This paper proposes an approach for stress condition monitoring and maintenance assessment in wind turbines (WTs) through large amounts of collected data from the supervisory control and data acquisition (SCADA) system. The objectives of the proposed approach are to provide a stress condition model for health monitoring, to assess the WT’s maintenance strategies, and to provide recommendations on current maintenance schemes for future operations of the wind farm. At first, several statistical techniques, namely principal component analysis, Pearson, Spearman and Kendall correlations, mutual information, regressional ReliefF and decision trees are used and compared to assess the data for dimensionality reduction and parameter selection. Next, a normal behavior model is constructed by an artificial neural network which performs condition monitoring analysis. Then, a model based on the mathematical form of a proportional hazards model is developed where it represents the stress condition of the WT. Finally, those two models are jointly employed in order to analyze the overall performance of the WT over the study period. Several cases are analyzed with five-year SCADA data and maintenance information is utilized to develop and validate the proposed approach.
A hybrid approach for the stratified mark‐specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials
