dc.description.abstract | The blades of offshore wind farms (OWTs) are susceptible to a wide variety of diverse sources of
damage. Internal impacts are caused primarily by structure deterioration, so even though outer
consequences are the consequence of harsh marine ecosystems. We examine condition-based
maintenance (CBM) for a multiblade OWT system that is exposed to environmental shocks in this
work. In recent years, there has been a significant rise in the number of wind turbines operating
offshore that make use of CBMs. The gearbox, generator, and drive train all have their own
vibration-based monitoring systems, which form most of their foundation. For the blades, drive
train, tower, and foundation, a cost analysis of the various widely viable CBM systems as well as
their individual prices has been done. The purpose of this article is to investigate the potential
benefits that may result from using these supplementary systems in the maintenance strategy.
Along with providing a theoretical foundation, this article reviews the previous research that has
been conducted on CBM of OWT blades. Utilizing the data collected from condition monitoring,
an artificial neural network is employed to provide predictions on the remaining life. For the
purpose of assessing and forecasting the cost and efficacy of CBM, a simple tool that is based on
artificial neural networks (ANN) has been developed. A CBM technique that is well-established
and is based on data from condition monitoring is used to reduce cost of maintenance. This can be
accomplished by reducing malfunctions, cutting down on service interruption, and reducing the
number of unnecessary maintenance works. In MATLAB, an ANN is used to research both the
failure replacement cost and the preventative maintenance cost. In addition to this, a technique for
optimization is carried out to gain the optimal threshold values. There is a significant opportunity
to save costs by improving how choices are made on maintenance to make the operations more
cost-effective. In this research, a technique to optimizing CBM program for elements whose
deterioration may be characterized according to the level of damage that it has sustained is
presented. The strategy may be used for maintenance that is based on inspections as well as
maintenance that is based on online condition monitoring systems. | |