A Hybrid Time Frequency Response and Fuzzy Decision Tree for Non-stationary Signal Analysis and Pattern Recognition
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.
KeywordsNon-stationary signals sparse S-transform (SST) scaling method fuzzy decision tree pattern classification
Unable to display preview. Download preview PDF.
- A. R. Abdullah, A. Z. Sha'ameri, M. S. Norhashimah. Power quality analysis using spectrogram and gabor transformation. In Proceedings of Asia-Pacific Conference on Applied Electromagnetics, IEEE, Melaka, Malaysia, pp. 1–5, 2007. DOI: 10.1109/APACE.2007.4603964.Google Scholar
- A. R. B. Abdullah, A. Z. B. Sha'ameri, B. J. Auzani. Classification of power quality signals using smooth-windowed Wigner-Ville distribution. In Proceedings of International Conference on Electrical Machines and Systems, IEEE, Incheon, South Korea, pp. 1981–1985, 2010.Google Scholar
- P. K. Dash, B. K. Panigrahi, D. K. Sahoo, G. Panda. Power quality disturbance data compression, detection, and classification using integrated spline wavelet and Stransform. IEEE Transactions on Power Delivery, vol. 18, no. 2, pp. 595–600, 2003. DOI: 10.1109/TPWRD.2002. 803824.CrossRefGoogle Scholar
- R. Kumar, B. Singh, D. T. Shahani, A. Chandra, K. Al-Haddad. Recognition of power-quality disturbances using S-transform-based ANN classifier and rule-based decision tree. IEEE Transactions on Industry Applications, vol. 51, no. 2, pp. 1249–1258, 2015. DOI: 10.1109/TIA.2014. 2356639.CrossRefGoogle Scholar
- M. Analysis of nonstationary power-quality waveforms using iterative Hilbert Huang transform and SAX algorithm. IEEE Transactions on Power Delivery, vol. 28, no. 4, pp. 2134–2144, 2013. DOI: 10.1109/TPWRD.2013.2264948.Google Scholar
- Y. Huang, Y. Q. Liu, Z. P. Hong. Detection and location of power quality disturbances based on mathematical morphology and Hilbert-Huang transform. In Proceedings of the 9th International Conference on Electronic Measurement & Instruments, IEEE, Beijing, China, 2000. DOI: 10.1109/ICEMI.2009.5274596.Google Scholar
- R. A. Brown, R. Frayne. A fast discrete S-transform for biomedical signal processing. In Proceedings of the 30th Annual International Conference of IEEE Engineering in Medicine and Biology Society, IEEE, Vancouver, Canada, pp. 2586–2589, 2008. DOI: 10.1109/IEMBS.2008.4649729.Google Scholar
- R. A. Brown, M. L. Lauzon, R. Frayne. A general description of linear time-frequency transforms and formulation of a fast, invertible transform that samples the continuous S-transform spectrum nonredundantly. IEEE Transactions on Signal Processing, vol. 58, no. 1, pp. 281–290, 2010. DOI: 10.1109/TSP.2009.2028972.MathSciNetCrossRefGoogle Scholar
- S. Khokhar, A. Asuhaimi B. Mohd Zin, A. S. B. Mokhtar, M. Pesaran. A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renewable and Sustainable Energy Reviews, vol. 51, pp. 1650–1663, 2015. DOI: 10.1016/j.rser.2015.07.068.CrossRefGoogle Scholar
- Y. Wang, X. X. Zhu. A robust design of hybrid fuzzy controller with fuzzy decision tree for autonomous intelligent parking system. In Proceedings of American Control Conference, IEEE, Portland, USA, 2014. DOI: 10.1109/ACC. 2014.6859439.Google Scholar
- Y. Wang, X. X. Zhu. Hybrid fuzzy logic controller for optimized autonomous parking. In Proceedings of American Control Conference, IEEE, Washington DC, USA, 2013. DOI: 10.1109/ACC.2013.6579834.Google Scholar
- Y. Wang, X. X. Zhu. Design and implementation of an integrated multi-functional autonomous parking system with fuzzy logic controller. In Proceedings of American Control Conference, IEEE, Montreal, Canada, 2012. DOI: 10.1109/ACC.2012.6315356.Google Scholar