## 4 Conclusions  
This paper employed a neural network to classify the conditions of ceramic insulators ultrasonic signals. In this sense, two different classifications were performed, the first regarding perforated and non-perforated insulators, and the second regarding contaminated and non-contaminated insulators. The results obtained show that it is possible to detect the failures in the insulators in a non-invasive way, using mechanical waves. The presented results were better at detecting perforated insulators, with less success in detecting contamination. When considering the whole dataset, an accuracy of up to 82% was obtained by classifying insulators with respect to perforation, while for contamination a result of up to 68.25% was reached. During the tests, it was noticed that the case study #4 was the most difficult to classify. When this specific insulator is disregarded, the neural network achieves an accuracy of 98.9% in detecting physical defects, just as it would achieve an accuracy of 88.1% in detecting contaminants. By individually analyzing the insulators, the results can reach even higher accuracy, both for perforated and contaminated detection. Furthermore, the double check technique can improve the results, reaching individual accuracy up to 95.2%. Overall, the applied technique can provide useful insights for the analysis of physical equipment, enabling improvement in the electrical system's evaluation and maintenance processes. The analysis presented in this paper can be applied to different insulator profiles, under different conditions of use. Based on a signal from an insulator in good condition, it is possible to apply the model to classify adverse conditions and thus improve the identification of faults in the electrical grid. As the ultrasound equipment can capture noise from partial discharges, it can even be used to evaluate other equipment in the electrical power system. The algorithm used in this paper can be developed in an embedded system, to become a product to be used by energy utilities. From an on-board equipment, the electrical system inspection teams may find it easier to identify faults and may take preventive action to improve the reliability of the electrical power system. For future work, we intend to create an embedded system to be tested on transmission lines, in order to analyze the economic impact of the solution. N.F.S.N. : Formal Analysis, Validation, Writing--Original Draft; S.F.S. : Conceptualization, Writing--Original Draft; L.H.M. : Review & Editing; R.B. : Data Acquisition; A.N. : Review & Editing; L.O.S. : Conceptualization, Writing--Original Draft; G.V.G. : Review & Editing; V.R.Q.L. : Review & Editing. K.-C.Y. : Review & Supervision. All authors have read and agreed to the published version of the manuscript. This work was supported by This work was supported by the Junta De Castilla y Leon--Consejeria De Economia Y Empleo: System for simulation and training in advanced techniques for the occupational risk prevention through the design of hybrid-reality environments with ref. J118. We would like to thank to the Coordination of Superior Level Staff Improvement, awarding a doctoral scholarship to one of the authors. We would like to thank the Canadian Bureau for International Education through the Emerging Leaders in the Americas Program, Government of Canada, who provided a scholarship for Visiting Graduate Research Student in the University of Regina, Canada. We would like to thank to Seed Funding ILIND--Instituto Lusofono de Investigacao e Desenvolvimento, COPELABS. Al Proyeto: Uso de algoritmos y protocolos de comunicacion en dispositivos con enfasis en la privacidad de los datos. The authors declare no conflict of interest.