Abstract
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. Wavelet Multiresolution Analysis (WMA) is used to separate a chaotic signal component from the sound emitted by the motor. 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.
Highlights
Brushless Continuous Chain (BLDC) motors have excellent power-to-weight ratio and high reliability control [1], important features for electric vehicle traction system applications. Unexpected failures in these motors can be catastrophic, causing financial and human losses, for example when they occur in an industrial environment or in Unmanned Aerial Vehicles (UAVs)
In [22] it was demonstrated that the same behavior detected from the current signal of Brushless Direct Current (BLDC) motors using FFT can be captured from the chaotic behavior of the signal and in [23] a signal processing technique called SAC-DM (Signal Analysis based on Chaos using Density of Maxima) is presented
We propose the application on monitoring BLDC motors in small UAVs for failure detection during the flights
Summary
Brushless Continuous Chain (BLDC) motors have excellent power-to-weight ratio and high reliability control [1], important features for electric vehicle traction system applications. Unexpected failures in these motors can be catastrophic, causing financial and human losses, for example when they occur in an industrial environment or in Unmanned Aerial Vehicles (UAVs). It is possible to classify fault identification methods in general by the sensor used or the signal processing technique. It is possible to find works that aim to identify failures of dynamic eccentricity in several types of electric machines. The most common methods are those that perform acquisition of the electrical signals of the motor. Can be highlighted works, which processing are based on Fourier Transform [9], [10], Finite Elements [11], [12], Wavelet [13] and hybrid processing methods such as: Finite Elements/Fourier [14], Hilbert/Fourier [15], Wavelet/Fourier [16]
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