An insight on VMD for diagnosing wind turbine blade faults using C4.5 as feature selection and discriminating through multilayer perceptron

Abstract

This paper presents a pioneer study of identifying the wind turbine blade condition based on its vibration pattern. The study used variational mode decomposition (VMD) for signal pre-processing. VMD is an adaptive signal decomposition technique, which can non-recursively decompose a multi-component signal into a number of quasi-orthogonal intrinsic mode functions. This new tool is based on a solid mathematical foundation and can obtain a well-defined time–frequency representation. The main advantage of VMD is, there is no residual noise in the modes and it can avoid redundant modes. Thus, VMD provides a noise-free fault signal for classification. Initially, the obtained vibration data are pre-processed using VMD and then the descriptive statistical features like mean, standard deviation, sample variance, and kurtosis were extracted. After the feature extraction, the dominant features were selected using the C4.5 decision tree algorithm. In this study, multilayer perceptron (MLP) was used for fault classification (blade bend, blade crack, erosion, hub-blade loose connection, and pitch angle twist). Then a comparative study was made with MLP without VMD and MLP with VMD to suggest a better model. From the results, the maximum classification accuracy of 87% was obtained for MLP with VMD when compared to MLP without VMD (76.83%).