基于RIME-VMD和修改后的WKN检测初始转子失衡故障
Qian Wang1,2, Shuo Hu3, Xinya Wang4
1College of Electrical Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450000, China. wangqian@zzuli.edu.cn.
Scientific reports
|February 26, 2024
概括
这项研究引入了一种新的方法,用于使用RIME-VMD和修改的波纹内核网络 (modified-WKN) 检测早期转子失衡故障. 这种方法提高了在信号噪声比率低的环境中对初始故障的敏感性.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 错误诊断 错误诊断 错误诊断 错误诊断 错误诊断 错误诊断
背景情况:
- 转子失衡故障由于其高发生率和微妙的初始症状而构成重大挑战.
- 在低信号噪声比 (SNR) 振动数据中检测初始故障特别困难.
研究的目的:
- 提出一种新且有效的方法,用于早期诊断小转子失衡故障.
- 在具有挑战性的信号条件下提高检测灵敏度和初始故障诊断的准确性.
主要方法:
- 利用RIME算法优化变化模式分解 (VMD) 参数,以从低SNR振动信号中提取故障信息.
- 采用皮尔森相关系数来选择和重建最相关的VMD分解组件.
- 开发了一种修改后的波形内核网络 (modified-WKN),用于多传感器数据融合,以改进初始故障模式检测.
主要成果:
- 与传统的WKN和VMD-WKN方法相比,提议的RIME-VMD和修改的WKN方法显示出更高的检测性能.
- 实验分析证实了该方法对各种SNR级别的初始转子失衡故障的增强灵敏度.
- 修改后的WKN的多传感器数据融合能力显著改善了初始故障特征的处理.
结论:
- 集成的RIME-VMD和修改的WKN方法提供了一个强大的解决方案,用于灵敏和准确地检测初始转子失衡故障.
- 这种方法在SNR低的环境中特别有效,克服了现有技术的局限性.
- 该研究强调了先进的信号处理和机器学习在关键机械健康监测方面的潜力.
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