Denoising of Acoustic Emission of Diamond-Coated Mechanical Seals Wear Based on Empirical Wavelet Transform and Kullback-Leibler Divergence
-
摘要:
为了准确获得机械密封金刚石涂层在磨损过程的声发射信号，在分析机械密封设备的噪声特性基础上，提出了基于经验小波变换（EWT）和相对熵（KLD）的声发射降噪方法；通过对磨损声发射信号进行经验小波变换得到划分其频带的滤波器组，对磨损声发射信号和背景噪声发射信号用相同的滤波器组划分频带；计算相应频带2种信号的相对熵，用累计和算法在升序排列的相对熵中找到首个大于$3\sigma $的值作为阈值，保留相对熵值大于阈值的频带重构信号，完成降噪. 研究结果表明：本文所提的EWT-KLD方法可以有效抑制不同工况、不同磨损状态的声发射信号的噪声，有效改善了磨损声发射信号的信噪比，尤其是微弱磨损信号的信噪比，提高了密封端面磨损声发射检测的精度和灵敏度；通过与传统降噪方法的对比发现，本文方法能够对不同工况下的密封磨损声发射信号降噪表现出更强的适应性和稳定性，对于及时检测早期密封磨损和准确监测磨损累积变化过程具有重要意义.
Abstract:In order to obtain the pure wear acoustic emission of diamond-coated mechanical seal, the denoising method based on empirical wavelet transform (EWT) and Kullback-Leibler divergence (KLD) was proposed. Firstly, filter bank was calculated with empirical wavelet transform on acquired acoustic emission signal. Then the filter bank was applied to both the acquired acoustic emission signal and background noise acoustic emission signal. The Kullback-Leibler divergences were calculated between the corresponding bands of two signals. The cumulative sum algorithm was employed to find a threshold for determining whether the corresponding band is used for signal reconstruction. The results show that the proposed method can effectively suppress the noise of acoustic emission signals under different working conditions and wear states, and effectively improve the signal-to-noise ratio of wear acoustic emission signals, especially weak wear signals. Compared with the traditional denoising methods, the proposed EWT-KLD method has stronger adaptability and stability for denoising of wear acoustic emission signal under different working conditions, which is of great significance for the monitoring early seal wear and the cumulative wear process of seal.
-
图 1 基于经验小波变换和相对熵的机械密封金刚石涂层磨损声发射信号降噪流程
Figure 1. Denoising flowchart for AE signal of diamond-coated mechanical seal wear based on EWT and KLD
图 3 机械密封环的摩擦状态监测系统
Figure 3. Diagram of friction condition monitoring system for mechanical seals
图 5 降噪前后信号原始波形与功率谱
Figure 5. Waveform and power spectrum of orignal signal and denoised signal
图 6 传统EWT相关系数降噪方法
Figure 6. Traditional denoising method based on EWT and correlation coefficient
表 1 被测试机械密封环的基本参数及形貌
Table 1. Basic parameters and morphology of testing mechanical seals
密封环 材料 端面内
径/mm端面外
径/mm端面涂
层/μm表面维氏
硬度/GPa外观 动环横截面
SEM
（3000 倍）金刚石涂层表面
SEM
（20000 倍）静环 掺杂石墨
的碳化硅52 58 24.5 
动环 碳化硅基
底金刚石
涂层52 63 6 98 
下载: 导出CSV
表 2 被测试机械密封环的设计参数
Table 2. Design parameters of testing mechanical seals
设计参数 值 表面粗糙度/μm 0.2 接触表面载荷/N 80 接触表面积/mm2 518.1 热传导系数/（W·（m·k）−1） 42 线速度/（m·s−1） 2.26～5.28 摩擦系数 0.15
下载: 导出CSV
表 3 实验工况条件
Table 3. Experimental working conditions
工况 转速/（r·min−1） 载荷/N 样本序号 1 1780 80 1～100 2 1780 100 101～200 3 2800 80 201～300
下载: 导出CSV
-
[1] 矫健. 高温油泵机械密封失效分析与对策[J]. 内燃机与配件,2020,321(21): 75-76. doi: 10.3969/j.issn.1674-957X.2020.21.034JIAO Jian. Failure analysis and countermeasure of mechanical seal of high temperature oil pump[J]. Internal Combustion Engine and Parts, 2020, 321(21): 75-76. doi: 10.3969/j.issn.1674-957X.2020.21.034 [2] 郝丽霞. 化工离心泵机械密封失效原因分析[J]. 中国设备工程,2020(3): 143-145.HAO Lixia. Failure analysis of chemical centrifugal pump mechanical seal[J]. China Equipment Engineering, 2020(3): 143-145. [3] 王超. 高温油泵机械密封失效分析及改进[J]. 石油化工设备,2019,48(6): 62-67.WANG Chao. Failure analysis and improvement of mechanical seal of high temperature oil pump[J]. Petro-demical Equipment, 2019, 48(6): 62-67. [4] 黄文涛. 机械密封在化工上的应用及泄漏的应对[J]. 化学工程与装备,2018(3): 185-186.HUANG Wentao. Application of mechanical seal in chemical industry and response to leakage[J]. Chemical Engineering and Equipment, 2018(3): 185-186. [5] 袁明蛟. 石油化工生产常用机械密封形式及失效原因探讨[J]. 中国化工贸易,2017,9(10): 204-206. doi: 10.3969/j.issn.1674-5167.2017.10.182YUAN Mingjiao. Discussion on commonly used mechanical seal forms and failure reasons in petrochemical production[J]. China Chemical Trade, 2017, 9(10): 204-206. doi: 10.3969/j.issn.1674-5167.2017.10.182 [6] 陈志高. 某重水堆核电站主泵机械密封失效原因分析[J]. 设备管理与维修,2019(13): 161-164. doi: 10.16621/j.cnki.issn1001-0599.2019.07.79CHEN Zhigao. Failure analysis of mechanical seal of main pump in a heavy water reaction nuclear power plant[J]. Equipment Management and Maintenance, 2019(13): 161-164. doi: 10.16621/j.cnki.issn1001-0599.2019.07.79 [7] 郭洪涛. 反应器冷却水泵机械密封失效原因及对策[J]. 齐鲁石油化工,2019,47(1): 51-52,56. doi: 10.3969/j.issn.1009-9859.2019.01.014GUO Hongtao. Failure causes and countermeasures of mechanical seal in cooling water pump for reactor[J]. Qilu Petrochemical Technology, 2019, 47(1): 51-52,56. doi: 10.3969/j.issn.1009-9859.2019.01.014 [8] 赵远. 单端面机械密封故障的分析与处理分析[J]. 装备维修技术,2020,176(2): 119.ZHAO Yuan. Fault analysis and treatment analysis of single end mechanical seal[J]. Equipment Maintenance Technology, 2020, 176(2): 119. [9] 尹源,黄伟峰,刘向锋,等. 机械密封智能化的技术基础和发展趋势[J]. 机械工程学报,2021,57(3): 116-128. doi: 10.3901/JME.2021.03.116YIN Yuan, HUANG Weifeng, LIU Xiangfeng, et al. Technical Basis and Development Trend of Mechanical Seal Intelligence[J]. Journal of Mechanical Engineering, 2021, 57(3): 116-128. doi: 10.3901/JME.2021.03.116 [10] TOWSYFYAN H, GU F S, BALL A D, et al. Modelling acoustic emissions generated by tribological behaviour of mechanical seals for condition monitoring and fault detection[J]. Tribology International, 2018, 125: 46-58. doi: 10.1016/j.triboint.2018.04.021 [11] 李晓晖. 流体动压型机械密封端面状态监测及健康评估[D]. 成都: 西南交通大学, 2016. [12] 张尔卿. 机械密封端面状态监测及寿命预测关键技术研究[D]. 成都: 西南交通大学, 2015. [13] FAN Y E, GU F S, BALL A. A review of the condition monitoring of mechanical seals[C]//Proceedings of ASME 7th Biennial Conference on Engineering Systems Design and Analysis. Manchester: [s.n.], 2008: 179-184. [14] LI R Y, HE D. Rotational machine health monitoring and fault detection using EMD-based acoustic emission feature quantification[J]. IEEE Transactions on Instrumentation and Measurement, 2012, 61(4): 990-1001. doi: 10.1109/TIM.2011.2179819 [15] HUANG W F, LIN Y B, LIU Y, et al. Face rub-impact monitoring of a dry gas seal using acoustic emission[J]. Tribology Letters, 2013, 52(2): 253-259. doi: 10.1007/s11249-013-0210-2 [16] 刘忠,宋嘉城,邹淑云,等. 基于EMD的水轮机空化声发射信号阈值降噪方法[J]. 动力工程学报,2018,38(6): 501-507. doi: 10.3969/j.issn.1674-7607.2018.06.012LIU Zhong, SONG Jiacheng, ZOU Shuyun, et al. Threshold denoising method for acoustic emission signals from hydraulic turbine cavitation based on EMD[J]. Journal of Chinese Society of Power Engineering, 2018, 38(6): 501-507. doi: 10.3969/j.issn.1674-7607.2018.06.012 [17] BACCAR D, SÖFFKER D. Wear detection by means of wavelet-based acoustic emission analysis[J]. Mechanical Systems and Signal Processing, 2015, 60/61: 198-207. doi: 10.1016/j.ymssp.2015.02.012 [18] BIANCHI D, MAYRHOFER E, GRÖSCHL M, et al. Wavelet packet transform for detection of single events in acoustic emission signals[J]. Mechanical Systems and Signal Processing, 2015, 64/65: 441-451. doi: 10.1016/j.ymssp.2015.04.014 [19] 关山,彭昶. 刀具磨损声发射信号的混沌特性分析[J]. 农业工程学报,2015,31(11): 60-65. doi: 10.11975/j.issn.1002-6819.2015.11.009GUAN Shan, PENG Chang. Chaotic characteristic analysis of tool wear acoustic emission signal[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(11): 60-65. doi: 10.11975/j.issn.1002-6819.2015.11.009 [20] 律方成,张瑜,董蒙,等. 基于复合评价指标的金属微粒声发射信号最优小波去噪及其特征提取[J]. 高压电器,2017,53(11): 1-8. doi: 10.13296/j.1001-1609.hva.2017.11.001LV Fangcheng, ZHANG Yu, DONG Meng, et al. Optimal wavelet denoising and feature extraction of metal particulate acoustic emission signal based on composite evaluation index[J]. High Voltage Appliances, 2017, 53(11): 1-8. doi: 10.13296/j.1001-1609.hva.2017.11.001 [21] WEI N S, CHEN Z, GU F S, et al. Wavelet package denoising of acoustic emission signals for lubrication oil monitoring in engine systems[C]//Proceedings of IncoME-V & CEPE Net-2020. [S.l.]: Springer, 2021: 69-80. [22] GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010. doi: 10.1109/TSP.2013.2265222 [23] JIANG W L, LI Z B, ZHANG S, et al. Hydraulic pump fault diagnosis method based on EWT decomposition denoising and deep learning on cloud platform[J]. Shock and Vibration, 2021, 2021(8): 6674351.1-6674351.18. [24] 张超,张晨,郭晓苏,等. 基于MCKD-EWT的滚动轴承故障诊断研究[J]. 轴承,2020(5): 43-48.ZHANG Chao, ZHANG Chen, GUO Xiaosu, et al. Study on fault diagnosis for rolling bearings based on MCKD-EWT[J]. Bearing, 2020(5): 43-48. [25] CHEGINI S N, BAGHERI A, NAJAFI F. Application of a new EWT-based denoising technique in bearing fault diagnosis[J]. Measurement, 2019, 144: 275-297. doi: 10.1016/j.measurement.2019.05.049 [26] 王友仁,陈伟,孙灿飞,等. 基于能量聚集度经验小波变换的齿轮箱早期微弱故障诊断[J]. 中国机械工程,2017,28(12): 1484-1490. doi: 10.3969/j.issn.1004-132X.2017.12.015WANG Youren, CHEN Wei, SUN Canfei, et al. Early weak fault diagnosis of gearboxes based on energy aggregation and EWT[J]. China Mechanical Engineering, 2017, 28(12): 1484-1490. doi: 10.3969/j.issn.1004-132X.2017.12.015 [27] 张瑞,邓艾东,司晓东,等. 一种新的声发射信号消噪及故障诊断方法[J]. 振动与冲击,2018,37(4): 75-81.ZHANG Rui, DENG Aidong, SI Xiaodong, et al. A new method for acoustic emission signal de-noised and fault diagnosis[J]. Journal of Vibration and Shock, 2018, 37(4): 75-81. [28] DUBITZKY W, WOLKENHAUER O, CHO K, et al. Encyclopedia of systems biology[M]. New York: Springer, 2013. -
下载:
点击查看大图
计量
- 文章访问数: 253
- HTML全文浏览量: 120
- PDF下载量: 22
- 被引次数: 0