Analysis of Diagnostic Capabilities for Degradation of Brushless Direct Current Motors Depending on Varying Simulation Data

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Published Sep 4, 2023
Max Weigert

Abstract

As the use of unmanned aerial vehicles (UAVs) becomes more widespread and their missions more complex, the need for safety measures for their technical components is also increasing. Among the components that are critical for the operation of UAVs, Brushless Direct Current (BLDC) motors are particularly important. This is due to their compact design and low number of wear parts, which make them well-suited for use in UAVs. In this work, test rig and simulation data of BLDC motors degradation are utilized to investigate the capabilities and limitations of different machine learning algorithms. For this purpose, suitable features representing the motor behavior are discussed. Classification and regression tasks are applied to analyze both the fault type and the degradation progress. The simulated data allows for an assessment of the influence of noise and degradation progress on the diagnosis performance. Furthermore, characteristics of various fault types and the representation of their degradation process in the simulation are discussed. The database and the derived features are shared publicly.

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Keywords

Brushless Direct Current Motor, Diagnosis, Simulation

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Regular Session Papers