空间时间协作感知启用故障特征图形构造和拓采矿用于可变操作条件诊断
Jiaxin Zhao1,2, Xing Wu1,2,3, Chang Liu1,2
1Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming University of Science & Technology, Kunming 650500, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
这项研究引入了一种用于工业设备故障诊断的新方法,增强了在各种操作条件中的概括性. 该方法有效地整合了多源数据,以提高复杂情景中的诊断准确性和可靠性.
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:
- 工业设备故障诊断受到操作条件之间的数据分布差异的挑战,限制了概括性.
- 多源数据通常包含未充分利用的时空信息,阻碍了有效的特征提取.
研究的目的:
- 开发一个时空协作感知驱动的方法,用于构建故障特征图形和采矿拓,用于可变条件诊断.
- 提高工业设备故障诊断系统的概括能力和诊断准确度.
主要方法:
- 使用单源数据和相似性聚类构建故障特征图,验证运行条件不变性.
- 通过协作感知透露了多源特征拓中的时空相关性.
- 开发了一个图形残余卷积网络,用于挖掘多源时空特征.
主要成果:
- 功能图表成功地整合了多个来源的信息,尽管操作变化.
- 该方法精确地捕获了振动路径差异导致的时空延迟.
- 拟议的模型在复杂的条件下实现了高诊断准确性和概括性.
结论:
- 开发的框架为旋转机械故障诊断提供了一个高度可靠的方法.
- 该方法有效地解决了可变条件工业设备诊断方面的挑战.
- 时空协作感知增强了特征表示和诊断性能.
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