Abstract
Due to the considerable increase in clean energy demand, there is a significant trend of increased wind turbine sizes, resulting in much higher loads on the blades. The high loads can cause significant out-of-plane deformations of the blades, especially in the area nearby the maximum chord. This paper briefly presents a discrete Markov chain model as a simplified probabilistic model for damages in wind turbine blades, based on a six-level damage categorization scheme applied by the wind industry, with the aim of providing decision makers with cost-optimal inspection intervals and maintenance strategies for the aforementioned challenges facing wind turbine blades. The in-history inspection information extracted from a database with inspection information was used to calibrate transition probabilities in the discrete Markov chain model. With the calibrated transition probabilities, the damage evolution can be statistically simulated. The classical Bayesian pre-posterior decision theory, as well as condition-based maintenance strategy, was used as a basis for the decision-making. An illustrative example with transverse cracks is presented using a reference wind turbine.
Highlights
Over the last decade, renewable and clean energy has accounted for an ever-increasing amount of total energy consumption around the world
Chan and Mo developed a Maintenance Aware Design Environment (MADe) model, which is based upon failure mode and effect analysis (FMEA) and bond graph modelling, to simulate the effects of maintenance strategies on the life-cycle costs of mechanical components of wind turbines (WTs) [11]
Where i denotes the different damage states, Nobs,i is the number of observations for damage category i and Nest,i is the estimated number of damages for damage category i by using the discrete Markov chain model with calibrated transition probabilities, NT denotes the total number of observations extracted from an inspection database for the failure of ‘Transverse cracks’, and b denotes the last damage state, namely the absorb state in the discrete Markov chain model
Summary
Renewable and clean energy has accounted for an ever-increasing amount of total energy consumption around the world. Chan and Mo developed a Maintenance Aware Design Environment (MADe) model, which is based upon failure mode and effect analysis (FMEA) and bond graph modelling, to simulate the effects of maintenance strategies on the life-cycle costs of mechanical components of WTs [11]. The Gamma process model parameters were extracted from a failure database In other industries such as oil & gas, risk-based planning of inspections and repairs is performed on the basis of use of probabilistic fracture mechanics models in combination with Bayesian decision theory; for example, see [17,18].
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