A Novel Approach for Speed and Failure Detection in Brushless DC Motors Based on Chaos

TLDR

The paper proposes a method to quantify chaotic behavior for characterizing electromechanical systems, introducing the Signal Analysis based on Chaos using Density of Maxima (SAC‑DM). SAC‑DM applies a peak‑counting algorithm to the current signal’s time domain to detect faults, demonstrated on a small BLdc motor running at various speeds with both regular and unbalanced propellers. The method achieved 99.16 % accuracy in speed detection and 99.79 % accuracy in identifying an unbalanced system at 50 % motor speed.

Abstract

This paper presents an approach developed to quantify the chaotic behavior for the characterization of electromechanical systems. A technique named Signal Analysis based on Chaos using Density of Maxima (SAC-DM) is presented. This technique uses a simple peak counting algorithm in the time domain of the current signal to detect faults. To demonstrate the potential of SAC-DM, an experiment is presented where a small brushless direct current (BLdc) motor at different speeds, with a regular and an unbalanced propeller, is used. The results demonstrate that the SAC-DM was able to detect the speed of the BLdc motor in 99.16% of the cases, and to identify the unbalanced system in 99.79% of the cases, when the speed is at 50%.