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

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

西南交通大学机械工程学院，四川成都 610031

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
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- 被引次数: 0
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

Denoising of Acoustic Emission of Diamond-Coated Mechanical Seals Wear Based on Empirical Wavelet Transform and Kullback-Leibler Divergence

School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China

摘要

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.
- 机械密封 /
- 声发射降噪 /
- 经验小波分解 /
- 金刚石涂层
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.
- mechanical seal /
- acoustic emission denoising /
- empirical wavelet transform /
- diamond coating

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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

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图 2 傅里叶频谱的分割

Figure 2. Spectrum segmentation

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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

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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

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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

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图 6 传统EWT相关系数降噪方法

Figure 6. Traditional denoising method based on EWT and correlation coefficient

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图 7 本文提出的EWT-KLD降噪方法

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

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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

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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

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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

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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

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