在杂环境和小样本条件下,通过多源信息融合对旋转机器进行可解释的智能故障诊断
Gaolei Mao1, Jinhua Wang1,2, Yali Sun1
1School of Automation and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究介绍了一种智能方法,用于通过多传感器数据融合来诊断旋转机械故障. 该方法增强了特征提取,在杂,低样本环境中提高了准确性.
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:
- 旋转机械故障诊断对于工业安全至关重要.
- 挑战包括杂的数据,有限的样本和单个传感器信息.
- 在这些条件下,现有的方法在准确性和可靠性方面扎.
研究的目的:
- 为旋转机械提出一种可解释的智能故障诊断方法.
- 为了应对噪音和小样本大小的挑战,使用多源信息融合.
- 提高工业系统的诊断准确性和可靠性.
主要方法:
- 开发了一个多传感器数据智能融合模块 (MSDIFM),使用连续波量变换 (CWT) 和像素级融合.
- 提出了一个多维的自适应不对称的软门残余收缩块 (MASRSB) 来增强特征表示.
- 设计了一个多尺度的Swin变压器 (MSSwin-T),通过扩展受体场和交叉窗口交互来改善特征提取.
主要成果:
- 拟议的方法在CWRU和PT890数据集上取得了出色的诊断性能.
- 在杂的环境和小样本大小中表现出显著的稳定性.
- 验证了多传感器融合和先进特征提取技术的有效性.
结论:
- 开发的可解释的智能故障诊断方法有效处理噪音和小样本条件.
- 多源信息融合和先进的深度学习架构提高了诊断准确度.
- 该方法为工业环境中旋转机械故障诊断提供了可靠的解决方案.
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