基于Pied Kingfisher优化器的Bogie变速箱的故障诊断 - 改进了完整的集体实证模式分解与自适应噪声,改进了多尺度加权转换,以及星优化算法 - 最小方形支持向量机器
Guangjian Zhang1, Shilun Ma1, Xulong Wang1
1School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China.
这项研究引入了使用PKO-ICEEMDAN和IMWPE的先进的轮变速箱故障诊断模型. 这种新的方法显著提高了识别变速箱故障的准确性,提高了运行安全.
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:
- 在轮变速箱故障检测中手动判断容易导致不准确.
- 可靠和自动化的故障诊断对于铁路安全和维护至关重要.
研究的目的:
- 开发一个自动化和准确的故障诊断模型,用于波吉变速箱.
- 与现有方法相比,提高故障识别的精度.
主要方法:
- 使用了Pied Kingfisher优化器,改进了全套实证模式分解与自适应噪声 (PKO-ICEEMDAN) 进行信号分解.
- 从重建的信号中提取了改进的多尺度加权变量 (IMWPE).
- 采用海星优化算法 (SFOA) 来优化最小平方支向量机 (LSSVM) 的分类.
主要成果:
- 实现了99.13% (SD 0.09) 的平均训练准确度.
- 获得了99.44% (SD 0.12) 的平均测试准确率.
- 与各种基准模型相比,表现出优越的性能.
结论:
- 拟议的PKO-ICEEMDAN,IMWPE和SFOA-LSSVM模型有效地诊断机变速箱故障.
- 该模型为自动故障检测提供了一个高度准确和可靠的解决方案.
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