Predictive maintenance for wind turbine diagnostics using vibration signal analysis based on collaborative recommendation approach

The application of decision making knowledge based methods is to analyze the system and identify its in-depth diagnosis and fault behavior by simulating the expert knowledge from a similar domain. Rule based systems with predefined conditions have been replaced and or upgraded to expert knowledge based systems and further replaced / upgraded by applying machine learning techniques wherein, association rules, reasoning and decision making processes have been considered similar to expert knowledge in resolution of diagnostics during critical scenarios. The application of condition monitoring technique has been widely applied to analyze the behavior pattern of the system. In this paper, vibration signal analysis is performed to study and extract the behavioral pattern of the bearings. Further, machine learning models such as k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), k-Means have been applied to classify the type of fault. Further, Collaborative Recommendation Approach (CRA) has been applied here to analyze the similarity of all the model results to suggest in advance, the replacement and correction of the deteriorating units and prevent severe system break downs and disruptions.