在可变的操作条件下,轴承和变速箱的混合故障数据集
Yinchuan Hou1, Jian Tang1, Jing Zhang1
1Army Engineering University of the PLA, Nanjing 210007, China.
Data in brief
|November 10, 2025
概括
这项研究引入了一个新的振动信号数据集,用于轴承和轮,使先进的故障诊断. 该数据集有助于改进智能算法,用于在各种工业条件下识别设备故障.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 轴承和轮等旋转元件在机械中至关重要,但在重负荷和不同条件下容易受损.
- 这些组件的故障可能会导致设备停机和运营中断.
- 使用振动信号进行有效的健康监测对于预测性维护至关重要.
研究的目的:
- 为轴承和变速箱的多种故障类型构建一个全面的振动信号数据集.
- 模拟现实的工业运行条件,进行可靠的算法测试.
- 为验证和增强智能故障诊断算法建立一个基准.
主要方法:
- 开发一个多维的振动信号数据集,包括轴承的单一和组合故障 (内环,外环,滚动元件,).
- 包括典型的变速箱缺陷,如牙表面磨损,破牙和异常.
- 为轴承和变速箱创建混合故障数据集,以表示复杂的场景.
主要成果:
- 该数据集为智能诊断算法提供了可靠的基准.
- 它有助于提高故障识别准确度.
- 在复杂的操作条件下实现了增强的模型概括能力.
结论:
- 构建的数据集对于推进旋转机械故障诊断领域至关重要.
- 它支持开发更准确,更强大的智能诊断系统.
- 该数据集将加速工业环境中条件监测的研究和应用.
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