基于DnCNN的侵彻过载时频去噪方法
Time-Frequency Denoising Method for Penetration Overload Signals Based on Denoising Convolutional Neural Network(DnCNN)
郑宏亮 1贾森清 2郭宇朋 1薛颖杰 3韩晶 1赵河明 1石志刚4
作者信息
- 1. 中北大学 机电工程学院,太原 030051;山西省高端装备可靠性技术重点实验室,太原 030051
- 2. 西安机电信息技术研究所 机电动态控制重点实验室,西安 710065
- 3. 中北大学 机电工程学院,太原 030051
- 4. 湖北三江航天红林探控有限公司,湖北 孝感 432000
- 折叠
摘要
目的 提高从侵彻过载中准确估计刚体过载信号的能力.方法 提出一种基于前馈去噪卷积神经网络(DnCNN)的侵彻过载时频去噪方法,该方法首先应用短时傅里叶变换(STFT)提取侵彻过载信号的时频图像,使DnCNN能够充分利用时频图像信息,估计出刚体过载时频图像.最后,通过逆STFT将时频图像转换回时域,得到估计的刚体过载信号.结果 在 5-Fold交叉验证中,所提方法在测试集上的平均绝对误差(MAE)为0.968%,Pearson相关系数(r)为90.35%.与低通滤波、总体经验模态分解(EEMD)和小波变换方法相比,所提方法的平均 MAE 分别降低了 1.82%、1.00%、0.75%,平均相关系数 r 值分别提高了47.81%、17.48%、22.93%.结论 所提方法可以从侵彻过载中准确估计出刚体过载信号,在去噪能力上优于低通滤波、EEMD和小波变换方法,且在去噪过程中,无需调整参数,能够自动完成去噪任务.
Abstract
The work aims to enhance the ability to accurately estimate rigid body overload signals from the penetration overload signals.A time-frequency denoising method based on feedforward denoising convolutional neural network(DnCNN)was proposed.In this method,firstly the short-time Fourier transform(STFT)was applied to extract the time-frequency images of the penetration overload signal so that the DnCNN network could make full use of these images to effectively estimate the time-frequency images of the rigid-body overload.Finally,the time-frequency images were converted back to the time domain by inverse STFT to obtain the estimated rigid body overload signal.In the 5-Fold Cross-Validation,the proposed method had a mean absolute error(MAE)of 0.968%and a Pearson correlation coefficient(r)of 90.35%on the test set.Compared with low-pass filtering,ensemble empirical modal decomposition(EEMD)and wavelet transform methods,the proposed method performed better in denoising ability.Specifically,the average MAE of the proposed method was reduced by 1.82%,1.00%,and 0.75%,while the average correlation coefficient r-value was improved by 47.81%,17.48%,and 22.93%,respectively.The pro-posed method can accurately estimate the rigid body overload signal from the penetration overload and outperform low-pass fil-tering,EEMD and wavelet transform methods in denoising capability.In the denoising process,there is no need to adjust pa-rameters and the denoising task can be automatically completed.
关键词
硬目标侵彻/侵彻过载/前馈去噪卷积神经网络/信号去噪/时频分析/k-Fold交叉验证Key words
hard target penetration/penetration overload/denoising convolutional neural network/signal denoising/time-frequency analysis/k-Fold cross-validation引用本文复制引用
基金项目
山西省高端装备可靠性技术重点实验室研究基金(446110103)
出版年
2024
NETL
NSTL
万方数据