Optimizing Detection: Compact MobileNet Models for Precise Hall Sensor Fault Identification in BLDC Motor Drives

This paper presents a comprehensive study on fault identification in Hall sensors within Brushless Direct Current (BLDC) motor drives using neural networks. Detecting these faults is critical for optimizing motor performance, enhancing energy efficiency, and ensuring overall reliability. Our objective is to propose an accurate, compact, and efficient fault detection neural network model for real-time monitoring and swift responses to Hall sensor faults. Conventional methods are often computationally demanding and fail to detect subtle faults, leading to significant performance decline and potential catastrophic failures. To address this, we leverage MobileNet-based compact models and compare them with state-of-the-art neural network models to identify the most accurate, lightweight, and fast inference option. Our findings demonstrate that MobileNet models excel in detecting Hall sensor faults, achieving over 90% accuracy with significantly fewer parameters (less than five million) and an impressive inference time of under 25 milliseconds. This highlights MobileNet as a robust and efficient choice for Hall sensor fault detection in BLDC motor drives.