluate the expected cost rate in terms of computation time.

A preventive, opportunistic maintenance strategy for the catenary system of high-speed railways based on reliability

The catenary is a vital component of the electrified railway system. It consists of many parts which are interrelated; the maintenance schedule of the catenary system should consider the influence of the interrelationship. In this study, a preventive, opportunistic maintenance method is proposed to schedule the maintenance process of the catenary system. First, the reliability of the key parts of the catenary is modeled using Weibull distribution. Second, a reliability margin is proposed to expand the maintenance time from point to interval, and the reliability margin is optimized to minimize the maintenance cost. Then, a preventive opportunistic maintenance schedule can be arranged on the basis of the optimal reliability margin. Case study results verify that the proposed preventive opportunistic maintenance method can reduce the number of maintenance schedules and can effectively save the maintenance cost.

Enhanced POM strategy and its application to wind turbines

Deficiencies exist during applying the traditional preventive opportunistic maintenance (POM) strategy to wind turbines. For example, its rough cost analysis only provides a total instead of the staged cost of once maintenance, which cannot give precise cost composition. Setting identical thresholds for all subassemblies reduces the potential of cost saving etc. An enhanced POM strategy for wind turbines is presented in this study with the consideration of maintenance modes including no maintenance, imperfect maintenance and replacement. The reliability variations of subassemblies under different maintenance modes are described by Weibull distribution with an age reduction factor. On the basis of reliability variations, a formula is proposed to uniformly compute maintenance cost under each maintenance mode via subdividing the cost in the light of diverse maintenance stages. An integrated objective function minimising the maintenance cost is established and solved, where reliability thresholds of each subassembly are separately set as optimisation variables. The enhanced POM strategy is applied to a wind farm in North China, verifying its effectiveness.

Reliability and opportunistic maintenance for a series system with multi-stage accelerated damage in shock environments

In this paper, a new shock model is proposed to fit the situation that the damage process of the component is accelerated with the increase of the degree of damage in shock environments. Shocks with the same magnitude may have more serious consequences as the component gets worse and component states are divided into different stages according to the degree of damage. Reliability indexes of a series system which consists of two components with the above characteristics are derived by using a Markov renewal process. Then, an opportunistic maintenance strategy is proposed for the system and an optimization model is constructed to obtain the optimal maintenance solutions. Finally, a numerical example for a two-rolling bearing system of a wind turbine is presented to illustrate the proposed shock model and maintenance strategy. Sensitivity analysis of maintenance costs is also discussed in order to improve the reliability of the proposed model. This study is of reference value and application significance for similar series systems.

Opportunistic Maintenance Optimization for Transmission Bays Based on Multistate Markov Process

Power equipment usually goes through multiple states during the transition from normal state to the failure state. In order to consider equipment multistate property, an opportunistic maintenance optimization model based on multistate Markov process is proposed considering economic dependence within the transmission bay. This model describe equipment state transition process using the multistate Markov process. And then, opportunistic maintenance strategy between dependent power equipment in a transmission bay is studied. The transmission bay availability is derived considering opportunistic maintenance and scheduled maintenance. Finally, system operation cost is quantified. System operation cost is minimized to optimize equipment maintenance strategies. Numerical studies demonstrate validity of the proposed model.

Maintenance Optimization of Offshore Wind Turbines Based on an Opportunistic Maintenance Strategy

Owing to the late development of offshore wind power in China, operational data and maintenance experience are relatively scarce. Due to the harsh environmental conditions, a reliability analysis based on limited sample fault data has been regarded as an effective way to investigate maintenance optimization for offshore wind farms. The chief aim of the present work is to develop an effective strategy to reduce the maintenance costs of offshore wind turbines in consideration of their accessibility. The three-parameter Weibull distribution method was applied to failure rate estimation based on limited data. Moreover, considering the impacts of weather conditions on the marine maintenance activities, the Markov method and dynamic time window were used to depict the weather conditions. The opportunistic maintenance strategy was introduced to cut down on the maintenance costs through optimization of the preventive maintenance age and opportunistic maintenance age. The simulation analysis we have performed showed that the maintenance costs of the opportunistic maintenance strategy were 10% lower than those of the preventive maintenance strategy, verifying the effectiveness of the proposed maintenance strategy.

Maintenance Scheduling of Power Equipment Considering Opportunistic Maintenance Strategy

This paper investigates the impact of opportunistic maintenance strategy on power equipment maintenance scheduling under economic dependence. The model focuses on power equipment with two types of failures: random failure and deterioration failure. Unlike previous research which didn’t consider opportunistic maintenance between different failures modes, opportunistic maintenance strategy is proposed in this paper. In the opportunistic maintenance strategy, whenever random failure occurs, decision has to be made on whether deterioration failure is preventively maintained based on the degree of equipment deterioration and economic dependence. Firstly, Markov process is used to describe equipment state-space diagram. And then, in order to determine the optimal inspection rate, preventive maintenance threshold and opportunistic maintenance threshold of power equipment, the analytical expressions of inspection cost, maintenance cost, repair cost and outage cost are quantified. Finally, the total cost is minimized to get the optimal maintenance strategy. Case studies demonstrate the feasibility of the proposed model.

An opportunistic maintenance strategy for offshore wind turbine based on accessibility evaluation

The maintenance costs of offshore wind turbines operated under the irregular, non-stationary conditions limit the development of offshore wind power industry. Unlike onshore wind farms, the weather conditions (wind and waves) have greater impacts on the operation and maintenance of offshore wind farm. Accessibility is a key factor related to the operation and maintenance of offshore wind turbine. Considering the impact of weather conditions on the maintenance activities, the Markov method and dynamic time window are applied to represent the weather conditions, and an index used to evaluate the maintenance accessibility is then proposed. As the wind turbine is a multi-component complex system, this article uses the opportunistic maintenance strategy to optimize the preventive maintenance age and opportunistic maintenance age for the main components of the wind turbine. Taking the minimum expectation cost as objective function, this strategy integrates the maintenance work of the key components. Finally, an offshore wind farm is taken for simulation case study of this strategy; the results showed that the maintenance cost of opportunistic maintenance strategy is 10% lower than that of the preventive maintenance strategy, verifying the effectiveness of the opportunistic maintenance.

An opportunistic condition-based maintenance strategy for offshore wind farm based on predictive analytics

The reduction of operation and maintenance cost plays a significant role in decreasing the cost of energy generated by offshore wind farm, which can be realized through better design of maintenance strategy. This paper proposes a dynamic opportunistic condition-based maintenance strategy for offshore wind farm by using predictive analytics. In the strategy, a new maintenance basis is developed by considering the varying maintenance lead time to make maintenance decisions for different wind turbine components. Meanwhile, the strategy also considers the economic dependence between the wind turbines and the components. We then present a maintenance model to derive the optimal maintenance plans for various turbine components under different weather and operation load conditions. A numerical example is used to illustrate the effectiveness of the maintenance model. It is found that the varying maintenance lead time has a significant effect on the annual maintenance cost which demonstrates the reasonableness of our proposed maintenance basis. Compared to the widely employed and the simple maintenance strategies, the proposed strategy can help reduce the annual maintenance cost by 39.24% and 32.46% respectively.

Detection and identification of windmill bearing faults using a one-class support vector machine (SVM)

The maintenance cost of wind turbines needs to be minimized in order to keep their competitiveness and, therefore, effective maintenance strategies are important. The remote location of wind farms has led to an opportunistic maintenance strategy where maintenance actions are postponed until they can be handled simultaneously, once the optimal opportunity has arrived. For this reason, early fault detection and identification are important, but should not lead to a situation where false alarms occur on a regular basis. The goal of the study presented in this paper was to detect and identify wind turbine bearing faults by using fault-specific features extracted from vibration signals. Automatic identification was achieved by training models by using these features as an input for a one-class support vector machine. Detection models with different sensitivity were trained in parallel by changing the model tuning parameters. Efforts were also made to find a procedure for selecting the model tuning parameters by first defining the criticality of the system and using it when estimating how accurate the detection model should be. Method was able to detect the fault earlier than using traditional methods without any false alarms. Optimal combination of features and model tuning parameters was not achieved, which could identify the fault location without using any additional techniques.