带式输送机托辊轴承声信号增强方法

游卿华; 伍鹏; 马波

doi:10.13272/j.issn.1671-251x.2024120083

带式输送机托辊轴承声信号增强方法

游卿华^{1, 2,},
伍鹏^{1, 2},
马波^{1, 2}

1.
北京化工大学机电工程学院，北京　100029
2.
北京化工大学高端机械装备健康监控与自愈化北京市重点实验室，北京　100029

基金项目: 国家自然科学基金项目（62273025）。

详细信息

作者简介:
游卿华（1999—），男，河南开封人，硕士研究生，主要研究方向为设备状态监测与诊断，E-mail：13126635083@163.com

中图分类号: TD634.1
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- 文章访问数: 26
- HTML全文浏览量: 4
- PDF下载量: 3
出版历程
- 收稿日期: 2024-12-30
- 修回日期: 2025-03-04
- 网络出版日期: 2025-02-27
- 刊出日期: 2025-03-14

Acoustic signal enhancement method for belt conveyor idler bearings

YOU Qinghua^{1, 2,},
WU Peng^{1, 2},
MA Bo^{1, 2}

1.
College of Mechanical and Electrical, Beijing University of Chemical Technology, Beijing 100029, China
2.
Beijing Key Laboratory of Health Monitoring and Self-Recovery for High-End Mechanical Equipment, Beijing University of Chemical Technology, Beijing 100029, China

摘要

摘要:
针对目前带式输送机托辊轴承声信号增强方法存在过度降噪导致信号失真、适应性差，提取复杂声场特征效果较差等问题，提出一种基于直方图噪声估计和维纳滤波的带式输送机托辊轴承声信号增强方法。首先，计算轴承声信号各频带功率谱，构建功率谱直方图进行噪声谱估计，并对噪声谱进行一阶递归平滑；其次，利用噪声谱估计计算维纳滤波增益函数，得到维纳滤波降噪后的声信号；然后，对降噪后的声信号进行包络谱分析，通过对比实际测得的故障特征频率和理论的故障特征频率，实现对带式输送机托辊轴承故障诊断。实验和现场测试结果表明：基于直方图噪声估计和维纳滤波的带式输送机托辊轴承声信号增强方法得到的包络谱中含有明显的故障频率及其倍频成分，实验数据信噪比至少提升了1.14 dB，现场数据信噪比至少提升了1.04 dB，轴承故障特征提取效果较好，可实现环境噪声干扰严重下的带式输送机托辊轴承声信号增强。
- 带式输送机 /
- 托辊轴承 /
- 声信号增强 /
- 直方图噪声估计 /
- 维纳滤波
Abstract:
To address the issues in existing acoustic signal enhancement methods for belt conveyor idler bearings, such as excessive noise reduction leading to signal distortion, poor adaptability, and ineffective extraction of complex sound field characteristics, a method based on histogram noise estimation and Wiener filtering was proposed. First, the power spectrum of each frequency band of acoustic signal was computed, and a power spectrum histogram was constructed to estimate the noise spectrum, followed by first-order recursive smoothing of the noise spectrum. Next, the estimated noise spectrum was used to calculate the Wiener filter gain function, obtaining the noise-reduced acoustic signal. Then, envelope spectrum analysis was performed on the denoised acoustic signal. By comparing the measured fault characteristic frequencies with theoretical fault characteristic frequencies, fault diagnosis of the belt conveyor idler bearing was achieved. Experimental and field test results show that the proposed acoustic signal enhancement method, based on histogram noise estimation and Wiener filtering, produces an envelope spectrum containing distinct fault characteristic frequencies and their harmonic components. The signal-to-noise ratio (SNR) of the experimental data improved by at least 1.14 dB, while the SNR of the field data improved by at least 1.04 dB. The method demonstrates good performance in extracting bearing fault features and can effectively enhance acoustic signals for belt conveyor idler bearings under severe environmental noise interference.
- belt conveyor /
- idler bearing /
- acoustic signal enhancement /
- histogram noise estimation /
- Wiener filtering

HTML全文

图 1 基于直方图噪声估计和维纳滤波的带式输送机托辊轴承故障诊断流程

Figure 1. Process of belt conveyor idler bearing fault diagnosis based on histogram noise estimation and Wiener filtering

下载: 全尺寸图片幻灯片

图 2 带式输送机托辊轴承故障诊断实验台

Figure 2. Test bench for fault diagnosis of belt conveyor idler bearing

下载: 全尺寸图片幻灯片

图 3 实验台和麦克风阵列示意图

Figure 3. Schematic of the test bench and microphone array

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图 4 内圈故障原始波形

Figure 4. Original waveform of inner ring fault

下载: 全尺寸图片幻灯片

图 5 内圈故障降噪后波形

Figure 5. Waveform of inner ring fault after noise reduction

下载: 全尺寸图片幻灯片

图 6 内圈故障原始包络谱

Figure 6. Original envelope spectrum of inner ring fault

下载: 全尺寸图片幻灯片

图 7 内圈故障降噪后包络谱

Figure 7. Envelope spectrum of inner ring fault after noise reduction

下载: 全尺寸图片幻灯片

图 8 文献[8]方法处理后的内圈故障包络谱

Figure 8. Envelope spectrum of inner ring fault after processing with method in reference [8]

下载: 全尺寸图片幻灯片

图 9 文献[9]方法处理后的内圈故障包络谱

Figure 9. Envelope spectrum of inner ring fault after processing with method in reference [9]

下载: 全尺寸图片幻灯片

图 10 文献[13]方法处理后的内圈故障包络谱

Figure 10. Envelope spectrum of inner ring fault after processing with method in reference [13]

下载: 全尺寸图片幻灯片

图 11 现场信号原始包络谱

Figure 11. Original envelope spectrum of on-site signal

下载: 全尺寸图片幻灯片

图 12 现场信号降噪后包络谱

Figure 12. Envelope spectrum of on-site signal after noise reduction

下载: 全尺寸图片幻灯片

表 1 故障模拟实验参数

Table 1 Parameters of fault simulation test

参数	数值	参数	数值
输入转速/（r·min⁻¹）	360	麦克风间距/m	2
采样频率/Hz	128 00	麦克风高度/m	0.35
采样点数/个	8 192	阵列距托辊垂直距离/m	1.2
阵列麦克风数量/个	5

下载: 导出CSV

表 2 轴承加工缺陷和故障信息

Table 2 Bearing manufacturing defects and fault information

故障类型	缺陷数量/条	缺陷尺寸/mm	缺陷频率/Hz
外圈故障	4	1×1	18.44
内圈故障	2	1×1	29.57

下载: 导出CSV

表 3 实验数据降噪后信号和原始信号的信噪比

Table 3 Signal-to-noise ratio of denoised and original signals in test data

组别	信噪比/dB
组别	本文方法	文献[8]方法	文献[9]方法	文献[13]方法	原始信号
1	2.46	2.19	2.25	1.97	1.32
2	2.38	1.94	2.18	1.89	1.25
3	2.55	2.25	2.21	2.07	1.48

下载: 导出CSV

表 4 现场数据降噪后信号和原始信号的信噪比

Table 4 Signal-to-noise ratio of denoised and original signals in on-site data

组别		信噪比/dB
组别	本文方法	文献[8]方法	文献[9]方法	文献[13]方法	原始信号
1	2.91	2.58	2.76	2.44	1.92
2	2.73	2.53	2.55	2.48	1.84
3	2.80	2.61	2.63	2.51	1.76

下载: 导出CSV

参考文献(21)

[1]	郭砚秋,苗长云,刘意. 基于热红外图像的带式输送机托辊故障检测研究[J]. 工矿自动化,2023,49(10):52-60. GUO Yanqiu,MIAO Changyun,LIU Yi. Research on fault detection of belt conveyor roller based on thermal infrared image[J]. Journal of Mine Automation,2023,49(10):52-60.
[2]	秦翥. 带式输送机智能化发展现状研究[J]. 煤矿机械,2025,46(1):73-76. QIN Zhu. Research on development status of intelligent belt conveyor[J]. Coal Mine Machinery,2025,46(1):73-76.
[3]	韩京哲,熊晓燕,陈帅,等. 带式输送机时变负载驱动特性的研究[J]. 机械设计与制造,2023(12):146-151. HAN Jingzhe,XIONG Xiaoyan,CHEN Shuai,et al. Research on driving characteristics of virtual prototype of belt conveyor[J]. Machinery Design & Manufacture,2023(12):146-151.
[4]	刘勇. 基于声信号的带式输送机托辊故障特征分析[J]. 中国安全科学学报,2023,33(增刊2):13-17. LIU Yong. Fault characteristic analysis of belt conveyor rollers based on sound signal[J]. China Safety Science Journal,2023,33(S2):13-17.
[5]	张中盘,张明,时瑛,等. 皮带输送机托辊故障声源定位方法[J]. 噪声与振动控制,2024,44(1):142-147. DOI: 10.3969/j.issn.1006-1355.2024.01.022 ZHANG Zhongpan,ZHANG Ming,SHI Ying,et al. Sound source localization method for belt conveyor idler faults[J]. Noise and Vibration Control,2024,44(1):142-147. DOI: 10.3969/j.issn.1006-1355.2024.01.022
[6]	杨小权,刘曰木,刘江. 基于卡尔曼滤波与子带选取的轴承声信号增强方法[J]. 机电工程,2023,40(11):1673-1681. YANG Xiaoquan,LIU Yuemu,LIU Jiang. Acoustic signal enhancement method for bearings based on Kalman filtering and sub-band selection[J]. Journal of Mechanical & Electrical Engineering,2023,40(11):1673-1681.
[7]	张晓东,张玉强,杜方鹏,等. 基于SBF−ISVD的带式输送机声信号增强方法[J]. 机电工程,2024,41(10):1875-1884. DOI: 10.3969/j.issn.1001-4551.2024.10.016 ZHANG Xiaodong,ZHANG Yuqiang,DU Fangpeng,et al. Acoustic signal enhancement method of belt conveyor based on SBF-ISVD[J]. Journal of Mechanical & Electrical Engineering,2024,41(10):1875-1884. DOI: 10.3969/j.issn.1001-4551.2024.10.016
[8]	YU Gongye,YAN Ge,MA Bo. Feature enhancement method of rolling bearing acoustic signal based on RLS-RSSD[J]. Measurement,2022,192. DOI: 10.1016/j.measurement.20.11088.
[9]	费红博,张超,吴乐,等. 基于VMD−MCKD的微弱故障信号降噪及冲击特征增强方法[J/OL]. 机电工程:1-11[2024-11-04]. http://kns.cnki.net/kcms/detail/.1088.th.20240914.144.004.html. FEI Hongbo,ZHANG Chao,WU Le,et al. Method for denoising weak fault signals and enhancing impact characteristics based on VMD-MCKD[J/OL]. Journal of Mechanical & Electrical Engineering:1-11[2024-11-04]. http://kns.cnki.net/kcms/detail/.1088.th.20240914.144.004.html.
[10]	石渡江,王文波. 基于改进TVF−EMD与SVD的轴承故障特征提取[J]. 机床与液压,2024,52(18):218-229. DOI: 10.3969/j.issn.1001-3881.2024.18.030 SHI Dujiang,WANG Wenbo. Bearing fault feature extraction based on improved TVF-EMD and SVD[J]. Machine Tool & Hydraulics,2024,52(18):218-229. DOI: 10.3969/j.issn.1001-3881.2024.18.030
[11]	张伟涛,张东江,纪晓凡,等. 基于CCA和多通道循环维纳滤波的滚动轴承故障源分析[J]. 振动与冲击,2023,42(24):317-325. ZHANG Weitao,ZHANG Dongjiang,JI Xiaofan,et al. Fault analysis of rolling element bearing based on CCA and a multichannel cyclic Wiener filter[J]. Journal of Vibration and Shock,2023,42(24):317-325.
[12]	李建坡,李美霖,杨涛,等. 大规模MIMO−OFDM系统中低复杂度维纳滤波信道估计算法[J]. 吉林大学学报(工学版),2022,52(1):211-218. LI Jianpo,LI Meilin,YANG Tao,et al. Low-complexity wiener filter channel estimation algorithm in massive MIMO-OFDM system[J]. Journal of Jilin University(Engineering and Technology Edition),2022,52(1):211-218.
[13]	许春冬,张震,战鸽,等. 面向语音增强的约束序贯高斯混合模型噪声功率谱估计[J]. 声学学报,2017,42(5):633-640. XU Chundong,ZHANG Zhen,ZHAN Ge,et al. Noise power estimation based on constrained sequential Gaussian mixture model for speech enhancement[J]. Acta Acustica,2017,42(5):633-640.
[14]	TIAN Fengchun,YANG S X,DONG K. Circuit and noise analysis of odorant gas sensors in an E-nose[J]. Sensors,2005,5(1):85-96. DOI: 10.3390/s5010085
[15]	潘甲. 多通道语音增强算法研究[D]. 武汉:湖北工业大学,2017. PAN Jia. Research on multichannel speech enhancement algorithm[D]. Wuhan:Hubei University of Technology,2017.
[16]	陈敬军,范威. 阵列信号维纳滤波用于主动声呐图像增强处理[J]. 声学技术,2021,40(6):858-863. DOI: 10.3969/j.issn.1000-3630.2021.6.sxjs202106018 CHEN Jingjun,FAN Wei. Active sonar image enhancement using Wiener filtering on array signals[J]. Technical Acoustics,2021,40(6):858-863. DOI: 10.3969/j.issn.1000-3630.2021.6.sxjs202106018
[17]	ZHENG Chengshi,ZHANG Huiyong,LIU Wenzhe,et al. Sixty years of frequency-domain monaural speech enhancement:from traditional to deep learning methods[J]. Trends in Hearing,2023,27. DOI: 10.1177/2331216523120991.
[18]	彭健新,唐云飞. 谱减法与维纳滤波法相结合的睡眠鼾声降噪处理[J]. 华南理工大学学报(自然科学版),2018,46(3):103-107. DOI: 10.3969/j.issn.1000-565X.2018.03.015 PENG Jianxin,TANG Yunfei. Noise Reduction of snoring sound by using traditional spectral subtraction and wiener filter[J]. Journal of South China University of Technology(Natural Science Edition),2018,46(3):103-107. DOI: 10.3969/j.issn.1000-565X.2018.03.015
[19]	WU Peng,YU Gongye,DONG Naiji,et al. Acoustic feature extraction method of rotating machinery based on the WPE-LCMV[J]. Machines,2022,10(12). DOI: 10.3390/MACHINES10121170.
[20]	LIU Juan,FU Shiming,LIU Fen,et al. Intelligent fault diagnosis of belt conveyor rollers using a polar KNN algorithm with audio features[J]. Engineering Failure Analysis,2025,168. DOI: 10.1016/J.ENGFAILANAL.2024.109101.
[21]	DAO Fang,ZENG Yun,QIAN Jing. A novel denoising method of the hydro-turbine runner for fault signal based on WT-EEMD[J]. Measurement,2023,219. DOI:10.1016/J.MEASUREMENT.2023. 113306.

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