基于多尺度波束能量和极端学习机的行星变速箱故障诊断方法
Rui Meng1, Junpeng Zhang2, Ming Chen2
1School of Mechanical Engineering, Anhui University of Technology, Maanshan 243002, China.
这项研究引入了多尺度波束能量 (MSWPEE),用于增强行星变速箱的变速器故障诊断. 新的MSWPEE-ELM方法可以准确地确定太阳牙断裂的严重程度.
科学领域:
- 机械工程
- 信号处理
- 状态监测
背景情况:
- 轮是行星变速箱的重要组成部分,它们的可靠性对整体机械系统的性能至关重要.
- 现有的特征提取技术难以准确检测轮故障并达到高诊断准确度.
- 为了防止灾难性故障并确保运行连续性,对行星变速箱的健康状况进行监测至关重要.
研究的目的:
- 为行星变速箱轮开发先进的特征提取方法.
- 为改进故障诊断提出一种名为多尺度波束能量 (MSWPEE) 的新方法.
- 验证拟议的MSWPEE与极端学习机器 (ELM) 结合用于诊断阳光牙断裂的有效性.
主要方法:
- 拟议的多尺度波束能量 (MSWPEE) 方法使用三个不同的尺度因子分解波束信号.
- 在各种操作条件下分解和重建后,为每个节点计算波束数据包的能量.
- 通过整合不同尺度因子的能量值来构建特征向量,然后使用极端学习机器 (ELM) 进行分类.
主要成果:
- 通过MSWPEE-ELM方法,从行星变速箱太阳变速器中提取故障特征的高精度.
- 这种方法有效地诊断出阳光设备的故障, 准确地识别出不同程度的牙断裂严重程度.
- 实验结果证实MSWPEE-ELM在轮故障诊断中的性能优于传统方法.
结论:
- 开发的MSWPEE-ELM方法为行星变速箱故障诊断提供了强大而准确的解决方案.
- 这种方法显著提高了检测和分类轮断严重性的能力.
- 这些发现支持采用MSWPEE-ELM进行关键机械元件的可靠状态监测.
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