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September 10, 2025Open Access

Predictive Maintenance System to RUL Prediction of Li-Ion Batteries and Identify the Fault Type of Brushless DC Electric Motor from UAVs

DADragos Alexandru AndrioaiaUniversity of Bacău

Key Points

The predictive maintenance system effectively predicts remaining useful life for li-ion batteries, improving UAV safety.
Validation confirms that the proposed architecture can identify fault types in brushless DC electric motors with high reliability.
Machine learning techniques were used to extract critical insights from the data collected within the UAV system.
Fog computing was utilized to enhance data processing and responsiveness in the predictive maintenance infrastructure.

Abstract

Unmanned Aerial Vehicles have started to be used more and more due to the benefits they bring. Failure of Unmanned Aerial Vehicle components may result in loss of control, which may cause property damage or personal injury. In order to increase the operational safety of the Unmanned Aerial Vehicle, the implementation of a Predictive Maintenance system using the Internet of Things is required. In this paper, the authors propose a new architecture of Predictive Maintenance system for Unmanned Aerial Vehicles that is able to identify the fault type of Brushless DC electric motor and determine the Remaining Useful Life of the Li-ion batteries. In order to create the Predictive Maintenance system within the Unmanned Aerial Vehicle, an architecture based on Fog Computing was proposed and Machine Learning was used to extract knowledge from the data. The proposed architecture was practically validated.

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