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

R Gopinath,K I Ramachandran,K Vishnuprasad,C Santhosh Kumar

doi:10.36001/ijphm.2015.v6i2.2256

Abstract

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 systemis not satisfactory since the statistical features are nonlinear 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 Bphase faults respectively.

Highlights

Condition based maintenance (CBM) is the most preferred technique in many industrial applications for its reduced maintenance costs and improved safety operations
The third approach is experimented to improve the performance of fault diagnosis of a synchronous generator using feature mapping techniques
Feature mapping algorithms, sparse coding and locality constrained linear coding (LLC) are used to improve the performance of Support vector machine (SVM) for the inter-turn fault identification of 3kVA synchronous generator

Summary

Introduction

Condition based maintenance (CBM) is the most preferred technique in many industrial applications for its reduced maintenance costs and improved safety operations. Feature extraction is an important process in CBM which maps the measured signal into the feature space. The performance of the fault diagnosis algorithm is dependent on the features Signal processing based feature extraction methods such as time-domain (Samanta & Al-Balushi, 2003), frequency-domain (Chen, Du, & Qu, 1995), wavelet (Peter, Peng, & Yam, 2001; Lin & Zuo, 2004; Yan, Gao, & Wang, 2009), and empirical mode decomposition (Yan & Gao, 2008; He, Liu, & Kong, 2011) have been widely used in machine condition monitoring applications. Zhang, Li, Scarf, & Ball, 2011) which are used to select the fault discriminative features from the feature space for better classification. Feature transformation approaches are used to improve the fault identification performance (Widodo, Yang, & Han, 2007; Widodo & Yang, 2007; Y. Zhang, 2009)

Methods

Results

Conclusion