滚动轴承的故障诊断和分类使用ICEEMDAN-CNN-BiLSTM和声波发射
Jinliang Li1, Haoran Sheng1, Bin Liu2
1College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了一种智能方法,用于使用声辐射信号检测轴承故障. 该方法结合了改进的全套实证模式分解与自适应噪声 (ICEEMDAN) 和CNN-BiLSTM模型,在故障识别中实现了高精度.
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:
- 滚动轴承的可靠性对于机械系统至关重要.
- 声波发射 (AE) 是有效的轴承故障检测由于其高频响应和噪声抑制.
- 现有的方法可能会在复杂的故障信号和噪声干扰方面扎.
研究的目的:
- 开发用于滚动轴承故障检测的智能诊断方法.
- 为了提高故障识别准确性和概括能力.
- 将先进的信号处理与深度学习相结合,以获得强大的性能.
主要方法:
- 波形无声化应用于声辐射 (AE) 信号.
- 改进的完整合体实证模式分解与自适应噪声 (ICEEMDAN) 进行信号分解和特征提取.
- 卷积神经网络 (CNN) 和双向长期短期记忆 (BiLSTM) 架构用于端到端的故障识别.
主要成果:
- ICEEMDAN有效地抑制了模式混合,平均速率为6.08%.
- 拟议的CNN-BiLSTM模型实现了平均测试集识别准确率为98.00%.
- 该模型表现出强大的概括性,在独立验证集上达到96.67%的准确性.
结论:
- 拟议的ICEEMDAN和CNN-BiLSTM方法为轴承故障检测提供了一个高度准确和强大的解决方案.
- 智能诊断系统显示出在机械系统监控中的实际应用的巨大潜力.
- 这种方法有效地解决了用于轴承诊断的AE信号处理方面的挑战.
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