Fisher vector encoding for improving the performance of fault diagnosis in a synchronous generator

Abstract

In this work, we experiment with Fisher vector encoding to map the feature vectors into a higher dimensional space and use linear support vector machine (SVM) for improving the performance of inter turn fault diagnosis in a 3kVA synchronous generator. Fisher vector encoding computes the first and second order differences between the feature vectors and Gaussians. We compare the performance of Fisher vector encoding with sparse coding and locality constrained linear coding (LLC). From the experiments and results, we observed that Fisher vector encoding is the most computationally efficient algorithm when compared to feature mapping using sparse coding and locality constrained linear coding (LLC).