Fault detection and isolation in electrical machines using deep Neural networks

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dc.contributor.author Sai, M.
dc.contributor.author Upadhyay, Parth Tarun
dc.contributor.author Srinivasan, Babji
dc.contributor.other Future Technology on Combat Vehicle Electronics (FTC 2018), National Technical Seminar on Combat Vehicles Research and Development Establishment (CVRDE)
dc.coverage.spatial Chennai, IN
dc.date.accessioned 2018-03-15T06:51:50Z
dc.date.available 2018-03-15T06:51:50Z
dc.date.issued 2018-02-23
dc.identifier.citation Sai, M.; Upadhyay, Parth Tarun and Srinivasan, Babji, "Fault detection and isolation in electrical machines using deep Neural networks", in the Future Technology on Combat Vehicle Electronics (FTC 2018),National Technical Seminar on Combat Vehicles Research and Development Establishment (CVRDE), Chennai, IN, Feb. 23, 2018. en_US
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/3511
dc.description.abstract With the single-tube and double-tube fault of seven-level converter, this paper presents a new way to learn the faults feature based on the deep neural network of sparse autoencoder. Sparse autoencoder is an unsupervised learning method, it can learn the feature information of the fault data according to training. The feature information is used to train the softmax classifier by softmax regression to realize the aim of classification. Comparing with the traditional neural network of BP neural network, the experimental results show that the method to classify the fault of seven level converter based on deep neural network of sparse autoencoder can achieve higher accuracy.
dc.description.statementofresponsibility by M.Sai, Parth Tarun Upadhyay and Babji Srinivasan
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.title Fault detection and isolation in electrical machines using deep Neural networks en_US
dc.type Article en_US


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