基于循环谱相干的IAS信号滚动轴承故障检测

王红伟; 郭瑜; 钟辉; 杨新敏; 高国泽

doi:10.13224/j.cnki.jasp.20230497

基于循环谱相干的IAS信号滚动轴承故障检测

doi: 10.13224/j.cnki.jasp.20230497

昆明理工大学机电工程学院，昆明 650500

基金项目: 国家自然科学基金（52165067）；云南省重点领域科技计划项目（202002AC080001）

详细信息

作者简介:
王红伟（1997-），男，硕士生，主要研究方向为旋转机械故障特征提取。E-mail：whw20231@163.com

通讯作者:
郭瑜（1971-），男，教授、博士生导师，博士，主要研究方向为机械动态测试、旋转机械故障特征提取。E-mail：kmgary@163.com

中图分类号: V263.6；TH133.33
计量
- 文章访问数: 319
- HTML浏览量: 294
- PDF量: 22
- 被引次数: 0
出版历程
- 收稿日期: 2023-07-31
- 网络出版日期: 2025-09-20

Rolling bearing fault detection based on cyclic spectral coherence of IAS signals

Faculty of Mechanical and Electrical Engineering，Kunming University of Science and Technology，Kunming 650500，China

摘要

摘要:
针对循环谱相干（cyclic spectral coherence, CSCoh）故障信息丰富频带选取困难的问题，结合编码器信号的优势，以瞬时角速度（instantaneous angular speed, IAS）为信号源，提出一种基于重加权峭度（reweighted kurtosis, RK）指标自适应确定CSCoh优化解调频带的方法。首先，基于编码器瞬时角位移信号，采用向前差分法估计IAS信号；其次，采用CSCoh分析提取滚动轴承故障分量，获得以循环阶次和谱阶次组成的双变量谱图；然后，沿谱阶次积分得到子频带改进包络谱（improved envelope spectrum, IES），再采用RK指标表征各子频带中滚动轴承故障信息的丰富性，随后沿谱阶次轴对子频带合并重构，选择合并重构后最大RK值所对应的合并后的子频带作为优化解调频带；最后通过包络分析揭示滚动轴承故障特征。采用本文所提方法对仿真信号和实验数据进行分析，并对比现有方法，所提方法能够有效提取到滚动轴承内圈和外圈故障特征。
- 故障特征提取 /
- 瞬时角速度 /
- 自适应解调频带选取 /
- 循环谱相干 /
- 改进包络谱
Abstract:
In order to address the challenge of selecting frequency bands with abundant fault information in cyclic spectral coherence (CSCoh), the advantages of encoder signals were combined, and instantaneous angular speed (IAS) was taken as the signal source to propose a method for adaptive determination of CSCoh optimized demodulation bands based on the reweighted kurtosis (RK) index. The forward differential method was employed to estimate the IAS signal based on the instantaneous angular displacement information from the encoder. CSCoh analysis was performed to extract the fault-related components of rolling bearings, and a bivariate spectrum composed of cycle order and spectral order was obtained. Subsequently, an improved envelope spectrum (IES) was obtained by integrating along the spectral order. The RK index was used to characterize the richness of rolling bearing fault information in each sub-band. The sub-band was combined and reconstructed along the spectral order axis, and the combined sub-band corresponding to the maximum RK value after the merger reconstruction was selected as the optimized demodulation band. Envelope analysis was conducted to reveal the bearing fault characteristics. By analyzing the simulation signals and experimental data with the method proposed and comparing with the existing methods, the proposed method can effectively extract the fault characteristics of the inner and outer rings of rolling bearing.
- fault feature extraction /
- instantaneous angular speed /
- adaptive demodulation band selection /
- cyclic spectral coherence /
- improved envelope spectrum

HTML

图 1 子频带合并重构过程示意图

Figure 1. Diagram of the sub-band merging and reconstruction process

下载: 全尺寸图片幻灯片

图 2 基于循环谱相干滚动轴承故障检测

Figure 2. Fault detection of rolling bearings based on CSCoh

下载: 全尺寸图片幻灯片

图 3 仿真信号时域波形

Figure 3. Simulating waveform in the time domain

下载: 全尺寸图片幻灯片

图 4 IESFOgram及改进包络谱

Figure 4. IESFOgram and improved envelope spectrum

下载: 全尺寸图片幻灯片

图 5 文献[12]所提方法的处理结果

Figure 5. Results obtained by the method presented in Ref. [12]

下载: 全尺寸图片幻灯片

图 6 文献[10]所提方法的处理结果

Figure 6. Results obtained by the method presented in Ref. [10]

下载: 全尺寸图片幻灯片

图 7 文献[14]所提方法的处理结果

Figure 7. Results obtained by the method presented in Ref. [14]

下载: 全尺寸图片幻灯片

图 8 本文所提方法及改进包络谱

Figure 8. Method in this paper and improved envelope spectrum

下载: 全尺寸图片幻灯片

图 9 实验台与故障轴承

Figure 9. Experimental bench and fault bearing

下载: 全尺寸图片幻灯片

图 10 外圈IAS信号

Figure 10. Outer ring IAS signal

下载: 全尺寸图片幻灯片

图 11 IESFOgram及改进包络谱

Figure 11. IESFOgram and improved envelope spectrum

下载: 全尺寸图片幻灯片

图 12 文献[12]所提方法及组合改进包络谱

Figure 12. Results obtained by the method presented in Ref. [12] and combined improved envelope spectrum

下载: 全尺寸图片幻灯片

图 13 文献[10]所提方法及改进包络谱

Figure 13. Results obtained by the method presented in Ref. [10] and improved envelope spectrum

下载: 全尺寸图片幻灯片

图 14 文献[14]所提方法及包络谱

Figure 14. Results obtained by the method presented in Ref. [14] and improved envelope spectrum

下载: 全尺寸图片幻灯片

图 15 本文所提方法及改进包络谱（实验）

Figure 15. Method in this paper and improved envelope spectrum （experiment）

下载: 全尺寸图片幻灯片

图 16 内圈IAS信号及包络谱

Figure 16. Inner ring IAS signal and envelope spectrum

下载: 全尺寸图片幻灯片

图 17 本文所提方法处理内圈故障结果

Figure 17. Results obtained by method in this paper for processing the inner fault

下载: 全尺寸图片幻灯片

参考文献(18)

[1]	盛嘉玖, 陈果, 康玉祥, 等. 一种用于滚动轴承故障诊断的改进EWT方法[J]. 航空动力学报, 2024, 39(9): 20220677. SHENG Jiajiu, CHEN Guo, KANG Yuxiang, et al. An improved EWT method for fault diagnosis of rolling bearings[J]. Journal of Aerospace Power, 2024, 39(9): 20220677. (in Chinese SHENG Jiajiu, CHEN Guo, KANG Yuxiang, et al. An improved EWT method for fault diagnosis of rolling bearings[J]. Journal of Aerospace Power, 2024, 39(9): 20220677. (in Chinese)
[2]	MIAO Yonghao, ZHAO Ming, LIANG Kaixuan, et al. Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal[J]. Renewable Energy, 2020, 151: 192-203. doi: 10.1016/j.renene.2019.11.012
[3]	RENAUDIN L, BONNARDOT F, MUSY O, et al. Natural roller bearing fault detection by angular measurement of true instantaneous angular speed[J]. Mechanical Systems and Signal Processing, 2010, 24(7): 1998-2011. doi: 10.1016/j.ymssp.2010.05.005
[4]	ZHANG Hengcheng, BORGHESANI P, RANDALL R B, et al. A benchmark of measurement approaches to track the natural evolution of spall severity in rolling element bearings[J]. Mechanical Systems and Signal Processing, 2022, 166: 108466. doi: 10.1016/j.ymssp.2021.108466
[5]	MOUSTAFA W, COUSINARD O, BOLAERS F, et al. Low speed bearings fault detection and size estimation using instantaneous angular speed[J]. Journal of Vibration and Control, 2016, 22(15): 3413-3425. doi: 10.1177/1077546314560600
[6]	GOMEZ J L, BOURDON A, ANDRÉ H, et al. Modelling deep groove ball bearing localized defects inducing instantaneous angular speed variations[J]. Tribology International, 2016, 98: 270-281. doi: 10.1016/j.triboint.2016.02.032
[7]	杨新敏, 郭瑜, 伍星. 基于Fast-SC和EC的滚动轴承故障检测[J]. 振动、测试与诊断, 2023, 43(3): 578-583, 624-625. YANG Xinmin, GUO Yu, WU Xing. Fault detection of rolling bearing based on fast-SC and EC[J]. Journal of Vibration, Measurement & Diagnosis, 2023, 43(3): 578-583, 624-625. (in Chinese YANG Xinmin, GUO Yu, WU Xing. Fault detection of rolling bearing based on fast-SC and EC[J]. Journal of Vibration, Measurement & Diagnosis, 2023, 43(3): 578-583, 624-625. (in Chinese)
[8]	ANTONI J. Cyclic spectral analysis of rolling-element bearing signals: facts and fictions[J]. Journal of Sound and Vibration, 2007, 304(3/4/5): 497-529.
[9]	MAURICIO A, QI Junyu, SMITH W A, et al. Bearing diagnostics under strong electromagnetic interference based on Integrated Spectral Coherence[J]. Mechanical Systems and Signal Processing, 2020, 140: 106673. doi: 10.1016/j.ymssp.2020.106673
[10]	WANG Dong, ZHAO Xuejun, KOU Linlin, et al. A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2019, 122: 754-768. doi: 10.1016/j.ymssp.2018.12.055
[11]	MAURICIO A, SMITH W A, RANDALL R B, et al. Improved envelope spectrum via feature optimisation-gram (IESFOgram): a novel tool for rolling element bearing diagnostics under non-stationary operating conditions[J]. Mechanical Systems and Signal Processing, 2020, 144: 106891. doi: 10.1016/j.ymssp.2020.106891
[12]	MAURICIO A, GRYLLIAS K. Cyclostationary-based multiband envelope spectra extraction for bearing diagnostics: the combined improved envelope spectrum[J]. Mechanical Systems and Signal Processing, 2021, 149: 107150. doi: 10.1016/j.ymssp.2020.107150
[13]	张坤, 胥永刚, 马朝永, 等. 经验快速谱峭度及其在滚动轴承故障诊断中的应用[J]. 振动工程学报, 2020, 33(3): 636-642. ZHANG Kun, XU Yonggang, MA Chaoyong, et al. Empirical fast kurtogram and its application in rolling bearing fault diagnosis[J]. Journal of Vibration Engineering, 2020, 33(3): 636-642. (in Chinese ZHANG Kun, XU Yonggang, MA Chaoyong, et al. Empirical fast kurtogram and its application in rolling bearing fault diagnosis[J]. Journal of Vibration Engineering, 2020, 33(3): 636-642. (in Chinese)
[14]	WANG Yanxue, LIANG Ming. An adaptive SK technique and its application for fault detection of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2011, 25(5): 1750-1764. doi: 10.1016/j.ymssp.2010.12.008
[15]	ANTONI J, XIN Ge, HAMZAOUI N. Fast computation of the spectral correlation[J]. Mechanical Systems and Signal Processing, 2017, 92: 248-277. doi: 10.1016/j.ymssp.2017.01.011
[16]	MIAO Yonghao, ZHAO Ming, HUA Jiadong. Research on sparsity indexes for fault diagnosis of rotating machinery[J]. Measurement, 2020, 158: 107733. doi: 10.1016/j.measurement.2020.107733
[17]	吴磊, 王家序, 张新, 等. 基于最大重加权峭度盲解卷积的风电故障诊断[J]. 中国机械工程, 2022, 33(19): 2356-2363. WU Lei, WANG Jiaxu, ZHANG Xin, et al. Blind deconvolution based on reweighted-kurtosis maximization for wind turbine fault diagnosis[J]. China Mechanical Engineering, 2022, 33(19): 2356-2363. (in Chinese doi: 10.3969/j.issn.1004-132X.2022.19.010 WU Lei, WANG Jiaxu, ZHANG Xin, et al. Blind deconvolution based on reweighted-kurtosis maximization for wind turbine fault diagnosis[J]. China Mechanical Engineering, 2022, 33(19): 2356-2363. (in Chinese) doi: 10.3969/j.issn.1004-132X.2022.19.010
[18]	徐万通, 郭瑜, 陈鑫. 编码器安装误差抑制的轴承外圈故障特征提取[J]. 航空动力学报, 2024, 39(8): 20220584. XU Wantong, GUO Yu, CHEN Xin. Feature extraction of bearing outer ring fault for encoder installation error suppression[J]. Journal of Aerospace Power, 2024, 39(8): 20220584. (in Chinese XU Wantong, GUO Yu, CHEN Xin. Feature extraction of bearing outer ring fault for encoder installation error suppression[J]. Journal of Aerospace Power, 2024, 39(8): 20220584. (in Chinese)