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| Time-Frequency Denoising Method for Penetration Overload Signals Based on Denoising Convolutional Neural Network (DnCNN) |
| Received:April 25, 2024 Revised:May 27, 2024 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/issn.1672-9242.2024.08.003 |
| KeyWord:hard target penetration penetration overload denoising convolutional neural network signal denoising time-frequency analysis k-Fold cross-validation |
| Author | Institution |
| ZHENG Hongliang |
School of Mechanical and Electrical Engineering, North University of China, Taiyuan , China;Shanxi Key Laboratory of High-end Equipment Reliability Technology, Taiyuan , China |
| JIA Senqing |
Science and Technology on Electromechanical Dynamic Control Laboratory, Xi'an Institute of Electromechanical Information Technology, Xi'an , China |
| GUO Yupeng |
School of Mechanical and Electrical Engineering, North University of China, Taiyuan , China;Shanxi Key Laboratory of High-end Equipment Reliability Technology, Taiyuan , China |
| XUE Yingjie |
School of Mechanical and Electrical Engineering, North University of China, Taiyuan , China |
| HAN Jing |
School of Mechanical and Electrical Engineering, North University of China, Taiyuan , China;Shanxi Key Laboratory of High-end Equipment Reliability Technology, Taiyuan , China |
| ZHAO Heming |
School of Mechanical and Electrical Engineering, North University of China, Taiyuan , China;Shanxi Key Laboratory of High-end Equipment Reliability Technology, Taiyuan , China |
| SHI Zhigang |
Hubei Space Sanjiang Honglin Detection and Control Co., Ltd., Hubei Xiaogan , China |
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| 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 proposed method can accurately estimate the rigid body overload signal from the penetration overload and outperform low-pass filtering, EEMD and wavelet transform methods in denoising capability. In the denoising process, there is no need to adjust parameters and the denoising task can be automatically completed. |
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