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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...
High accuracy fault diagnosis systems are extremely important for effective condition based maintenance (CBM) of rotating machines. In this work, we develop a fault diagnosis system using time and frequency domain statistical features as input to a backend support vector machine (SVM) classifier. We evaluate the performance of the baseline system for speed dependent and speed independent performance...
Support Vector Machine (SVM) is an important machine learning algorithm widely used for the development of machine fault diagnosis systems. In this work, we use an SVM back-end classifier, with statistical features in time and frequency domain as its input, for the development of a fault diagnosis system for a rotating machine. Our baseline system is evaluated for its speed dependent and speed independent...
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