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基于EWT-KLD的机械密封金刚石涂层磨损声发射降噪

林志斌 高宏力 吴昱东 谭咏文

林志斌, 高宏力, 吴昱东, 谭咏文. 基于EWT-KLD的机械密封金刚石涂层磨损声发射降噪[J]. 西南交通大学学报, 2024, 59(1): 177-184. doi: 10.3969/j.issn.0258-2724.20210599
引用本文: 林志斌, 高宏力, 吴昱东, 谭咏文. 基于EWT-KLD的机械密封金刚石涂层磨损声发射降噪[J]. 西南交通大学学报, 2024, 59(1): 177-184. doi: 10.3969/j.issn.0258-2724.20210599
LIN Zhibin, GAO Hongli, WU Yudong, TAN Yongwen. Denoising of Acoustic Emission of Diamond-Coated Mechanical Seals Wear Based on Empirical Wavelet Transform and Kullback-Leibler Divergence[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 177-184. doi: 10.3969/j.issn.0258-2724.20210599
Citation: LIN Zhibin, GAO Hongli, WU Yudong, TAN Yongwen. Denoising of Acoustic Emission of Diamond-Coated Mechanical Seals Wear Based on Empirical Wavelet Transform and Kullback-Leibler Divergence[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 177-184. doi: 10.3969/j.issn.0258-2724.20210599

基于EWT-KLD的机械密封金刚石涂层磨损声发射降噪

doi: 10.3969/j.issn.0258-2724.20210599
基金项目: 国家自然科学基金(51775452)
详细信息
    作者简介:

    林志斌(1990—),男,工程师,博士,研究方向为智能化状态监测与故障诊断技术,E-mail:freezinglin@163.com

    通讯作者:

    高宏力(1971—),男,教授,研究方向为设备智能化状态监测与故障诊断技术,E-mail:hongli_gao@swjtu.edu.cn

  • 中图分类号: TP277;TH117.1

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.

     

  • 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 2  傅里叶频谱的分割

    Figure 2.  Spectrum segmentation

    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 4  3种工况下实验样本的功率谱汇总

    Figure 4.  Power spectrum samples under three working conditions

    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

    图 7  本文提出的EWT-KLD降噪方法

    Figure 7.  Proposed denoising method method based on EWT-KLD

    图 8  不同降噪方法得到的去噪AE信号ASL

    Figure 8.  Denoised AE signal ASL via two approaches

    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
    表面粗糙度/μm0.2
    接触表面载荷/N80
    接触表面积/mm2518.1
    热传导系数/(W·(m·k)−142
    线速度/(m·s−12.26~5.28
    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
    11780801~100
    21780100101~200
    3280080201~300
    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.2020.21.034

    JIAO 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.1674-5167.2017.10.182

    YUAN 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.issn1001-0599.2019.07.79

    CHEN 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.1009-9859.2019.01.014

    GUO 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.2021.03.116

    YIN 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.1674-7607.2018.06.012

    LIU 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.1002-6819.2015.11.009

    GUAN 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.2017.11.001

    LV 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.2017.12.015

    WANG 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.
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出版历程
  • 收稿日期:  2021-07-27
  • 修回日期:  2021-10-29
  • 网络出版日期:  2022-11-02
  • 刊出日期:  2021-11-03

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