Diagnosis and prognosis of real world wind turbine gears

Abstract

Today, condition monitoring (CM) is unarguably the most important field in any industrial applications. CM of wind turbines (WT’s) has in the past few years grown substantially. Although numerous initiatives to develop CM techniques and make operations more efficient were launched, most developed tools failed to respond on time to unpredictable events. One area that shows great potential in the battle against machine damages and their exploits is the diagnosis and prognosis of WT gears. In the world of big varying modulated data, analysis of health conditions of WT gears by traditional CM methods is no longer sufficient. Example for this is the high dimensionality and very extremely modulated vibration dataset, provided by Suzlon company. Suzlon unworkably attempted to online discriminate its machines using a set of well-known CM analysis methods. However, only visual inspection could identify the faulty WT gear. Hence, Suzlon flagged up a top priority need to identify more efficient online tools for improving CM processes. In the response to this essential need, the author employs Signal Intensity Estimator (SIE) method and some machine learning (ML) algorithms to analyse Suzlon dataset. A conclusion was reached that these techniques could successfully provide a reliable estimate of WT’s conditions.