改进的傅里叶分解方法及其在滚动轴承故障诊断中的应用

PDF(3860 KB)

振动与冲击 ›› 2023, Vol. 42 ›› Issue (12) : 178-186.

论文

黄斯琪1，谭志银1，杨思国1，詹玉新1，王兴龙2

作者信息 +

An improved Fourier decomposition method and its application in fault diagnosis of rolling bearings

HUANG Siqi1,TAN Zhiyin1,YANG Siguo1,ZHAN Yuxin1,WANG Xinglong2

Author information +

文章历史 +

摘要

为了克服傅里叶分解方法在频谱扫描过程中容易获得较多相近边界，导致无效分量过多的问题，提出了一种改进的傅里叶分解方法（Improved Fourier decomposition method，简称IFDM），并将其应用到轴承故障诊断中。首先，IFDM以傅里叶变换为基础，通过建立邻域叠加准则，将同大于或同小于特征平均值的若干相邻原始分量进行合并，得到一组傅里叶固有模态函数（Fourier intrinsic mode functions，简称FIMF），从而减少无效分量。其次，重构峭度值大于均值的若干FIMF分量，提取敏感故障特征信息。然后，采用自适应多尺度加权形态学滤波（Adaptive multi-scale weighted morphological filtering，简称AMWMF）去除重构分量中的无关分量及背景噪声。最后，对滤波信号进行频谱分析。仿真和实测信号的分析结果验证了所提方法在轴承故障诊断中的有效性，同时，与现有方法的对比结果表明了所提方法的优越性。

Abstract

In order to overcome the problem that the Fourier decomposition method can easily obtain more similar boundaries during the spectrum scanning process, resulting in too many invalid components, an improved Fourier decomposition method (IFDM) is proposed. And this method is applied to the bearing fault diagnosis. First, based on the Fourier transform, several adjacent original components that are simultaneously larger or smaller than the feature mean are combined by establishing a neighborhood superposition criterion in IFDM, and a set of Fourier intrinsic mode functions (FIMF) is obtained by this method, thus reducing the invalid components. Secondly, some FIMF components with kurtosis value is greater than the mean value are reconstructed to extract sensitive fault feature information. Then, adaptive multi-scale weighted morphological filtering (AMWMF) is used to remove irrelevant components and background noise in the reconstructed component. Finally, the filtered signal is analyzed by spectrum. The effectiveness of the proposed method in bearing fault diagnosis is verified by the results of simulation and measured signals. At the same time, the superiority of the proposed method is verified in the comparison results with the existing methods.

导出引用

黄斯琪1，谭志银1，杨思国1，詹玉新1，王兴龙2. 改进的傅里叶分解方法及其在滚动轴承故障诊断中的应用[J]. 振动与冲击, 2023, 42(12): 178-186

HUANG Siqi1,TAN Zhiyin1,YANG Siguo1,ZHAN Yuxin1,WANG Xinglong2. An improved Fourier decomposition method and its application in fault diagnosis of rolling bearings[J]. Journal of Vibration and Shock, 2023, 42(12): 178-186

参考文献

[1] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the royal society A-mathematical physical and engineering sciences, 1998, 454(1971): 903-995.
[2] Zheng J D, Su M X, Ying W M, et al. Improved uniform phase empirical mode decomposition and its application in machinery fault diagnosis[J]. Measurement,179, JUL 2021.
[3] 张建伟, 侯鸽, 暴振磊, 等. 基于CEEMDAN与SVD的泄流结构振动信号降噪方法[J]. 振动与冲击, 2017, 36(22): 138-143.
Zhang Jian-wei1, Hou Ge, Bao Zhen-lei1, et al. A signal de-noising method for vibration signals from flood discharge structures based on CEEMDAN and SVD[J]. Journal of Vibration and Shock, 2017, 36(22): 138-143.
[4] Moore K J, Kurt M, Eriten M, et al. Wavelet-bounded empirical mode decomposition for measured time series analysis[J]. Mechanical Systems and Signal Processing, 2018, 99: 14-29.
[5] Wang Y H，Hu K，Lo M. Uniform phase empirical mode decomposition: An optimal hybridization of masking signal and ensemble approcaches[J]. IEEE Access, 2018, 06: 34819-34833.
[6] Li H, Liu T, Wu X, et al. Research on Test Bench Bearing Fault Diagnosis of Improved EEMD Based on Improved Adaptive Resonance Technology[J]. Measurement, 2021, Available online 8 August 2021, 109986.
[7] Wu Z H, Huang N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(01): 1-41.
[8] Wang Y H , Hu K, Lo Me T. Uniform Phase Empirical Mode Decomposition: An Optimal Hybridization of Masking Signal and Ensemble Approaches[J]. IEEE Access, 2018 Jun 15.
[9] Gilles J. Empirical Wavelet Transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010.
[10] Kedadouche M, Thomas M, Tahan A. A comparative study between Empirical Wavelet Transforms and Empirical Mode Decomposition Methods: Application to bearing defect diagnosis[J]. Mechanical systems and signal processing, 2016, 81: 88-107.
[11] Zheng J D, Huang S Q, Pan H Y, et al. An improved empirical wavelet transform and refined composite multiscale dispersion entropy based fault diagnosis method for rolling bearing[J]. IEEE Access, 2019: 1-11.
[12] Dragomiretskiy K, Zosso D. Variational Mode Decomposition[J]. IEEE Transactions on signal processing, 2014, 62(03): 531-544.
[13] Wang Y X, Markert R, Xiang J W, et al. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system[J]. Mechanical Systems and Signal Processing, 2015, 60: 243-251.
[14] Singh P, Joshi S D, Patney R K, et al. The Fourier decomposition method for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A-Mathematical Physical and Engineering Science, 2017, 473(2199): 1-27.
[15] Singhal A, Singh P, Fatimah B, et al. An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique[J]. Biomedical signal processing and control, 2020, 57(SI).
[16] 黄斯琪,郑近德,潘海洋,等. 最大相关峭度反褶积与傅里叶分解方法相结合的滚动轴承故障诊断[J].机械科学与技术,2020,39(08):1163-1170.
Huang Si-qi, Zheng Jin-de, Pan Hai-yang, et al. Rolling Bearing Fault Diagnosis of Maximum Correlation Kurtosis Deconvolution Combining with Fourier Decomposition Method [J]. Mechanical Science and Technology for Aerospace Engineering, 2020,39(08):1163-1170.
[17] Elbi MD, Kizilkaya A. Multicomponent signal analysis: Interwoven Fourier decomposition method[J]. Digital Signal Processing, Volume 104, September 2020.
[18] 刘洋, 刘晓波, 梁珊. 基于傅里叶分解方法的航空发动机转子故障诊断[J]. 中国机械工程, 2019, 30(18): 2156-2163.
Liu Yang, Liu Xiao-bo, Liang Shan. Aeroengine Rotor Fault Diagnosis Based on Fourier Decomposition Method[J]. China Mechanical Engineering, 2019, 30(18): 2156-2163.
[19] 郑近德,潘海洋,程军圣,等.基于自适应经验傅里叶分解的机械故障诊断方法[J].机械工程学报,2020,56(09):125-136.
Zheng Jin-de, Pan Hai-yang, Cheng Jun-sheng, et al. Adaptive Empirical Fourier Decomposition Based Mechanical Fault Diagnosis Method[J]. Journal of Mechanical Engineering, 2020,56(09):125-136.
[20] 胡爱军, 唐贵基, 安连锁. 基于数学形态学的旋转机械振动信号降噪方法[J]. 机械工程学报, 2006, 42(04): 127-130.
Hu Ai-jun, Tang Gui-ji, An Lian-suo. De-noising Technique for Vibration Signals of Rotating Machinery based on Mathematical Morphology Filter[J]. Chinese Journal of Mechanical Engineering, 2006, 42(04): 127-130.
[21] 史维, 严良俊, 谢兴兵, 等. 基于数学形态学与小波阈值组合滤波算法的大地电磁噪声压制[J]. 科学技术与工程, 2019, 19(09): 36-42.
Shi Wei, Yan Liang-jun, Xie Xing-bing, et al．Noise suppression of magnetotclluric sounding data based on mathematical morphology combined with wavelet threshold[J]. Science Technology and Engineering, 2019,19(9):36-42.
[22] Lu S L, He Q B, Wang J. A review of stochastic resonance in rotating machine fault detection[J]. Mechanical systems and signal processing, 2018, 116: 230-260.