Research on Early Fault Diagnosis Method of Rolling Bearing Based on RTH-FMD and 1.5-dimensional Spectrum

TANG Guiji; ZHANG Long; XUE Gui; XU Zhenli; ZENG Pengfei; WANG Xiaolong

doi:10.19805/j.cnki.jcspe.2025.240113

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Research on Early Fault Diagnosis Method of Rolling Bearing Based on RTH-FMD and 1.5-dimensional Spectrum

更新时间：2025-10-16

- Research on Early Fault Diagnosis Method of Rolling Bearing Based on RTH-FMD and 1.5-dimensional Spectrum
- Chinese Journal of Power Engineering Vol. 45, Issue 5, Pages: 714-723(2025)
- 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：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.240113
  CLC： TH133.33
- Published Online：19 May 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
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

肖俊青, 金江涛, 李春, 等. 基于CEEMDAN样本熵与卷积神经网络的轴承故障诊断[J]. 动力工程学报, 2022, 42(5): 429-436. XIAO Junqing, JIN Jiangtao, LI Chun, et al. Bearing fault diagnosis based on CEEMDAN sample entropy and convolutional neural network[J]. Journal of Chinese Society of Power Engineering, 2022, 42(5): 429-436.

安国庆, 秦程, 郭立炜, 等. 峭度滤波器用于电机轴承早期故障特征提取[J]. 电机与控制学报, 2014, 18(6): 55-60. AN Guoqing, QIN Cheng, GUO Liwei, et al. Kurtosis filter in feature extraction for incipient motor bearing fault diagnosis[J]. Electric Machines and Control, 2014, 18(6): 55-60.

董路南, 邓艾东, 范永胜, 等. 基于VMD和改进DenseNet的滚动轴承故障诊断[J]. 动力工程学报, 2023, 43(11): 1500-1505, 1522. DONG Lunan, DENG Aidong, FAN Yongsheng, et al. Rolling bearing fault diagnosis based on VMD and improved DenseNet[J]. Journal of Chinese Society of Power Engineering, 2023, 43(11): 1500-1505, 1522.

马天霆, 孙振波, 邓敏强, 等. 基于RSBLMD算法的风机滚动轴承早期故障诊断[J]. 动力工程学报, 2020, 40(12): 982-987. MA Tianting, SUN Zhenbo, DENG Minqiang, et al. Early fault diagnosis of fan rolling bearings based on RSBLMD algorithm[J]. Journal of Chinese Society of Power Engineering, 2020, 40(12): 982-987.

NI Qing, JI J C, FENG Ke, et al. A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis[J]. Mechanical Systems and Signal Processing, 2022, 164: 108216.

李星, 于德介, 张顶成. 基于最优品质因子信号共振稀疏分解的滚动轴承故障诊断[J]. 振动工程学报, 2015, 28(6): 998-1005. LI Xing, YU Dejie, ZHANG Dingcheng. Rolling bearing fault diagnosis based on optimal quality factor signal resonance sparse decomposition[J]. Journal of Vibration Engineering, 2015, 28(6): 998-1005.

ZHENG Kai, TANG Jiaquan, SHI Yang, et al. A maximum cyclic impulses energy ratio deconvolution fused with enhanced envelope derivative operator frequency spectrum for early fault diagnosis of rolling bearing[J]. Measurement Science and Technology, 2024, 35(2): 025021.

程旭, 江星星, 石娟娟, 等. 广义稀疏解卷算法研究及其轴承故障诊断应用[J]. 电子测量与仪器学报, 2018, 32(5): 182-187. CHENG Xu, JIANG Xingxing, SHI Juanjuan, et al. Generalized sparse deconvolution algorithm and its application of bearing fault diagnosis[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(5): 182-187.

冯坤, 李业政, 胡明辉. 一种用于滚动轴承故障诊断的脉冲增强提取方法[J]. 振动工程学报, 2023, 36(2): 582-592. FENG Kun, LI Yezheng, HU Minghui. A pulse enhancement extraction method for fault diagnosis of rolling bearing[J]. Journal of Vibration Engineering, 2023, 36(2): 582-592.

MIAO Yonghao, ZHANG Boyao, LI Chenhui, et al. Feature mode decomposition: new decomposition theory for rotating machinery fault diagnosis[J]. IEEE Transactions on Industrial Electronics, 2023, 70(2): 1949-1960.

FERAHTIA S, HOUARI A, REZK H, et al. Red-tailed hawk algorithm for numerical optimization and real-world problems[J]. Scientific Reports, 2023, 13(1): 12950.

陈略, 訾艳阳, 何正嘉, 等. 总体平均经验模式分解与1.5维谱方法的研究[J]. 西安交通大学学报, 2009, 43(5): 94-98. CHEN Lue, ZI Yanyang, HE Zhengjia, et al. Research and application of ensemble empirical mode decomposition principle and 1.5 dimension spectrum method[J]. Journal of Xi'an Jiaotong University, 2009, 43(5): 94-98.

唐贵基, 王晓龙. 变分模态分解方法及其在滚动轴承早期故障诊断中的应用[J]. 振动工程学报, 2016, 29(4): 638-648. TANG Guiji, WANG Xiaolong. Variational mode decomposition method and its application on incipient fault diagnosis of rolling bearing[J]. Journal of Vibration Engineering, 2016, 29(4): 638-648.

鄢小安, 贾民平. 基于参数自适应特征模态分解的滚动轴承故障诊断方法[J]. 仪器仪表学报, 2022, 43(10): 252-259. YAN Xiaoan, JIA Minping. A rolling bearing fault diagnosis method based on parameter-adaptive feature mode decomposition[J]. Chinese Journal of Scientific Instrument, 2022, 43(10): 252-259.

YAN Xiaoan, JIA Minping. Bearing fault diagnosis via a parameter-optimized feature mode decomposition[J]. Measurement, 2022, 203: 112016.

WANG Xiangrong, LI Congming, TIAN Hongying, et al. A step-by-step parameter-adaptive FMD method and its application in fault diagnosis[J]. Measurement Science and Technology, 2024, 35(4): 046109.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Fault Diagnosis of Rolling Bearing Based on Channel Attention Mechanism and Multi-scale Subtraction Lightweight Neural Network

基于注意力机制与残差胶囊网络的滚动轴承故障诊断

泛采样频率下基于VMD-MTSO-XGBoost的滚动轴承故障诊断

Related Author

傅行军

梁志宏

范永胜

邓艾东

高原

谯自健

李瑞雄

许学方

Related Institution

国家能源集团江苏电力有限公司

东南大学能源与环境学院

东南大学大型发电装备安全运行与智能测控国家工程研究中心

华东交通大学轨道交通基础设施性能监测与保障国家重点实验室

宁波大学机械工程与力学学院

AI问答

⁰