An embedded application to identify degradation in energized polymeric insulators using machine learning and wavelet transform
Insulators are among the main causes of failure in the electric power lines. In this paper is described the process to develop and embed an application to identify degradation in high voltage polymeric insulators using ultrasonic emissions. The proposed approach is a combination of the wavelet transform and two different classifiers, Naive Bayes or Knn. Both strategies are evaluated in a workstation and in an embedded platform, an ARM Cortex M4. Their accuracy, execution time, and memory footprint are compared for the embedded implementation. The results indicate that the selected techniques offer good prediction rate and can be embedded in low-cost microcontrollers.
S. F. Stefenon, L. H. Meyer, F. H. Molina, ”Real Time Automates Diagnosis of Insulating System Employing Ultrasound Inspection”. 23rd International Conference on Electricity Distribution, Lyon, 2015.
G. Madruga, F. Scarpini, L. H. Meyer and F. H. Molina, ”A Practical Approach for Detection of Incipient Failure of Ceramic Insulators”, in IEEE International Conference on Solid Dielectrics. Bologna, Italy: 2013.
F. B. Arfia; M. B. Messaoud and M. Abid, ”A New Image denoising Technique Combining the Empirical Mode Decomposition with a Wavelet Transform Technique”. Proceedings of 17th International Conference on Systems, Signals and Image Processing. Rio de Janeiro. Brazil: 2010, pp. 514-517.
R. Ramos, B. V. Salas, R. Zlatev, M. S. Wiener and J. M. B. Rull, ”The discrete wavelet transform and its application for noise removal in localized corrosion measurements”, International Journal of Corrosion, 2017, pp. 1-7.
S. Mallat,”A Wavelet tour of signal processing - The Sparse Way”. Burlington: Elsevier, 2009, pp. 102-112.
A. Abbate, J. Frankel , P. Das, ” Wavelet Transform Signal Processing Applied to Ultrasonics”. Review of Progress in Quantitative Nondestruc- tive Evaluation. Springer, Boston, MA: 1996, pp. 741-748.
M. Aboofazeli, P. Abolmaesumi, M. Moradi, E. Sauerbrei , R. Siemens , et al. ”Automated detection of prostate cancer using wavelet transform features of ultrasound RF time series”. Proceedings of SPIE 7260 Medical Imaging, 2009.
A. Yahiaoui , M. S. C. Med and S. Laddada, ”Analysis of the Composite Materials using the Wavelet Transform”. Journal of Scientific and Industrial Research, vol. 75. pp.344-348, 2016.
R. Dogaru. A modified Nave Bayes classifier for efficient implementations in embedded systems. ISSCS 2011 - International Symposium on Signals, Circuits and Systems 2011: 1-4.
P. Ziaie, T. Muller, M. Foster and A. Knoll. ”A Naive Bayes Classifier with Distance Weighting for Hand-Gesture Recognition”. 13th international CSI computer conference 2009.
G. Zwartjes. ”Adaptive Naive Bayes Classification for Wireless Sensor Networks”. PhD Thesis, University of Twente, The Netherlands: 2017. ISBN 978-90-365-4263-0.
T. V. Ferreira, A. D. Germano and E. G. da Costa, ”Ultrasound and Artificial Intelligence Applied to the Pollution Estimation in Insulations”, In IEEE Transactions on Power Delivery, vol. 27, no. 2, pp.583-589, 2002
M. A. Amini and A. R. Sedighi, ”A new procedure for determination of insulators contamination in electrical distribution networks”, In International Journal of Electrical Power & Energy Systems, vol. 61, 2014, pp. 380-385
A. Ukil and A. Brlocher, ”Implementation of discrete wavelet transform for embedded applications using tms320vc5510”. International Symposium on Industrial Embedded Systems. Lisbon. Portugal: 2007.
K. Andra, , C. Chakrabarti and T. Acharya, ”A VLSI architecture for lifting-based forward and inverse wavelet transform”. IEEE Transactions on Signal Processing, v. 50, n. 4, p. 966977, 2002. ISSN 1053-587X.
J. Rennie, L. Shih; J. Teevan and D. Karger. ”Tacking the poor assumption of Naive Bayes classifiers”. In Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washing- ton DC, 2003.
C. Bishop. ”Pattern Recognition and Machine Learning”. Springer- Verlag, Berlin, Heidelberg, 2006.
H. Zhang. ”The Optimality of Naive Bayes”. In Proceedings of the 17th international Florida artificial intelligence research society conference, Miami Beach, FL, USA, 2004.
O. Sutton. ”Introduction to k Nearest Neighbour Classification and Condensed Nearest Neighbour Data Reduction”. 2012.
L. Hu, M. Huang, S. Ke and C. Tsai. ”The distance function effect on k-nearest neighbor classification for medical datasets. Springer Plus, 2016.
E. Silva Jr, F. Aquino, A. Rocha Neto, K. Gurgel, et al. ”Corona Effect Detection in Energized Polymeric Insulators Using Machine Learning and Ultrasonic Emissions”[in Portuguese], Detecção de efeito corona em isoladores poliméricos energizados usando aprendizagem de máquina e emissões de ultrassom, In IEEE Latin America Transactions vol. 16, Issue 6, pp 1587-1594, 2018.
R. Cunha, E. Silva Jr, J. Souza and P. Rebouças Filho. ”Toward an embedded application to identify degradation in energized polymeric insulators using discrete wavelet transform”. In 25th International Conference on Systems, Signals and Image Processing, 2018.
STMICROELECTRONICS. AN4841 Application Note Digital Signal processing for STM32 microcontrollers using CMSIS. 1st. ed. [S.l.: s.n.], 2016.
A. Starzacher and B. Rinner. ”Evaluating Knn, LDA and QDA classification for embedded online feature fusion”. International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2008. ISSNI 2008.4761967.