基于RTH-FMD和1.5维谱的滚动轴承早期故障诊断方法研究

唐贵基; 张龙; 薛贵; 徐振丽; 曾鹏飞; 王晓龙

doi:10.19805/j.cnki.jcspe.2025.240113

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基于RTH-FMD和1.5维谱的滚动轴承早期故障诊断方法研究

数字化与智能化 | 更新时间：2025-10-16

- 基于RTH-FMD和1.5维谱的滚动轴承早期故障诊断方法研究
- Research on Early Fault Diagnosis Method of Rolling Bearing Based on RTH-FMD and 1.5-dimensional Spectrum
- 动力工程学报 2025年45卷第5期页码：714-723
- 作者机构：
  
  1. 华北电力大学机械工程系,河北,保定,071003
  2. 华北电力大学河北省电力机械装备健康维护与失效预防重点实验室,河北,保定,071003
- 作者简介：
  
  [ "唐贵基(1962—),男,山东龙口人,教授,博士生导师,博士,研究方向为电站设备状态监测与故障诊断" ]
- 基金信息：
  
  国家自然科学基金资助项目(52005180);河北省自然科学基金资助项目(E2022502003);中央高校基本科研业务费专项资金资助项目(2023MS127)
- DOI：10.19805/j.cnki.jcspe.2025.240113
  中图分类号： TH133.33
- 网络出版：2025-05-19，
  
  纸质出版：2025
- 稿件说明：
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唐贵基,张龙,薛贵,徐振丽,曾鹏飞,王晓龙. 基于RTH-FMD和1.5维谱的滚动轴承早期故障诊断方法研究动力工程学报, 2025, 45(5): 714-723 https://doi.

org/10.19805/j.cnki.jcspe.2025.240113
唐贵基,张龙,薛贵,徐振丽,曾鹏飞,王晓龙. 基于RTH-FMD和1.5维谱的滚动轴承早期故障诊断方法研究动力工程学报, 2025, 45(5): 714-723 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240113.

org/10.19805/j.cnki.jcspe.2025.240113 DOI：

摘要

针对滚动轴承的早期故障诊断问题

深入研究了一种红尾鹰(RTH)算法参数优化特征模态分解(FMD)和1.5维谱相结合的滚动轴承故障诊断方法。首先

通过理论分析

设计出脉冲能量因子指标(PEFI)

并将其作为适应度函数;其次

利用RTH算法并行搜寻FMD的关键影响参数组合

自适应地达到信号最佳分解效果;再次

通过PEFI选取分解后的最优信号分量

并进行包络解调运算;最后

计算包络信号的1.5维谱

在谱图中分析、提取轴承故障特征频率信息

实现轴承早期微弱故障的准确性诊断。模拟故障实验和工程案例分析结果表明:所研究方法解决了参数自适应的问题

大幅降低了噪声及其他干扰成分对诊断的影响

拥有良好的鲁棒性

能够有效提取轴承早期故障信号中的微弱特征信息

具有重要的实际工程参考价值。

Abstract

Aiming at the problem of early fault diagnosis of rolling bearings

a fault diagnosis method of red-tailed hawk (RTH) algorithm optimization with feature mode decomposition (FMD) and 1.5-dimensional spectrum was studied. Firstly

through theoretical analysis

pulse energy factor index (PEFI) was designed and used as fitness function. Secondly

RTH algorithm combining with FMD was used to search the key parameters of the optimal decomposition in parallel. Then

the optimal signal component after decomposition was selected by PEFI

and the envelope demodulation was performed. Finally

the 1.5-dimensional spectrum of envelope signal was calculated

and the characteristic frequency information of bearing fault extracted was analyzed in the spectrum diagram to realize the accurate diagnosis of early weak bearing fault. The results of simulated fault experiments and engineering case analysis show that the proposed method can solve the problem of parameter self-adaptation

greatly reducing the impact of noise and other interfering components on diagnosis. It possesses good robustness and can effectively extract weak feature information from early bearing fault signals. It has important practical engineering reference value.

关键词

Keywords

references

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