A Novel Approach for Brushless DC Motors Characterization in Drones Based on Chaos

Abstract

A novel technique named Signal Analysis based on Chaos using Density of Maxima (SAC-DM) is presented to analyze Brushless Direct Current (BLDC) motors behavior. These motors are vastly used in electric vehicles, especially in Drones. The proposed approach is compared with the traditional Fast-Fourier Transform (FFT) and the experiments analyzing a BLDC motor of a drone demonstrates similar results but computationally simpler than that. The main contribution of this technique is the possibility to analyze signals in time domain, instead of the frequency domain. It is possible to identify working and faulty behavior with less computational resources than the traditional approach.

Computationally Efficient Force and Moment Models for Propellers in UAV Forward Flight Applications

“…However, in general, knowing the loads as a function of ψ is not so interesting, as the propeller rotates at high rates; one generally cares about the average thrust produced (although it is important to keep this in mind, as the varying loads can excite vibrational modes of a system [71,72], and may be observed using an approach as in [73]). Thus the average thrust over a complete rotation of the propeller is given by…”

“…Furthermore, the pitching moment constants c m,0 , c m,α , are consistently large in magnitude. From thin airfoil theory, this could again be expected for airfoils with very large positive camber [70]; however, there could be some unmodelled phenomena, possibly due to blade flapping [34], or an artefact of the assumptions made to derive the map (73), and could be a subject for future investigation.…”

Eccentricity Failure Detection of Brushless DC Motors From Sound Signals Based on Density of Maxima

“…It results in a single and simple experimental realization that counts the density of maxima associated with the chaotic evolution of the species in an artificial life simulation to infer its correlation length. A technique called Signal Analysis based on Chaos using Density of Maxima (SAC-DM) is presented in [22], [23]. These works demonstrate that the current signal from a BLDC motor is chaotic, and that the correlation length can be estimated from the density of maxima, and apply it to detect failures and estimate the speed of the BLDC motor from the current signal.…”

“…Equation 5 is applied to obtain the theoretical average density of maxima ( ρ 1 ) from the auto-correlation function C from non-periodic signals. The derivative second and forth are represented as C II and C IV of the Auto-correlation Function in zero, as defined in other previous works [21], [22]…”

“…In [22] it was demonstrated that the same behavior detected from the current signal of Brushless Direct Current (BLDC) motors using FFT can also 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. The work also demonstrated that SAC-DM can be applied to identify the speed at which the BLDC is operating and to detect the presence of unbalanced propeller analyzing the motor current signal.…”