A Hybrid Empirical Mode Decomposition (EMD)-Support Vector Machine (SVM) for Multi-Fault Recognition in a Wind Turbine Gearbox

The increasing complexity of the wind turbine planetary gearbox and the ever-worsening operating conditions of off-shore wind turbines has made gearbox vibration-based fault diagnosis increasingly challenging. This quagmire has shifted research emphasis in recent times from traditional time-domain feature extraction to the use of more advanced signal processing tools. In this study, a hybrid empirical mode decomposition (EMD)-support vector machine (SVM) is proposed for multi-fault recognition in a wind turbine gearbox. The study investigates the impact of the SVM kernel and the EMD window size on the performance of the SVM model. The study results show that the size of the EMD window and the SVM kernel all impact the performance of the SVM model. The optimal EMD window was 500,000 samples. The radial basis function (RBF) and sigmoid kernel-based SVMs all scored 100% for accuracy, specificity, and sensitivity; however, the RBF kernel-based SVM resulted in the least training time, making it the best for the study. The high performance of the models makes them suitable for use as diagnostic tools for wind turbine gearboxes, allowing for precise fault diagnosis with minimum classification errors.