Programs
- Ph. D. in Mechanical Engineering -
- B.Tech. in Artificial Intelligence (AI) and Data Science (Medical Engineering) - Undergraduate
Publication Type : Journal Article
Publisher : Measurement: Journal of the International Measurement Confederation
Source : Measurement: Journal of the International Measurement Confederation, Elsevier B.V., Volume 111, p.264-270 (2017)
Keywords : Codes (symbols), Computationally efficient, Electric fault currents, Encoding (symbols), Failure analysis, Fault detection, Feature vectors, Fisher vectors, Higher-dimensional, Inter-turn fault, Linear coding, Linear Support Vector Machines, Sparse coding, Support vector machines, Synchronous generators, Vector spaces, Vectors
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Mechanical Engineering
Year : 2017
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 3 kVA 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). © 2017 Elsevier Ltd
Cite this Research Publication : R. Gopinath, Kumar, C. S., and Dr. K. I. Ramachandran, “Fisher vector encoding for improving the performance of fault diagnosis in a synchronous generator”, Measurement: Journal of the International Measurement Confederation, vol. 111, pp. 264-270, 2017.