Descriptor: BLDC Hall Sensor Displacement Dataset

Brushless dc (BLDC) motors depend on accurate rotor position detection via Hall sensors for optimal performance. Faults, such as sensor displacement, can disrupt commutation and lead to efficiency losses. Any research that utilizes deep learning to detect Hall sensor faults will benefit from using the BLDC-HSD dataset for training and testing their AI. BLDC-HSD was meticulously prepared and designed for this purpose. BLDC-HSD consists of phase current measurements under various Hall sensor displacement conditions, categorized as no delay, 0.0001 delay, 0.005 delay, and 0.01 delay. Each condition includes 60 000 data points recorded at intervals of 500 ns. Data are structured in an Excel file with columns for time and phase currents. This well-organized dataset supports the development of deep learning models for accurate fault detection and classification, contributing to enhanced motor control and diagnostic capabilities.