皮带机故障诊断中基于自适应小波阈值去噪法的应用  

Application of Adaptive Wavelet Threshold Denoising Method in Fault Diagnosis of Belt Conveyor

作　　者：薛忠新 呼少平 张艳军 韩培强 XUE Zhongxin;HU Shaoping;ZHANG Yanjun;HAN Peiqiang(Shaanxi Coal Group Shenmu Zhangjiamao Mining Co.,Ltd.,Shenmu Shaanxi 719399,China)

机构地区：[1]陕煤集团神木张家峁矿业有限公司,陕西神木719399

出　　处：《机械设计与研究》2023年第4期213-216,共4页Machine Design And Research

基　　金：国家自然科学基金资助项目(51775157)。

摘　　要：皮带机故障信号中包含大量运行信息,采集故障信号进行去噪处理有利于故障状态识别。为了进一步提高小波阈值去噪方法在强噪声背景下微弱故障信号提取能力,设计了一种基于改进自适应阈值的小波阈值去噪法的皮带机故障诊断方法。研究结果表明:进行小波阈值去噪时未进行阈值函数优化,在阈值选取缺乏灵活性。以自适应小波阈值去噪时,可以获得更平滑的阈值曲线,能够满足最优去噪性能。采用小波阈值去噪时并未达到理想去噪效果,存在明显噪声成分,并且获得了更大均方根误差;完成小波阈值改进后获得理想去噪效果,更准确保留有用信号,使噪声信号被充分去除,实现信噪比的明显提升。该研究可以拓宽到其它传动设备上,对后续的故障状态识别奠定理论基础。The fault signal of the belt conveyor contains a large amount of operating information.Collecting the fault signal for denoising is beneficial to fault state identification.In order to improve the ability of wavelet threshold denoising method to extract weak fault signals under strong noise background,a fault diagnosis method of belt conveyor based on improved adaptive threshold wavelet threshold denoising method is designed.The results show that the threshold function is not optimized in the wavelet threshold denoising method,and the selection of threshold is not flexible.When the adaptive wavelet threshold is used for denoising,a smoother threshold curve can be obtained,which can satisfy the optimal denoising performance.The wavelet threshold denoising method does not achieve the ideal denoising effect,there are obvious noise components,and a larger root mean square error is obtained.After the improvement of wavelet threshold,the ideal denoising effect is obtained,and useful signals are retained more accurately,so that the noise signal is fully removed,and the signal-to-noise ratio is significantly improved.This research can be extended to other transmission equipment and lay a theoretical foundation for subsequent fault state identification.

关 键 词：皮带机故障 故障信号 小波去噪 自适应阈值 特征提取 

分 类 号：TN912[电子电信—通信与信息系统]