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二级行星齿轮箱典型故障试验数据

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国家基础学科公共科学数据中心2024-03-05 收录
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https://www.nbsdc.cn/general/dataDetail?id=64edc612bb16e07753c340bb&type=1
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因为大扭矩轮毂驱动系统的功率与转矩太大,齿轮箱故障不可能从实际 减速器上采集获取原始样本数据,所以进行实验模拟,模拟实验台为二级行星轮毂减速机齿轮箱,一级减速比为7,二级减速比为5,总速比为35,电机功率1.5KW,采集频率=25600Hz,实验工况为分半载5nm或全载10nm;转速为25%转速-300rpm;50%转速-600rpm;100%转速-1200rpm,试验在不同转速及负载条件下的各种故障,实验收集记录了一级齿圈断齿二级齿圈裂纹、一级减速器太阳轮断齿故障、一级太阳轮保持架故障、一级太阳轮点蚀故障、一级太阳轮滚珠裂纹故障、一级太阳轮轴承内圈裂纹故障、一级太阳轮轴承外圈裂纹故障、一级行星轮磨损二级行星轮点蚀故障和正常齿轮箱的振动信号,建立原始样本数据库,数据量5.9GB。

Due to the excessively high power and torque of the high-torque hub drive system, it is impossible to collect raw fault sample data from actual reducers. Therefore, experimental simulation is carried out. The simulation test bench uses a two-stage planetary hub reducer gearbox, with a first-stage reduction ratio of 7, second-stage reduction ratio of 5, and total reduction ratio of 35. The motor power is 1.5 kW, and the sampling frequency is set to 25600 Hz. The experimental operating conditions include half load (5 N·m) or full load (10 N·m), as well as rotational speeds of 25% rated speed (300 rpm), 50% rated speed (600 rpm), and 100% rated speed (1200 rpm). Various faults under different rotational speed and load conditions were tested, and vibration signals were collected and recorded for the following states: broken teeth of the first-stage ring gear and cracks of the second-stage ring gear, broken teeth of the first-stage sun gear, faults of the first-stage sun gear cage, pitting corrosion of the first-stage sun gear, cracks of the first-stage sun gear balls, cracks of the inner race of the first-stage sun gear bearing, cracks of the outer race of the first-stage sun gear bearing, wear of the first-stage planetary gears and pitting corrosion of the second-stage planetary gears, as well as normal gearboxes. An original sample database was established, with a total data size of 5.9 GB.
提供机构:
中国矿业大学
搜集汇总
数据集介绍
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背景与挑战
背景概述
该数据集为二级行星齿轮箱典型故障试验数据,来源于国家重点研发计划项目的实验模拟,采集频率为25600Hz,覆盖一级齿圈断齿、太阳轮点蚀等多种故障类型,以及不同转速和负载工况,数据量达5.9GB,共1168个文件,主要用于旋转机械故障诊断研究。数据集具有多故障模拟、高采样频率和大数据量的特点,为故障诊断模型训练提供丰富资源。
以上内容由遇见数据集搜集并总结生成
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