在复合机器故障场景下,具有各种轴承类型的多域振动数据集
Seongjae Lee1, Taewan Kim1, Taehyoun Kim1
1Department of Mechanical and Information Engineering/Smart Cities, University of Seoul, Seoul 02504, Korea.
Data in brief
|September 30, 2024
概括
一个新的多域数据集解决了复杂的机器故障和不断变化的操作条件. 这一数据集通过包括各种轴承类型和复合故障来帮助工业故障诊断的深度学习.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 机器故障通常同时发生在多个组件中.
- 传感器数据域因不同的操作条件而发生变化.
- 用于故障诊断的深度学习需要全面的多域数据.
研究的目的:
- 引入一个新的多域数据集用于机器故障诊断.
- 解决现有数据集在覆盖各种条件和故障方面的局限性.
- 促进研究基于深度学习的智能故障诊断.
主要方法:
- 从深槽球,圆柱形滚子和形滚子轴承收集的振动数据.
- 包含各种操作条件 (采样速率,旋转速度) 和故障类型 (单个,复合).
- 使用短时间里叶变换 (STFT) 生成的光谱图用于二维分析.
主要成果:
- 数据集包括各种轴承类型和复合故障.
- 它涵盖了比传统数据集更广泛的操作条件.
- 用于数据驱动分析,提供光谱图.
结论:
- 这种新型数据集增强了对复杂复合断层和域移位的研究.
- 它支持开发更强大的深度学习故障诊断方法.
- 这种资源对于现实世界的工业应用是有价值的.
相关概念视频
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Consider a member subjected to equal and opposite forces that are applied along a line that does not coincide with the member's neutral axis. In unsymmetrical...
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