An embedded application to identify degradation in energized polymeric insulators using machine learning and wavelet transform

  • Rebeca G. C. Cunha IFCE
  • Elias T. da Silva Junior IFCE
  • Claudio M. S. Medeiros IFCE


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.

Palavras-chave: Embedded Systems, Machine Learning, Bayes, Knn, DWT, Pattern Classifier, Polymeric Insulator


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CUNHA, Rebeca G. C. ; DA SILVA JUNIOR, Elias T. ; MEDEIROS, Claudio M. S. . An embedded application to identify degradation in energized polymeric insulators using machine learning and wavelet transform. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 8. , 2018, Salvador. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 58-65. ISSN 2237-5430.