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. This study utilizes deep learning to detect Hall sensor faults, focusing on a meticulously prepared dataset designed for this purpose. The dataset study 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.