LAUSR

This paper explores a Bayesian update strategy applying the experts’ subjective viewpoints concerning the wind turbines’ preventive maintenance. The approach is general enough to be employed in other than wind turbine maintenance applications. Although extant literature has examined the implementation of optimal adaptive Bayesian update strategies relating to the preventive maintenance time, they have not expanded the findings to the subjective views of wind farm managers or technicians. Against this backdrop, subjective opinions have been successfully deployed herein for Bayesian updates in the meantime the experts explore key distribution parameters without any prior expertise of statistics by merely putting forth opinions as disbelief, belief, or ambiguity. The choices, along with their concomitant effect on statistical parameters, including the minimized time of maintenance and new approach of costs, are presented before the wind farm manager and technician directly in the form of quantitative data, whereas their inputs are considered as opinion. Notably, such an approach adjuncts the quantitative data from turbine supervisory control and data acquisition (SCADA). As an integral component of turbine failures, the preventive maintenance pitch control device has been explored in this paper, which demonstrates the viability of this approach. A reliability networks Bayesian update strategy that develops the subjective opinions of variable and random costs are formulated and systematically investigated for preventive maintenance while several expert opinion has been implemented by the author in an earlier preliminary study. The reliability network is implemented for components where the SCADA does not provide clear data where the technician (expert) has opinion.