基于CEEMDAN-IAWT方法的滚动轴承振动信号降噪

任海军,韦冲,谭志强,罗亮,丁显飞

振动与冲击 ›› 2023, Vol. 42 ›› Issue (13) : 199-207.

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (13) : 199-207.
论文

基于CEEMDAN-IAWT方法的滚动轴承振动信号降噪

  • 任海军,韦冲,谭志强,罗亮,丁显飞
作者信息 +

Denoising of rolling bearing vibration signals based on CEEMDAN-IAWT method

  • REN Haijun, WEI Chong, TAN Zhiqiang, LUO Liang, DING Xianfei
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文章历史 +

摘要

针对滚动轴承振动信号中混入噪声的问题,设计了一种自适应白噪声完全集合经验模态分解(CEEMDAN)结合改进自适应小波阈值(IAWT)的联合降噪方法。首先使用CEEMDAN对信号进行模态分解得到本征模态函数(IMFs);然后将得到的IMFs与原信号进行相关性分析识别有效分量;针对小波阈值降噪算法(WT)不能自适应选取小波基和分解层数以及阈值函数存在缺陷的问题,设计了IAWT算法,利用IAWT算法过滤IMFs中的噪声;最后将处理后的IMFs进行信号重构。利用设计的联合降噪算法对仿真信号和实验台信号进行处理可知,相比于WT,使用IAWT处理后的信号信噪比提高了约0.5dB,与原信号的相关系数提高了约0.03,均方根误差降低了约0.01。将本文设计的方法与CEEMDAN-WT等方法对比可得,经本文方法处理后的信号信噪比至少提高了1.37dB,且信号特征保存完好。

Abstract

To solve the problem of mixing noise into rolling bearing vibration signals, a joint noise reduction method is designed, which combines the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the improved adaptive wavelet threshold (IAWT). First, use CEEMDAN to modal decomposition of the signal to obtain intrinsic mode functions (IMFs); Then the obtained IMFs and the original signal were analyzed to identify the effective component. Aiming at the problem that wavelet threshold denoising algorithm(WT) can not adaptively select wavelet base and decomposition layer and threshold function has defects, IAWT algorithm is designed, IAWT algorithm is used to filter noise in IMFs. Finally, the processed IMFs signal is reconstructed. The designed joint denoising algorithm is used to process simulation signals and experimental signals. Compared with WT, the signal-to-noise ratio of the signal processed by IAWT is improved by about 0.5dB, the correlation coefficient with the original signal is increased by about 0.03, and the root mean square error is reduced by about 0.01. By comparing the proposed method with CEEMDAN-WT and other methods, the signal-to-noise ratio of signal processed by the proposed method is improved by at least 1.37dB, and the signal characteristics are well preserved.

关键词

滚动轴承 / 振动信号降噪 / 自适应白噪声完全集合经验模态分解(CEEMDAN) / 改进的自适应小波阈值(IAWT)

Key words

rolling bearing / vibration signal noise reduction / complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) / improved adaptive wavelet threshold(IAWT);

引用本文

导出引用
任海军,韦冲,谭志强,罗亮,丁显飞. 基于CEEMDAN-IAWT方法的滚动轴承振动信号降噪[J]. 振动与冲击, 2023, 42(13): 199-207
REN Haijun, WEI Chong, TAN Zhiqiang, LUO Liang, DING Xianfei. Denoising of rolling bearing vibration signals based on CEEMDAN-IAWT method[J]. Journal of Vibration and Shock, 2023, 42(13): 199-207

参考文献

[1] LEI Z H, WEN G R, DONG S Z, et al. An Intelligent Fault Diagnosis Method Based on Domain Adaptation and Its Application for Bearings Under Polytropic Working Conditions [J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-14.
[2] 樊高瞻, 周俊, 朱昆莉. 基于改进形态-小波阈值降噪的轴承复合故障声学诊断[J]. 振动与冲击, 2020, 39(12): 221-226+288.
FAN Gaozhan, ZHOU Jun, ZHU Kunli. Acoustic Diagnosis of Bearing Complex Fault Based on Improved Morphological Wavelet Threshold Denoising[J]. Journal of Vibration and Shock, 2020, 39(12): 221-226+288.
[3] ZHENG K, LI T, SU Z, et al. Sparse Elitist Group Lasso Denoising in Frequency Domain for Bearing Fault Diagnosis[J]. IEEE Transactions on Industrial Informatics, 2021, 17(7): 4681-4691.
[4] 纪俊卿, 张亚靓, 张静, 等. 基于新小波阈值的轴承故障诊断方法[J]. 小型微型计算机系统, 2021, 42(02): 315-319.
JI Junqing, ZHANG Yaliang, ZHANG Jing, et al. Bearing Fault Diagnosis Method Based on New Wavelet Threshold [J]. Journal of Small and Microcomputer System, 2021, 42(02): 315-319.
[5] 朱敏, 段志善, 郭宝良. EEMD结合小波包的振动筛轴承信号降噪效果分析[J]. 机械设计与制造, 2020, (05): 63-67.
ZHU Min, DUAN Zhishan, GUO Baoliang. Analysis of Noise Reduction Effect of Vibration Screen Bearing Signal Based on EEMD Combined with Wavelet Packet [J]. Machinery Design and Manufacture, 2020, (05): 63-67.
[6] 肖茂华, 张存义, 傅秀清, 等. 基于ICEEMDAN和小波阈值的滚动轴承故障特征提取方法[J]. 南京农业大学学报, 2018, 41(04): 767-774.
XIAO Maohua, ZHANG Cunyi, FU Xiuqing, et al. Rolling Bearing Fault Feature Extraction Method Based on ICEEMDAN and Wavelet Threshold [J]. Journal of Nanjing Agricultural University, 2018, 41(04): 767-774.
[7] M. E. Torres, M. A. Colominas, G. Schlotthauer, et al. A complete ensemble empirical mode decomposition with adaptive noise[C]// IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011:4144-4147.
[8] LIU Y, WANG L H. Drought Prediction Method Based on an Improved CEEMDAN-QR-BL Model[J]. IEEE Access, 2021, 9: 6050-6062.
[9] 米翰宁, 王昕, 任广振, 等. 自适应小波阈值去噪算法用于局部放电白噪声去噪[J]. 高压电器, 2021, 57(06): 94-101.
MI Hanning, WANG Xin, REN Guangzhen, et al.  Adaptive Wavelet Threshold Denoising Algorithm for Pd White Noise Denoising[J]. High Voltage Apparatus, 2021, 57(06): 94-101. 
[10] 杨旭, 邱明, 陈立海, 等. 基于PSO-RWE的自适应小波阈值函数滚动轴承振动信号去噪方法[J]. 航空动力学报, 2020, 35(11): 2339-2347.
YANG Xu, QIU Ming, CHEN Lihai, et al. An Adaptive Wavelet Threshold Function Denoising Method for Rolling Bearing Vibration Signals Based on PSO-RWE [J]. Journal of Aerospace Power, 2020, 35(11): 2339-2347.
[11] WANG Y H, Zhang B, DING F G, et al. Estimating Dynamic Motion Parameters with an Improved Wavelet Thresholding and Inter-Scale Correlation[J]. IEEE Access, 2018, 6(3): 39827-39838.
[12] Donoho D L, Johnstone I M. Adapting to unknown smoothness via wavelet shrinkage[J]. Journal of the American Statistical Association, 1995, 90(12): 1200-1224.
[13] 王亚萍, 匡宇麒, 葛江华, 等. CEEMD和小波半软阈值相结合的滚动轴承降噪[J]. 振动.测试与诊断, 2018, 38(01): 80-86+207.
Wang Yaping, Kuang Yuqi, Ge Jianghua, et al. Noise Reduction of Rolling Bearing Based on CEEMD and Wavelet Semi-Soft Threshold[J]. Journal of Vibration, Measurement & Diagnosis, 2018, 38(01): 80-86+207.
[14] 熊春宝, 王猛, 于丽娜. 桥梁GNSS-RTK变形监测数据的CEEMDAN-WT联合降噪法[J]. 振动与冲击, 2021, 40(09): 12-18.
XIONG Chunbao, WANG Meng, YU Lina. CEEMDAN-WT Joint Noise Reduction Method for Bridge GNSS-RTK Deformation Monitoring Data [J]. Journal of Vibration and Shock, 2021, 40(09): 12-18.
[15] 董鑫, 李国龙, 何坤, 等. 谱图小波阈值降噪及其在滚刀主轴振动信号分析中的应用[J]. 机械工程学报, 2020, 56(11): 96-107.
DONG Xin, LI Guolong, HE Kun, et al. Spectral Wavelet Threshold Denoising and Its Application in Vibration Signal Analysis of Hob Spindle [J]. Journal of Mechanical Engineering, 2020, 56(11): 96-107.
[16] 陈真诚, 吴贤亮, 赵飞骏. EEMD结合小波阈值的光电容积脉搏波信号降噪[J]. 光学精密工程, 2019, 27(06): 1327-1334.
CHEN Zhencheng, WU Xianliang, ZHAO Feijun. EEMD combined with wavelet threshold photoelectric volumetric pulse wave signal denoising [J]. Optics and Precision Engineering, 2019, 27(06): 1327-1334.

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