BLDC HALL SENSOR DISPLACEMENT

Brushless DC (BLDC) motors depend on accurate rotor position detection via Hall sensorsfor optimal performance. Faults, such as sensor displacement, can disrupt commutation and lead toefficiency losses. This study utilizes deep learning to detect Hall sensor faults, focusing on a meticulouslyprepared dataset designed for this purpose. The dataset study consists of phase current measurementsunder 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 nanoseconds.Data is structured in an Excel file with columns for time and phase currents. This well-organizeddataset supports the development of deep learning models for accurate fault detection and classification,contributing to enhanced motor control and diagnostic capabilities.