Fusion denoising algorithm for vibration signal of mine hoist with low signal-to-noise ratio
-
摘要: 针对矿井复杂环境下提升机振动信号非线性、低信噪比的特点，提出了一种基于总体平均经验模态分解(CEEMDAN)和自适应小波阈值的矿井提升机振动信号融合去噪算法。首先，采用CEEMDAN算法对含噪的矿井提升机振动信号进行分解，得到本征模态分量(IMF)和残差，对IMF分量进行高低频判断，采用t检验方法对该均值是否显著区别于0进行检验，趋于0的IMF分量为高频分量，显著区别于0的IMF分量为低频分量。然后，选取合适的小波基函数及分解层数，结合自适应小波阈值方法对高频IMF分量进行去噪处理。最后，将处理后的高频IMF分量和未处理的低频IMF分量与残差重构，得到融合算法去噪后的振动信号。分别采用CEEMDAN去噪算法、CEEMD−小波阈值联合去噪算法、CEEMDAN−小波阈值联合去噪算法和CEEMDAN−自适应小波阈值融合去噪算法对仿真信号进行去噪处理，结果表明：① CEEMDAN−自适应小波阈值融合去噪算法去噪后的信号在局部波形特征和信号峰值上与原始信号相似度较高，信号波形的一些特征得到了很好的复原，在去噪过程中很好地保留了原始信号的特征信息。② 采用复合评价指标H作为客观评价标准，CEEMDAN−自适应小波阈值融合去噪算法的H值最小，说明融合去噪算法对于仿真信号的去噪效果要优于其他几种去噪算法的去噪效果。在黑龙江某矿正在运行的矿井提升机上进行试验，结果表明：① 采用db4小波基函数对含噪IMF分量进行4层分解，CEEMDAN−自适应小波阈值融合去噪算法去噪后的信号比较光滑，信号的一些波形特征也得到了很好的复原，在剔除噪声的同时，最大程度上保留了原有信号的特征信息。② 在实际矿井提升机振动信号的去噪过程中，CEEMDAN−自适应小波阈值融合去噪算法的H值最小，去噪效果最佳。Abstract: Aiming at the nonlinear and low signal-to-noise ratio characteristics of mine hoist vibration signal in complex environments, a mine hoist vibration signal fusion denoising algorithm based on complete EEMD with adaptive noise (CEEMDAN) and adaptive wavelet threshold is proposed. Firstly, the CEEMDAN algorithm is used to decompose the noisy mine hoist vibration signal to obtain the intrinsic mode component (IMF) and the residual. The IMF component is judged for high and low frequency. The t-test method is used to test whether the mean value is significantly different from 0. The IMF component which tends to 0 is the high-frequency component, and the IMF component which is significantly different from 0 is the low-frequency component. Secondly, the appropriate wavelet basis function and decomposition level are selected. The high-frequency IMF component is denoised by using the adaptive wavelet threshold method. Finally, the processed high-frequency IMF components and the unprocessed low-frequency IMF components are reconstructed with the residuals to obtain the de-noised vibration signal from the fusion algorithm. The CEEMDAN denoising method, CEEMD-wavelet threshold combined denoising method, CEEMDAN-wavelet threshold combined denoising method and CEEMDAN-adaptive wavelet threshold fusion denoising method are used to denoise the simulated signal respectively. The results show the following points. ① The signal denoised by the CEEMDAN-adaptive wavelet threshold fusion denoising method is similar to the original signal in local waveform features and signal peak values. Some features of the signal waveform have been restored well. The feature information of the original signal has been well preserved in the process of denoising. ② The composite evaluation index H is used as the objective evaluation standard. The H value of the CEEMDAN-adaptive wavelet threshold fusion denoising method is the smallest. This shows that the denoising effect of the fusion denoising algorithm for the simulation signal is better than that of other denoising methods. The experiment is carried out on the running mine hoist in a mine in Heilongjiang Province. The results show the following points. ① The db4 wavelet basis function is used to decompose the noisy IMF component in four layers. The signal de-noised by CEEMDAN-adaptive wavelet threshold fusion de-noising method is smooth. Some waveform features of the signal have also been restored well. While removing the noise, the feature information of the original signal has been retained to the greatest extent. ② In the actual mine hoist vibration signal denoising process, the CEEMDAN-adaptive wavelet threshold fusion denoising method has the smallest H value and the best denoising effect.
-
图 2 CEEMDAN−自适应小波阈值融合去噪算法流程
Figure 2. CEEMDAN adaptive wavelet threshold fusion denoising method flowchart
图 9 CEEMDAN−自适应小波阈值融合去噪信号
Figure 9. CEEMDAN-adaptive wavelet threshold fusim denoising signal
图 13 IMF分量自适应小波阈值小波去噪后的时域波形
Figure 13. Time-domain waveforms of IMF components after adaptive threshold wavelet denoising
图 15 CEEMD−小波阈值联合去噪后的信号
Figure 15. Signal after CEEMD-wavelet threshold combined denoising
图 16 CEEMDAN−小波阈值联合去噪后的信号
Figure 16. Signal after CEEMDAN-wavelet threshold combined denoising
图 17 CEEMDAN−自适应小波阈值融合去噪后的信号
Figure 17. Signal after CEEMDAN-adaptive wavelet threshold fusion denoising
表 1 不同去噪方法的去噪性能对比
Table 1. Comparison of denoising performance of different denoising methods
去噪算法 H CEEMDAN去噪算法 0.916 5 CEEMD−小波阈值联合去噪算法 0.848 2 CEEMDAN−小波阈值联合去噪算法 0.878 5 CEEMDAN−自适应小波阈值融合去噪算法 0.777 3 
下载: 导出CSV
表 2 不同小波基函数去噪效果对比
Table 2. Comparison of denoising effects of different wavelet basis functions
db小波系 sym 小波系 coif 小波系 bior小波系 dbN H symN H coifN H bior Nr.Nd H db1 0.728 3 sym1 0.821 9 coif1 0.564 2 bior1.1 0.756 4 db2 0.298 5 sym2 0.785 6 coif2 0.273 5 bior1.3 0.687 5 db3 0.142 8 sym3 0.567 5 coif3 0.198 6 bior1.5 0.453 8 db4 0.129 7 sym4 0.398 2 coif4 0.198 6 bior2.4 0.256 7 db5 0.130 6 sym5 0.266 4 — — bior3.5 0.157 8 db6 0.132 9 sym6 0.138 6 — — bior3.9 0.264 5 db7 0.137 8 sym7 0.142 7 — — bior4.4 0.389 5 db8 0.256 5 sym8 0.159 6 — — bior5.5 0.563 7 — — — — — — bior6.8 0.765 9
下载: 导出CSV
表 3 不同分解层数去噪效果对比
Table 3. Comparison of denoising effects of different decomposition layers
分解层数 H db4 sym6 coif3 bior3.5 1 0.897 8 0.922 5 0.908 4 0.857 6 2 0.685 6 0.758 6 0.698 5 0.725 6 3 0.232 9 0.276 5 0.265 3 0.298 5 4 0.129 6 0.139 5 0.198 6 0.159 6 5 0.129 8 0.138 6 0.198 9 0.157 8 6 0.132 5 0.139 2 0.199 4 0.156 9 7 0.132 7 0.139 7 0.212 5 0.157 9 8 0.132 9 0.140 2 0.258 6 0.159 3
下载: 导出CSV
表 4 不同去噪算法的去噪性能对比
Table 4. Comparison of denoising performance of different denoising methods
去噪算法 H CEEMDAN去噪算法 0.232 7 CEEMD−小波阈值联合去噪算法 0.185 4 CEEMDAN−小波阈值联合去噪算法 0.178 3 CEEMDAN−自适应小波阈值融合去噪算法 0.155 6
下载: 导出CSV
-
[1] 夏战国,夏士雄,蔡世玉,等. 类不均衡的半监督高斯过程分类算法[J]. 通信学报,2013,34(5):42-51.XIA Zhanguo,XIA Shixiong,CAI Shiyu,et al. Semi-supervised gaussian process classification algorithm addressing the class imbalance[J]. Journal on Communications,2013,34(5):42-51. [2] YE Hanmin, LYU Hao, SUN Qianting. An improved semi-supervised K-means clustering algorithm[C]. IEEE Information Technology, Networking, Electronic and Automation Control Conference, Chongqing, 2016, 71-74. [3] TORRES M E, COLOMINAS M A, SCHLOTTHAUER G, et al. A complete ensemble empirical mode decomposition with adaptive noise[C]. IEEE International Conference on Acoustics, Speech and Signal Processing （ICASSP）, Prague, 2011: 4144-4147. [4] 罗小燕,卢小江,熊洋,等. 小波分析球磨机轴承振动信号特征提取方法[J]. 噪声与振动控制,2016,36(1):148-152.LUO Xiaoyan,LU Xiaojiang,XIONG Yang,et al. Feature extraction method for ball-mill bearing's vibration signals using wavelet analysis[J]. Noise and Vibration Control,2016,36(1):148-152. [5] TIJANI I A,ABDELMAGEED S,FARES A,et al. Improving the leak detection efficiency in water distribution networks using noise loggers[J]. Science of the Total Environment,2022,821:153530. doi: 10.1016/j.scitotenv.2022.153530 [6] BARBOSH M,SINGH P,SADHU A. Empirical mode decomposition and its variants:a review with applications in structural health monitoring[J]. Smart Materials and Structures,2020,29(9):1-45. [7] JIN Tao,LI Qiangguang,MOHAMED M A. A novel adaptive EEMD method for switchgear partial discharge signal denoising[J]. IEEE Access,2019,7:58139-58147. doi: 10.1109/ACCESS.2019.2914064 [8] FAN Jiang,ZHU Zhencai,LI Wei,et al. Lifting load monitoring of mine hoist through vibration signal analysis with variational mode decomposition[J]. Journal of Vibroengineering,2017,19(8):6021-6035. doi: 10.21595/jve.2017.18859 [9] 张振凤,威欢,谭博文. 一种改进的小波阈值去噪方法[J]. 光通信研究,2018(2):75-78.ZHANG Zhenfeng,WEI Huan,TAN Bowen. An improved wavelet threshold denoising method[J]. Study on Optical Communications,2018(2):75-78. [10] 林金朝,刘乐乐,李国权,等. 基于改进EEMD的心电信号基线漂移消除方法[J]. 数据采集与处理,2018,33(5):880-890.LIN Jinzhao,LIU Lele,LI Guoquan,et al. A method for removing baseline drift in ECG signal based on improved EEMD[J]. Journal of Data Acquisition and Processing,2018,33(5):880-890. [11] 张宁,刘友文. 基于 CEEMDAN 改进阈值滤波的微机电陀螺信号去噪模型[J]. 中国惯性技术学报,2018,26(5):665-669.ZHANG Ning,LIU Youwen. Signal denoising model for MEMS gyro based on CEEMDAN improved threshold filtering[J]. Journal of Chinese Inertial Technology,2018,26(5):665-669. [12] JUNIOR P O,FRIMPONG S,ADAM A M,et al. COVID-19 as information transmitter to global equity markets:evidence from CEEMDAN-based transfer entropy approach[J]. Mathematical Problems in Engineering,2021(2):1-19. [13] LU Jingyi,LIN Hong,YE Dong,et al. A new wavelet threshold function and denoising application[J]. Mathematical Problems in Engineering,2016(5):1-8. [14] SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions[C]. Conference on Computer Vision and Pattern Recognition（CVPR）, Boston, 2015: 1-9. [15] ZHANG Xin,GU Hongbin,ZHOU Lai,et al. Improved dual-domain filtering and threshold function denoising method for ultrasound images based on non-subsampled contourlet transform[J]. Journal of Medical Imaging and Health Informatics,2017,7(7):1624-1628. doi: 10.1166/jmihi.2017.2176 [16] JIA Hairong,ZHANG Xueying,BAI Jing. A continuous differentiable wavelet threshold function for speech enhancement[J]. Journal of Central South University,2013,20(8):2219-2225. doi: 10.1007/s11771-013-1727-0 [17] SU Li, ZHAO Guoliang, ZHANG Renyan. Translation-invariant wavelet de-noising method with improved thresholding[C]. IEEE International Symposium on Communications and Information Technology, Beijing, 2005, 619-622. [18] CAO Jian,LI Zhi,LI Jian. Financial time series forecasting model based on CEEMDAN and LSTM[J]. Physica A:Statistical mechanics and its applications,2019,519:127-139. doi: 10.1016/j.physa.2018.11.061 [19] 朱建军,章浙涛,匡翠林,等. 一种可靠的小波去噪质量评价指标[J]. 武汉大学学报(信息科学版),2015,40(5):688-694.ZHU Jianjun,ZHANG Zhetao,KUANG Cuilin,et al. A reliable evaluation indicator of wavelet de-noising[J]. Geomatics and Information Science of Wuhan University,2015,40(5):688-694. [20] GIRI R, ISIK U, KRISHNASWAMY A. Attention wave-u-net for speech enhancement[C]. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics （WASPAA）, New Paltz, 2019: 249-253. [21] AHROUM RIDA, ACHCHAB BOUJEMA. Harvesting Islamic risk premium with long-short strategies: a time scale decomposition using the wavelet theory[J]. International Journal of Finance & Economics, 2019, 26（1）: 430-444. [22] XIE Zhijie,SONG Baoyu,ZHANG Yang,et al. Application of an improved wavelet threshold denoising method for vibration signal processing[J]. Advanced Materials Research,2014:799-806. [23] YANG Hong,CHENG Yuanxun,LI Guohui. A denoising method for ship radiated noise based on Spearman variational mode decomposition,spatial-dependence recurrence sample entropy,improved wavelet threshold denoising,and Savitzky-Golay filter[J]. Alexandria Engineering Journal,2021,60(3):3379-3400. doi: 10.1016/j.aej.2021.01.055 -
点击查看大图
计量
- 文章访问数: 175
- HTML全文浏览量: 43
- PDF下载量: 19
- 被引次数: 0
