基于小波降噪的振动传感器数据分析

计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 537-539.

基于小波降噪的振动传感器数据分析

张阳峰¹, 韦仕鸿¹, 邓娜娜², 王文瑞

中国水利水电第七工程局有限公司成都610000¹;
上海云统信息科技有限公司上海201210²;
中国科学院上海高等研究院上海201210³

出版日期:2019-06-14 发布日期:2019-07-02
通讯作者: 邓娜娜(1983-),女,硕士生,高级工程师,主要研究方向为物联网、智能检测与控制等,E-mail:dnn1023@126.com
作者简介:张阳峰(1983-),男,工程师,主要研究方向为工程项目管理;韦仕鸿(1977-),男,工程师,主要研究方向为水电工程技术管理;王文瑞(1988-),男,工程师,主要研究方向为智能检测与控制技术、物联网等。
基金资助:
本文受上海市经信委专项基金(201601060),国家自然科学基金(51507175)资助。

Vibration Sensor Data Analysis Based on Wavelet Denoising

ZHANG Yang-feng¹, WEI Shi-hong¹, DENG Na-na², WANG Wen-rui³

Sinohydro Bureau 7 Co.,Ltd.,Chengdu 610000,China¹;
Shanghai Unitoon Information Technology Co.,Ltd.,Shanghai 201210,China²;
Shanghai Advanced Research Institute,Chinese Academy of Sciences,Shanghai 201210,China³

Online:2019-06-14 Published:2019-07-02

摘要/Abstract

摘要： 针对矿山机械设备的振动数据在信号滤波和故障信号数据保存及提取方面存在的问题,提出了神经网络优化阈值的小波变换方法。采用MEMS三轴加速度传感器采集数字量,对其运算处理后转换成位移,再进行小波分解,对分解出的高频系数部分进行神经网络阈值优化调节,重构数据以达到降噪的效果,最终对滤波后的信号进行傅里叶变换,并根据幅频能量计算高频系数的占比。实验表明,基于神经网络调节阈值的小波变换方法能够在自适应学习后自动调节阈值,对振动传感器信号具有理想的滤波效果。优化重构后的信号比传统方法多滤除了15%以上的高频噪声能量,并能保留突变故障信息,为后期的故障诊断提供重要依据。

关键词: 神经网络, 数据处理, 小波变换, 阈值优化, 振动传感器

Abstract: Aiming at the problems of signal filtering and fault signal data preservation and extraction in vibration data of mining machinery and equipment,this paper proposed a wavelet transform method based on neural network optimization threshold.The MEMS triaxial accelerometer is used to sample the digital data,then converted into displacement by the processing,and then the wavelet decomposition is performed.The high-frequency coefficients are optimized and adjusted by neural network threshold,and the data are reconstructed to achieve the effect of noise reduction.Finally,Fourier transform of filtered signals is carried out,and the ratio of high-frequency coefficients is calculated according to amplitude frequency energy.Experimental results show that the wavelet transform method based on neural network adjusting threshold can automatically adjust the threshold after adaptive learning,and has ideal filtering effect on vibration sensor signal.The high frequency noise energy can be filtered more than 15% than the traditional threshold,and can retain abrupt fault information,which provides an important basis for later fault diagnosis.

Key words: Data processing, Neural network, Threshold optimization, Vibration sensor, Wavelet transform

中图分类号:

TM937

张阳峰, 韦仕鸿, 邓娜娜, 王文瑞. 基于小波降噪的振动传感器数据分析[J]. 计算机科学, 2019, 46(6A): 537-539. https://doi.org/

ZHANG Yang-feng, WEI Shi-hong, DENG Na-na, WANG Wen-rui. Vibration Sensor Data Analysis Based on Wavelet Denoising[J]. Computer Science, 2019, 46(6A): 537-539. https://doi.org/

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