Rolling bearing fault time-varying instantaneous characteristics extraction based on Bi-CEEMDAN-CCJC and ELMSSCT
-
摘要:
针对滚动轴承早期故障时的故障性时变瞬时特征微弱且受噪声干扰不易识别的问题，将降噪和特征增强结合考虑。首先根据自适应噪声完全集合经验模态分解（CEEMDAN）的分解规律和分量相关系数特征，构建相关系数跳变准则（CCJC），通过二次CEEMDAN-CCJC方式对原始滚动轴承振动信号进行降噪；然后采用增强局部最大频率啁啾率同步压缩啁啾变换（ELMSSCT）将降噪后的信号表征至时频啁啾率（T-F-C）空间，以此将信号能量聚集于滚动轴承固有频率区域，从而增强固有频率附近的故障性时变瞬时特征，同时去除边频与残余噪声干扰；最后从滚动轴承时域重构信号的Hilbert包络谱辨识故障特征频率。通过对滚动轴承故障仿真信号和实验信号的分析，结果表明所提方法可以清晰准确地提取滚动轴承的故障性时变瞬时特征，且故障特征谱线的频率值与理论值误差不超过1.2%。
Abstract:To address the problem that the fault time-varying instantaneous characteristics of rolling bearing in early fault stage are weak and difficult to identify due to noise interference, the noise reduction and characteristic enhancement were considered together. Firstly, based on the decomposition law of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and component correlation coefficient characteristic, a correlation coefficient jump criterion (CCJC) was constructed, and the original rolling bearing vibration signal was denoised by the Bi-CEEMDAN-CCJC operation. Then, the denoised signal was characterized into the time-frequency-chirp-rate (T-F-C) space using enhanced local maximum frequency-chirp-rate synchrosqueezed chirp transform (ELMSSCT), so as to concentrate the signal energy in the rolling bearing natural frequency regions. Thus, the fault time-varying instantaneous characteristics near the natural frequency can be enhanced, and the side frequency and residual noise interference can be removed. Finally, the fault characteristic frequency was identified from Hilbert envelope spectrum of the rolling bearing time domain reconstruction signal. The rolling bearing simulation and experimental signals analysis results showed that the proposed method can extract the rolling bearing fault time-varying instantaneous characteristics clearly and accurately, and the error between the frequency value of the fault characteristic spectral line and the theoretical value did not exceed 1.2%.
-
图 4 一次CEEMDAN分解结果相关系数曲线
Figure 4. Correlation coefficient curve of the first CEEMDAN decomposition results
图 5 仿真信号一次重构信号时域波形
Figure 5. The first reconstruction signal time-domain waveform of simulation signal
图 6 二次CEEMDAN分解结果相关系数曲线
Figure 6. Correlation coefficient curve of the second CEEMDAN decomposition results
图 7 二次重构信号及其Hilbert包络谱
Figure 7. The second reconstruction signal and its Hilbert envelope spectrum
图 8 二次重构信号的STFT时频图
Figure 8. STFT time-frequency spectrum of the second reconstruction signal
图 10 重构轴承故障信号及其Hilbert包络谱
Figure 10. Bearing fault reconstruction signal and its Hilbert envelope spectrum
图 13 降噪后信号及其Hilbert包络谱（外圈故障）
Figure 13. Denoised signal and its Hilbert envelope spectrum （outer ring fault）
图 14 降噪后信号的STFT时频图（外圈故障）
Figure 14. STFT time-frequency spectrum of the denoised signal （outer ring fault）
图 15 经ELMSSCT处理后的时频图（外圈故障）
Figure 15. Time-frequency diagram after ELMSSCT processing （outer ring fault）
图 16 重构轴承故障信号及其Hilbert包络谱（外圈故障）
Figure 16. Bearing fault reconstruction signal and its Hilbert envelope spectrum （outer ring fault）
图 18 降噪后信号及其Hilbert包络谱（内圈故障）
Figure 18. Denoised signal and its Hilbert envelope spectrum （inner ring fault）
图 19 降噪后信号的STFT时频图（内圈故障）
Figure 19. STFT time-frequency spectrum of the denoised signal （inner ring fault）
图 20 经ELMSSCT处理后的时频图（内圈故障）
Figure 20. Time-frequency diagram after ELMSSCT processing （inner ring fault）
-
[1] 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. (in ChineseDONG 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. (in Chinese) [2] CHEN Baojia, HAI Zhichao, CHEN Xueliang, et al. A time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed[J]. Journal of Sound and Vibration, 2023, 560: 117785. doi: 10.1016/j.jsv.2023.117785 [3] YANG Huan, ZHANG Kun, JIANG Zuhua, et al. An adaptive time-frequency demodulation method and its applications in rolling bearing fault diagnosis[J]. Measurement Science and Technology, 2023, 34(12): 126101. doi: 10.1088/1361-6501/acf7de [4] 刘湘楠, 赵学智, 何宽芳. 圆柱滚子轴承振动信号时频特征提取及状态识别[J]. 振动工程学报, 2022, 35(4): 932-941. LIU Xiangnan, ZHAO Xuezhi, HE Kuanfang. Time-frequency feature extraction and state recognition of vibration signal of cylindrical roller bearing[J]. Journal of Vibration Engineering, 2022, 35(4): 932-941. (in ChineseLIU Xiangnan, ZHAO Xuezhi, HE Kuanfang. Time-frequency feature extraction and state recognition of vibration signal of cylindrical roller bearing[J]. Journal of Vibration Engineering, 2022, 35(4): 932-941. (in Chinese) [5] CHENG Zhiqing. Extraction and diagnosis of rolling bearing fault signals based on improved wavelet transform[J]. Journal of Measurements in Engineering, 2023, 11(4): 420-436. doi: 10.21595/jme.2023.23442 [6] JIA Lianhui, WANG Hongchao, JIANG Lijie, et al. Weak fault detection of rolling element bearing combining robust EMD with adaptive maximum second-order cyclostationarity blind deconvolution[J]. Journal of Vibration and Control, 2023, 29(9/10): 2374-2391. [7] YU Mingyue, ZHANG Yi, YANG Chunxue. Rolling bearing faults identification based on multiscale singular value[J]. Advanced Engineering Informatics, 2023, 57: 102040. doi: 10.1016/j.aei.2023.102040 [8] 梁睿君, 冉文丰, 余传粮, 等. 基于CWT-CNN的齿轮箱运行故障状态识别[J]. 航空动力学报, 2021, 36(12): 2465-2473. LIANG Ruijun, RAN Wenfeng, YU Chuanliang, et al. Recognition of gearbox operation fault state based on CWT-CNN[J]. Journal of Aerospace Power, 2021, 36(12): 2465-2473. (in ChineseLIANG Ruijun, RAN Wenfeng, YU Chuanliang, et al. Recognition of gearbox operation fault state based on CWT-CNN[J]. Journal of Aerospace Power, 2021, 36(12): 2465-2473. (in Chinese) [9] CHAABI L, LEMZADMI A, DJEBALA A, et al. Fault diagnosis of rolling bearings in non-stationary running conditions using improved CEEMDAN and multivariate denoising based on wavelet and principal component analyses[J]. The International Journal of Advanced Manufacturing Technology, 2020, 107(9): 3859-3873. [10] MA Jianpeng, LI Zhen, XIA Changtao, et al. Research on early fault feature extraction technology of aviation bearing based on noise estimation ITD[J]. Measurement Science and Technology, 2024, 35(1): 015105. doi: 10.1088/1361-6501/acfa19 [11] ZHU Hongxuan, JIANG Hongkai, YAO Renhe, et al. Rolling bearing incipient fault feature extraction using impulse-enhanced sparse time-frequency representation[J]. Measurement Science and Technology, 2023, 34(10): 105124. doi: 10.1088/1361-6501/ace545 [12] LI Yifan, ZHANG Xin, CHEN Zaigang, et al. Time-frequency ridge estimation: an effective tool for gear and bearing fault diagnosis at time-varying speeds[J]. Mechanical Systems and Signal Processing, 2023, 189: 110108. doi: 10.1016/j.ymssp.2023.110108 [13] HUANG Huan, BADDOUR N, LIANG Ming. Bearing fault diagnosis under unknown time-varying rotational speed conditions via multiple time-frequency curve extraction[J]. Journal of Sound and Vibration, 2018, 414: 43-60. doi: 10.1016/j.jsv.2017.11.005 [14] HUANG Baoyu, ZHANG Yongxiang, ZHAO Lei, et al. Fault diagnosis of rolling bearings based on impulse feature enhancement and time-frequency joint noise reduction[J]. Journal of Mechanical Science and Technology, 2021, 35(5): 1935-1944. doi: 10.1007/s12206-021-0411-9 [15] YU Gang, WANG Zhonghua, ZHAO Ping, et al. Local maximum synchrosqueezing transform: an energy-concentrated time-frequency analysis tool[J]. Mechanical Systems and Signal Processing, 2019, 117: 537-552. doi: 10.1016/j.ymssp.2018.08.006 [16] 李佳鑫, 孙树峰, 林天然. 基于局部最大值2阶同步压缩变换的变工况轴承故障诊断[J]. 噪声与振动控制, 2023, 43(3): 110-116. LI Jiaxin, SUN Shufeng, LIN Tianran. Fault diagnosis of bearings with variable working conditions based on local maximum second-order synchro-squeezing transform[J]. Noise and Vibration Control, 2023, 43(3): 110-116. (in ChineseLI Jiaxin, SUN Shufeng, LIN Tianran. Fault diagnosis of bearings with variable working conditions based on local maximum second-order synchro-squeezing transform[J]. Noise and Vibration Control, 2023, 43(3): 110-116. (in Chinese) [17] ZHANG Dingcheng, ENTEZAMI M, STEWART E, et al. Wayside acoustic detection of train bearings based on an enhanced spline-kernelled chirplet transform[J]. Journal of Sound and Vibration, 2020, 480: 115401. doi: 10.1016/j.jsv.2020.115401 [18] 栾孝驰, 徐石, 沙云东, 等. 基于GWO-NLM与CEEMDAN的滚动轴承故障诊断方法[J]. 航空动力学报, 2023, 38(5): 1185-1197. LUAN Xiaochi, XU Shi, SHA Yundong, et al. Rolling bearing fault diagnosis method based on GWO-NLM and CEEMDAN[J]. Journal of Aerospace Power, 2023, 38(5): 1185-1197. (in ChineseLUAN Xiaochi, XU Shi, SHA Yundong, et al. Rolling bearing fault diagnosis method based on GWO-NLM and CEEMDAN[J]. Journal of Aerospace Power, 2023, 38(5): 1185-1197. (in Chinese) [19] QIN Limu, YANG Gang, SUN Qi. Maximum correlation Pearson correlation coefficient deconvolution and its application in fault diagnosis of rolling bearings[J]. Measurement, 2022, 205: 112162. doi: 10.1016/j.measurement.2022.112162 [20] ZHANG Ran, WANG Zimeng, TAN Yu, et al. Local maximum frequency-chirp-rate synchrosqueezed chirplet transform[J]. Digital Signal Processing, 2022, 130: 103710. doi: 10.1016/j.dsp.2022.103710 -
下载:
点击查看大图
计量
- 文章访问数: 348
- HTML浏览量: 212
- PDF量: 21
- 被引次数: 0