Eventos Anais de eventos
COBEM 2023
27th International Congress of Mechanical Engineering
Demagnetization Fault Detection Method In Brushless Dc Motors Based On Fractal Dimensions
Submission Author:
Thyago L. de Vasconcelos Lima , PB
Co-Authors:
Thyago L. de Vasconcelos Lima, Aldeni Sudário de Sousa, Abel Lima Filho, Francisco Belo, Ramon Leonn Victor Medeiros, Igor Stefan
Presenter: Alisson Alves dos Santos
doi://10.26678/ABCM.COBEM2023.COB2023-2148
Abstract
BLDC motors (brushless direct current motors) have numerous benefits over brushed DC motors and induction motors, some of these benefits are better speed versus torque characteristic, high dynamic response, high efficiency, long operating life and silent operation. Despite this, they present an important negative point, the demagnetization failure, whose occurrence in some BLDC applications can cause severe financial loss and risk of death. There are many works in the literature that aim to identify dynamic eccentricity faults in various types of electrical machines, the most common methods are those that perform the acquisition of electrical signals from the motor. There are few works that address the identification and detection of demagnetization faults in BLDC motors using processing techniques based on chaos theory, thus being an area that is little explored by researchers. The main objective of the work is to implement, validate and analyze a solution for the detection of demagnetization fault in BLDC motors based on chaos analysis, more precisely using fractal dimensions. A BLDC EMAX 2822 – 1200 KV motor was used to carry out the experiments. The motor characteristics are: operating voltage: 7.4 – 11.1 V, maximum current: 16 A – 10 sec., KV: 1,200 KV (revolutions per Volts). Current signals were obtained under normal and fault operating conditions at two different speeds. To cause the demagnetization failure of the motor, one of the permanent magnets was worn out. The use of the fractal dimensions method made the analysis simpler, since the demand for computational effort was lower, since it was not necessary to involve transformations to other domains or complex operations in its calculations. It is possible to conclude that the performance of the algorithm was excellent, since for all steps its success rate was 100%. This fact is justified by the separation noted between the fractal dimensions extracted for the studied signals. Therefore, the results were very positive, indicating that the methodology adopted is configured as a tool with good potential for analyzing other fault situations in BLDC.
Keywords
Brushless dc motor, Fault Detection, Fractal Dimensions, Demagnetization
