Detecção de velocidade e de falha de excentricidade em motores elétricos a partir de sinais sonoros utilizando densidade de máximos

• Ano de defesa: 2019
• Autor(a) principal: Véras, Frank César Lopes
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Engenharia Mecânica; Programa de Pós-Graduação em Engenharia Mecânica; UFPB
• Palavras-chave em Português: Motores BLDC; Teoria do caos; Análise de sinais; Detecção; Excentricidade; Velocidade; BLDC motors; Chaos theory; Signal analysis; Detection; Unbalanced; CNPQ::ENGENHARIAS::ENGENHARIA MECANICA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/19191

Brushless Direct Current (BLDC) motors have been used in a wide range of fields. In some critical applications, failures in these machines can cause operational disasters and cost lives if they are not detected in advance. The classical methods for detecting incipient faults in BLDC motors perform processing of the current signal to obtain the required information. In this work, the SAC-DM (Signal Analysis based on Chaos using Density of Maxima) technique is applied for the first time in the diagnosis of failures of electromechanical systems from sound signals. Multiresolution wavelet analysis is used to separate a chaotic signal component from the sound emitted by the motor, from which the Correlation Length Coefficient (CLC) is calculated using the SAC-DM technique. This work demonstrates that it is feasible to perform dynamic eccentricity diagnosis in BLDC motors by identifying variations of the SAC-DM of the sound signal. The technique exposed in this work requires low computational cost and achieves high success rate. To validate the method, tests were carried out on a small BLDC motor normally used in Unmanned Aerial Vehicle (UAV), demonstrating the ability of the method to detect the speed of the motor in 95,89% of the cases, and to detect eccentricity problems at a fixed speed in 88.34% of the cases, using the Wavelet Transform. For the cases analyzed without Wavelet, 98.38% accuracy in speed detection and 88.65 % in eccentricity detection were achieved at a fixed speed.