Feature Mapping Techniques for Improving the Performance of Fault Diagnosis of Synchronous Generator

R. Gopinath, C. Santhosh Kumar, K. Vishnuprasad, and K. I. Ramachandran
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Full Paper
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Aeronautical Development Agency (ADA) Bangalore, India
ijphm_15_025.pdf2.92 MBOctober 5, 2015 - 6:08am

Support vector machine (SVM) is a popular machine learning algorithm used extensively in machine fault diagnosis. In this paper, linear, radial basis function (RBF), polynomial, and sigmoid kernels are experimented to diagnose inter-turn faults in a 3kVA synchronous generator. From the preliminary results, it is observed that the performance of the baseline system is not satisfactory since the statistical features are non-linear and does not match to the kernels used. In this work, the features are linearized to a higher dimensional space to improve the performance of fault diagnosis system for a synchronous
generator using feature mapping techniques, sparse coding and locality constrained linear coding (LLC). Experiments and results show that LLC is superior to sparse coding for improving the performance of fault diagnosis of a synchronous generator. For the balanced data set, LLC improves the overall fault identification accuracy of the baseline RBF system by 22.56%, 18.43% and 17.05% for the R, Y and B-phase faults respectively.

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Submission Keywords: 
Machine fault diagnosis
Synchronous generator
Support Vector Machine
Sparse coding
Locality Constrained Linear Coding
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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