###### Abstract  
Interruptions in the supply of electricity cause numerous losses to consumers, whether residential or industrial and may result in fines being imposed on the regulatory agency's concessionaire. In Brazil, the electrical transmission and distribution systems cover a large territorial area, and because they are usually outdoors, they are exposed to environmental variations. In this context, periodic inspections are carried out on the electrical networks, and ultrasound equipment is widely used, due to non-destructive analysis characteristics. Ultrasonic inspection allows the identification of defective insulators based on the signal interpreted by an operator. This task fundamentally depends on the operator's experience in this interpretation. In this way, it is intended to test machine learning applications to interpret ultrasound signals obtained from electrical grid insulators, distribution, class 25 kV. Currently, research in the area uses several models of artificial intelligence for various types of evaluation. This paper studies Multilayer Perceptron networks' application to the classification of the different conditions of ceramic insulators based on a restricted database of ultrasonic signals recorded in the laboratory. artificial neural network; multilayer perceptron; ultrasound; ceramic insulators +
Footnote †: journal: Applied Sciences  
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there is a high interest in keeping the systems working fully to ensure that these indicators will be achieved [2]. In this scenario, one reason for the interruptions is equipment failure that can reach small to large regions depending on the system protection's selectivity. One of the pieces of equipment responsible for the insulation of the system is the insulators [3]. The insulators are present both in the distribution lines of regional scope, as well as in applications of transmission lines of national scope. Therefore, its failure may be linked to the most diverse interruption scenarios [4]. Artificial intelligence techniques can be used promisingly for assessing contaminated insulators; comparative analysis is necessary, as hybrid techniques may be superior to well-established models depending on the problem being assessed [5]. Ceramic insulators are usually robust and durable and are widely used in the electrical power grid [6]. However, as they are usually in an external environment, they are exposed to variations in the environment, so they can suffer the simultaneous action of the applied electrical voltage, the mechanical traction of cables to which they are subjected, and the contaminants of the environment. The main contamination agents are pollution, salinity, and biological agents [7]. These agents' combined action can gradually compromise the insulator in terms of dielectric support, making it more susceptible to failure. The ultrasound equipment is efficient for identifying faults in the electrical power system, as it is a directional device and for this reason, it can be used in the exact identification of the source of the fault. Based on this characteristic, the use of artificial intelligence techniques with the data extracted from ultrasound equipment can improve the identification and prediction of a defect in insulators [8]. Extracting features can improve the interpretation of the ultrasonic signal to identify defects. The ultrasonic signal may have interference noise in the vicinity of the grid that can hide the defect. The application of techniques such as the wavelet transform to filter the ultrasonic signal can improve signal prediction and fault identification [9]. In this context, several studies attempt to reproduce the effects of one or more of these agents on the component in a laboratory environment [10], as well as technical analyzes of the concessionaires on the equipment present in the field [11]. These works aim to produce robust databases used in the development and/or implementation of techniques that make it possible to identify the most susceptible insulators to failure. [PERSON] et al.