Component-level Maintenance Research Articles

A State-Age-Dependent Opportunistic Intelligent Maintenance Framework for Wind Turbines Under Dynamic Wind Conditions

Intelligent maintenance powered by advanced sensor technology is crucial to ensure safe and reliable operation of wind turbines. Most maintenance models are scheduled solely based on age/degradation conditions, while ignoring the dynamics of wind conditions and residual lifetime that significantly affect maintenance executions. This paper addresses such challenges by constructing a dynamic age-state-dependent intelligent opportunistic maintenance framework, which is capable of integrating (a) degradation and age state, (b) estimation of remaining lifetime, (c) both the positive (extra maintenance opportunities) and negative impacts (maintenance delays) of wind conditions. Specially, component-level maintenance is allowed postponed to balance lifetime extension and resource allocation, whose implementation interval is controlled by real-time estimations of lifetime and dynamic wind velocities. Moreover, both wind and health centered opportunistic maintenance are incorporated to mitigate power generation losses. The applicability and superiority of the proposed framework are validated by a case study on an Ontario wind farm.

Optimal maintenance planning for repairable multi-component systems subject to dependent competing risks

Many complex multi-component systems suffer from dependent competing risks. The reliability modeling and maintenance planning of repairable dependent competing risks systems are challenging tasks because the repair of the failed component can change the lifetime of the other components when multiple components fail dependently. This article first proposes a generally dependent latent age model to capture the dependence of competing risks under general component repairs. Based on the proposed reliability model, both system- and component-level periodic inspection-based maintenance polices are considered for repairable multi-component systems that are subject to dependent competing risks. Under the system-level maintenance policy, the entire system is restored to the as-good-as-new state once a failure is detected. While under the component-level maintenance policy, only the failed component is repaired imperfectly. The optimal solution of the system-level policy is obtained by using renewal theory. The optimal solution of the component-level policy, however, cannot be obtained analytically, due to its complex failure and repair characteristics. A simulation-based optimization approach with stochastic approximation is developed to solve the optimization problem for the component-level policy. The developed methods are illustrated by using a cylinder head assembly cell that consists of multiple stations.

A particle swarm model for estimating reliability and scheduling system maintenance

Modifying data and information system components may introduce new errors and deteriorate the reliability of the system. Reliability can be efficiently regained with reliability centred maintenance, which requires reliability estimation for maintenance scheduling. A variant of the particle swarm model is used to estimate reliability of systems implemented according to the model view controller paradigm. Simulations based on data collected from an online system of a large financial institute are used to compare three component-level maintenance policies. Results show that appropriately scheduled component-level maintenance greatly reduces the cost of upholding an acceptable level of reliability by reducing the need in system-wide maintenance.

Simulation-Based Network Maintenance Planning and Scheduling

Lock deterioration in a waterway network requires timely maintenance to maintain navigability and regular lockage service. Locks degrading over time have reduced capacities and increased service times. Because demand responds to service changes, the objective function maximizes the overall net benefits rather than the minimization of costs. To maximize the net benefits, it is important to schedule maintenance that preserves lock conditions above threshold values, provides minimum acceptable service, and reduces the risks of serious failures. With constrained budgets, network-level maintenance can be scheduled over a planning horizon. A waterway model that combines simulation and optimization was developed to allocate maintenance funds and to schedule maintenance tasks optimally. Numerical cases are evaluated by parallel processing. The promising demonstration of simulation-based optimization shows the applicability of the proposed methodology to network-level and component-level maintenance planning and scheduling.

Maintenance-policy cost-analysis for a series system with highly-censored data

This paper considers the problem of estimating the optimal age-replacement time for a series arrangement of functional subsystems when data are subject to high levels of random censoring on the right. The system does not have redundant components. Simulation is used to compare the performance of the Kaplan-Meier Estimator (KME), the Piecewise Exponential Estimator (PEXE) and the Maximum Likelihood Estimator (MLE) in estimating the optimal replacement time for the system, as well as for each component, under high levels of random censorship. Monte Carlo analysis is used to estimate 'average optimal age replacement times' determined using total time on test (TTT) transforms based on the KME, PEXE, and MLE methods. The optimal replacement time is used to calculate a value which is used to compare the relative long-run cost per unit-time for each method. The differences between using system-level data vs. component-level data to construct a maintenance policy are examined. With respect to cost effectiveness, the results identify the crucial factor in determining whether to perform system-level maintenance or component-level maintenance; that factor is the ratio of the 'cost of performing preventive maintenance' and the 'penalty cost of experiencing a system failure'. For the ratios used in this study (0.1 to 0.5) a 'component-level maintenance policy' is more cost effective than a 'system-level maintenance policy'. The results also show that for a correctly specified model and for large sample sizes, the age replacement times provided by the MLE are more accurate than those provided by the KME and PEXE, especially under high levels of censoring.