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.

无刷直流电机（Brushless DC, BLDC）依赖通过霍尔传感器（Hall sensors）实现精准的转子位置检测，以获得最佳运行性能。诸如传感器位移类的故障会打乱电机换向流程，进而导致效率损失。本研究借助深度学习开展霍尔传感器故障检测，重点使用了专为该任务精心构建的数据集。该数据集涵盖不同霍尔传感器位移工况下的相电流测量数据，工况分为无延迟（No Delay）、0.0001延迟、0.005延迟以及0.01延迟四类。每种工况均包含60000个数据点，采样间隔为500纳秒。数据以Excel文件格式存储，包含时间与相电流两列数据。这款组织规范的数据集可支撑用于精准故障检测与分类的深度学习模型开发，助力提升电机控制与故障诊断能力。