基于ABC-VMD和优化的EfficientNet的玉米收割机轴承故障诊断
Zhiyuan Liu1, Wenlei Sun1, Saike Chang1
1School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了对玉米收割机驱动系统的改进故障诊断方法. 它通过使用变化模式分解和EfficientNet提取最佳轴承信号模式来提高准确性,实现超过98%的分类准确性.
科学领域:
- 农业工程 农业工程
- 机械系统诊断 机械系统诊断
- 信号处理 信号处理
背景情况:
- 在玉米收割机驱动系统中的轴承信号分析在最佳模式提取中提出了挑战.
- 现有的故障诊断模型往往缺乏足够的准确性和稳定性.
研究的目的:
- 为玉米收割机驱动系统提出一种新的故障诊断方法.
- 通过利用轴承信号的最佳模式组件来提高故障诊断的准确性和稳定性.
主要方法:
- 使用变化模式分解 (VMD) 与人工蜂群 (ABC) 优化参数分解的振动信号.
- 关键组件通过组合排列,SNR和PSD权重的评估函数进行选择.
- 斯托克威尔变换用于时间频率图像生成.
- 优化的EfficientNet模型 (MBConv,激活功能) 用于最终的故障分类.
主要成果:
- 拟议的方法有效地从轴承振动信号中提取最佳的模态组件.
- 在比较实验中达到超过98%的故障分类准确度.
- 在故障诊断中表现出更高的准确性和稳定性.
结论:
- 开发的方法为玉米收割机驱动系统的故障诊断提供了卓越的方法.
- 最佳的模态组件提取对于提高诊断模型性能至关重要.
- 集成VMD,信号处理和优化的EfficientNet显示了机械健康监测的巨大潜力.
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