###### Abstract
[12] worked with the detection of contaminants in aerial network insulators by studying the leakage current waveform. Instead of neural networks, the work uses another mathematical tool called short time modified Hilbert transform. In this work, the results were compared with those obtained using other tools or methods. The work method reached 95% while, for example, the use of an Artificial Neural Networks (ANN) reached 85%. [PERSON] et al. [13] developed an analysis using electromagnetic waves of the visible spectrum (image), processing the image in an ANN and convolutional neural network (CNN) to classify the images in three levels of erosion (mechanical defect). The work achieves results of up to 80% using ANN and 89.5% using CNN. An alternative to system evaluation through measurable signs is the ultrasound signal [14]. The analysis of ultrasound signals is a non-invasive preventive option that is already applied in the market and used by energy utility [15]. This technique uses microphones that capture the ultrasonic signal from the insulators and convert that signal into audible frequencies through electronic processing [16]. From the audible information, the operator then evaluates the condition of the component [17]. If the dependence and complications of demanding an operator with experience operating equipment are considered, it is justified to search for techniques that analyze the signal emitted through mathematical tools [18]. Studies are currently using artificial intelligence to interpret inspection equipment signals for electrical power system instead of the human factor. The artificial intelligence models have more space in the research related to the electricity sector [19; 20; 21; 22], whereas they are promising for applications of the analysis of disturbances of an electrical nature [23]. Considering that they can predict patterns fromtheirtorical data, such as the forecast of loads in the power system [24]. However, many surveys use varied models with the need for a large volume of data [25]. This combination of factors makes the application and development of equipment for automatic fault identification more difficult [26]. [PERSON] et al. [27] tested the effectiveness of a non-invasive technique for detecting different types of pollution through multiband analysis (hyperspectral technique) where using an extreme learning machine neural network (ELM) to interpret the results. Despite not identifying the type of pollution, the network obtained an accuracy ranging from 87.5% to 97.5% in the laboratory, depending on the composition of the pollution. [PERSON], [PERSON], [PERSON] worked with ultrasound techniques [28], but in the detection of five different types of leakage current (secondary effects) and three different types of faults caused by contamination and mechanical defects, also using a non-recurring neural network to interpret the results. The acoustic signals of 60, 120 and 180 Hz provided an accuracy of 90% for detecting leakage currents and 85% for classification between corona, crack and surface discharge. Some researchers conducted promising work to evaluate insulators of the electricity distribution system [29]. The problem of fault identification can be seen in two ways, through the classification of the fault or the failure's prediction. Considering a classification problem in [17], the ultrasound signal is evaluated from the comparison of the Fourier transform in relation to different insulators' conditions. [PERSON] [30] using the same database of this paper, obtained 98.8% accuracy in differentiating a dry and non-perforated insulator from a perforated and wet one. The current work would arrive at an accuracy of 97.5% if we consider the same parameters, that is, do not consider the cases with contaminants. Using an optimized algorithm, the classification problem is evaluated and presents promising results, when the particle swarm optimization and the Wavelet transform are combined with an ensemble learning algorithm [31].