A Hybrid Time Frequency Response and Fuzzy Decision Tree for Non-stationary Signal Analysis and Pattern Recognition

Article

Abstract

A Fourier kernel based time-frequency transform is a proven candidate for non-stationary signal analysis and pattern recognition because of its ability to predict time localized spectrum and global phase reference characteristics. However, it suffers from heavy computational overhead and large execution time. The paper, therefore, uses a novel fast discrete sparse S-transform (SST) suitable for extracting time frequency response to monitor non-stationary signal parameters, which can be ultimately used for disturbance detection, and their pattern classification. From the sparse S-transform matrix, some relevant features have been extracted which are used to distinguish among different non-stationary signals by a fuzzy decision tree based classifier. This algorithm is robust under noisy conditions. Various power quality as well as chirp signals have been simulated and tested with the proposed technique in noisy conditions as well. Some real time mechanical faulty signals have been collected to demonstrate the efficiency of the proposed algorithm. All the simulation results imply that the proposed technique is very much efficient.

Keywords

Non-stationary signals sparse S-transform (SST) scaling method fuzzy decision tree pattern classification 

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Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Multidisciplinary Research CellSiksha O Anusandhan University, Khandagiri SquareBhubaneswarIndia

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