基于IDBO-VMD和CNN-BiLSTM的滚动轴承的多故障诊断和损坏评估
Lihai Chen1,2, Xiaolong Bai3, Yonghui He3
1College of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, Henan, China. chenlihaide@126.com.
Scientific reports
|August 24, 2025
概括
这项研究引入了改进的虫优化器 (IDBO) 与变化模式分解 (VMD) 以及CNN-BiLSTM模型,用于准确的滚动轴承故障诊断和损坏评估,提高机械设备的可靠性.
科学领域:
- 机械工程
- 人工智能
- 信号处理
背景情况:
- 越来越多的高精度机械设备需要强大的轴承健康监测.
- 机器的可靠运行取决于准确评估轴承的状态.
- 滚动轴承的复合故障带来了重大的诊断挑战.
研究的目的:
- 开发一种有效的方法来诊断复合材料的故障,并评估滚动轴承的损坏.
- 提高轴承故障诊断系统的准确性和可靠性.
- 解决复杂轴承故障的现有诊断技术的局限性.
主要方法:
- 使用改进的泥虫优化器 (IDBO) 来优化变化模式分解 (VMD) 超参数以进行增强的信号预处理.
- 使用卷积神经网络-双向长期短期记忆 (CNN-BiLSTM) 模型进行特征提取和故障分类.
- 集成的混沌映射,金正弦算法和共正弦代策略,以提高IDBO的优化性能.
主要成果:
- 拟议的IDBO优化VMD与CNN-BiLSTM相结合,在滚动轴承复合故障诊断方面表现出卓越的性能.
- 该模型有效地弥补了传统CNN在处理时间序列信号方面的局限性.
- 使用HUST数据集的验证证实了该方案在复合故障诊断和损害评估方面的有效性.
结论:
- 开发的方法为滚动轴承复合故障诊断和损坏评估提供了可靠的解决方案.
- 这种方法通过确保轴承的安全性,显著提高了机械设备的可靠性和精度.
- 集成先进的优化和深度学习技术为未来的状态监测系统提供了有希望的方向.
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