Frequency Occurrence Plots for Motor Fault Diagnosis based on Image Recognition

The dataset has 150 three-second sampling motor current signals from each synthetically-prepared motors. There are five motors with respective fault condition - bearing axis deviation (F1), stator coil inter-turn short circuit (F2), rotor broken strip (F3), outer bearing ring damage (F4), and healthy (H). The motors are run under five coupling loads - 0, 25, 50, 75, and 100%. The sampling signals are collected and processed into frequency occurrence plots (FOPs). Each image has a label, for example F2_L50_130, where F2 is the fault condition, L50 is the coupling load condition. and 130 is the index of motor current signal. A total of 3,750 FOPs which can be used for motor fault diagnosis through image recognition problem.

该数据集包含每台人工制备电机的150组3秒采样电机电流信号。本次实验共设置5类故障工况与1类健康工况，对应工况分别为轴承轴线偏差（F1）、定子线圈匝间短路（F2）、转子断条（F3）、轴承外圈损伤（F4）以及健康状态（H）。所有受试电机均在5种耦合负载工况下运行，负载水平分别为0%、25%、50%、75%与100%。采集到的采样信号经处理后转换为频率出现图（frequency occurrence plots，FOPs）。每张图像均带有专属标签，例如F2_L50_130，其中F2代表故障工况类型，L50代表耦合负载工况，130为对应电机电流信号的索引编号。该数据集总计包含3750张频率出现图，可用于基于图像识别的电机故障诊断任务。

搜集汇总

背景与挑战

背景概述

该数据集包含3750个频率出现图（FOPs），用于电机故障诊断的图像识别研究。数据集涵盖了五种故障条件和五种耦合负载条件，适用于深度学习等技术的应用。