Denoising Method of dbN Wavelet Threshold for Die Repair Hammer Force Signals
In order to solve the problem that the noise of the hammer power signal in mold repair is large, the dbN wavelet (abbreviation of Daubechies, N is the wavelet order) is used to reconstruct the hammer force signal according to the obtained low frequency coefficient. The low-frequency coefficients of...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2019-08-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1714 |
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| Summary: | In order to solve the problem that the noise of the hammer power signal in mold repair is large, the dbN wavelet (abbreviation of Daubechies, N is the wavelet order) is used to reconstruct the hammer force signal according to the obtained low frequency coefficient. The low-frequency coefficients of the signal are reserved and the high-frequency coefficients are discarded Ultimately, the signal-to-noise ratio after noise reduction is 10.0778and the mean square error is 0.6633, the noise reduction of hammer power signal in mold repair is initially achieved. Simultaneously, the thresholding is done on the basis of dbN wavelet decomposition. Experimental results show that the SNR is significantly increased after threshold treatment, up to 39.85dB; the RMSE is significantly reduced and the minimum value is 0.4498. Comprehensive macro-waveform characteristics indicating that the two methods can achieve noise filtering of hammering force signal. Besides, soft threshold methods can also be a greater degree of reduction of the original signal at the mutation point for the details characteristics, to avoid the signal distortion and ensure that the subsequent calculation accuracy of hammering force signal in mold repair. |
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| ISSN: | 1007-2683 |