基于BAYES-ICEEMDAN-SNR特征增强和ITOC-LSSVM的滚动轴承和轮的混合智能故障诊断框架
Xiaoxu He1, Xingwei Ge1, Zhe Wu1
1School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究引入了一种新的方法,将贝叶斯优化与增强的信号处理相结合,以改善滚动轴承和轮故障诊断. 新方法的准确度超过97%,超过现有模型的性能.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 滚动轴承和轮诊断在特征提取和模型优化方面面临着挑战.
- 现有的方法经常与局部最佳值和参数调节效率作斗争.
研究的目的:
- 为滚动轴承和轮开发一个先进的故障诊断模型.
- 为了提高特征提取的稳定性,并有效地优化诊断模型参数.
主要方法:
- 提议使用贝叶斯优化和自适应信号与噪声比率 (SNR) 增强 (BAYES-ICEEMDAN-SNR) 改进的完整合体实证模式分解与自适应噪声 (ICEEMDAN) 功能提取方法.
- 将BAYES-ICEEMDAN-SNR与改进的科里奥利斯力优化算法 (ITOC) 结合起来,以优化最小平方支持向量机 (LSSVM) 的超参数.
- 整合了切比舍夫混乱映射,考契突变和动态反向学习到ITOC中,以改进全球搜索和本地逃生能力.
主要成果:
- 拟议的贝耶斯-伊塞姆丹-SNR和ITOC优化的LSSVM模型在轴承和轮数据集上实现了超过97%的平均故障识别精度.
- 与现有的诊断模型相比,其表现优越.
- 废弃实验证实了在拟议模型中的个别改进模块的有效性.
结论:
- 开发的方法为滚动轴承和轮的智能故障诊断提供了有效的解决方案.
- 将贝叶斯优化用于SNR增强和改进的ITOC算法的集成显著提高了诊断准确性和模型稳定性.
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