基于空间时间特征融合的反转压缩机故障诊断方法
Haibo Xu1,2, Xiaolong Ji1, Xiaogang Qin1,2
1Department of Safety Engineering, College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了一种用于式压缩机的新型故障诊断方法,提高了石化工业中关键组件的可靠性. 时空特征融合模型 (STFFM) 在识别故障方面达到99.14%的准确性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 交换式压缩机在石化和天然气行业至关重要,但由于可变负载和冲击而面临部件故障.
- 目前的故障诊断方法缺乏有效的时空特征提取和相关性分析.
- 关键部件如门和活塞环的故障需要改进诊断技术.
研究的目的:
- 开发一个先进的故障诊断方法,用于互换式压缩机.
- 解决现有方法关于时空特征提取和相关性的局限性.
- 为了提高关键压缩机组件故障识别的精度和可靠性.
主要方法:
- 开发了一个时空特征融合模型 (STFFM).
- 该模型集成了一个多层堆叠的双向封闭的反复单元 (BiGRU) 用于时间特征提取和一个增强的图形同型网络 (GIN) 用于空间特征提取.
- 采用双向多头注意力机制和跨模式封闭更新来进行特征融合和选择.
主要成果:
- STFFM有效地整合了时空断层特征.
- 该模型在实验数据集上实现了高平均准确率99.14%.
- 该方法在捕获复杂故障特征方面表现出卓越的性能.
结论:
- 拟议的时空特征融合方法提供了一种有效的技术途径,用于精确诊断反转压缩机的故障.
- 这种方法提高了关键工业设备的可靠性和安全性.
- 这些发现有助于提高石化和天然气行业的诊断能力.
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