Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method
The utilization of ultraviolet (UV) absorption spectroscopy for monitoring the concentration of specific decomposition gas components in gas-insulated switchgear (GIS) can provide a means to assess its insulation status. Nevertheless, UV optical modules currently deployed in the field are susceptibl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/22/12301 |
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| author | Zhaoyu Qin Zhaofan Wang Ruxing Wang |
| author_facet | Zhaoyu Qin Zhaofan Wang Ruxing Wang |
| author_sort | Zhaoyu Qin |
| collection | DOAJ |
| description | The utilization of ultraviolet (UV) absorption spectroscopy for monitoring the concentration of specific decomposition gas components in gas-insulated switchgear (GIS) can provide a means to assess its insulation status. Nevertheless, UV optical modules currently deployed in the field are susceptible to external interferences like ambient noise and equipment vibrations. Real-time spectral data acquisition often suffers from significant noise contamination, directly impinging on subsequent feature extraction and detection accuracy. This paper presents an optimized singular value decomposition (SVD) noise reduction method for mitigating noisy spectral signals. First, each singular value within the noisy signal is transformed into a component signal. Next, the highest frequency value in the signal serves as an indicator to characterize the signal. Finally, the primary frequency values are arranged based on the decreasing singular values of the original noisy signal. The singular value corresponding to the first primary frequency value surpassing a preset threshold is selected as the effective order for denoising. Random noise with varying intensities was intentionally introduced to the UV spectral signal of sulfur dioxide (SO<sub>2</sub>), followed by noise reduction procedures. It is shown that the improved SVD noise reduction algorithm proposed in this paper enhances the signal-to-noise ratio (SNR) by 18.02% and 16.86%, and reduces the root-mean-square error (RMSE) by 15.13% and 14.92%, respectively, compared with the singular value difference spectrum (SVDS) denoising method and wavelet transform denoising method under the condition of low SNR. Furthermore, there exists a linear relationship between the concentration of SO<sub>2</sub> samples and the eigenvalues of the UV spectra, demonstrating a higher linear goodness with a coefficient of 0.99735. The denoising method proposed in this paper does not require the manual setting of various types of parameters, and has a better ability to deal with the noise of UV spectral signals in engineering sites with complex environments. |
| first_indexed | 2024-03-09T17:03:47Z |
| format | Article |
| id | doaj.art-b43b06d5ac3740e68f2af1f33579f667 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:03:47Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b43b06d5ac3740e68f2af1f33579f6672023-11-24T14:27:06ZengMDPI AGApplied Sciences2076-34172023-11-0113221230110.3390/app132212301Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD MethodZhaoyu Qin0Zhaofan Wang1Ruxing Wang2Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, ChinaHubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, ChinaHubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, ChinaThe utilization of ultraviolet (UV) absorption spectroscopy for monitoring the concentration of specific decomposition gas components in gas-insulated switchgear (GIS) can provide a means to assess its insulation status. Nevertheless, UV optical modules currently deployed in the field are susceptible to external interferences like ambient noise and equipment vibrations. Real-time spectral data acquisition often suffers from significant noise contamination, directly impinging on subsequent feature extraction and detection accuracy. This paper presents an optimized singular value decomposition (SVD) noise reduction method for mitigating noisy spectral signals. First, each singular value within the noisy signal is transformed into a component signal. Next, the highest frequency value in the signal serves as an indicator to characterize the signal. Finally, the primary frequency values are arranged based on the decreasing singular values of the original noisy signal. The singular value corresponding to the first primary frequency value surpassing a preset threshold is selected as the effective order for denoising. Random noise with varying intensities was intentionally introduced to the UV spectral signal of sulfur dioxide (SO<sub>2</sub>), followed by noise reduction procedures. It is shown that the improved SVD noise reduction algorithm proposed in this paper enhances the signal-to-noise ratio (SNR) by 18.02% and 16.86%, and reduces the root-mean-square error (RMSE) by 15.13% and 14.92%, respectively, compared with the singular value difference spectrum (SVDS) denoising method and wavelet transform denoising method under the condition of low SNR. Furthermore, there exists a linear relationship between the concentration of SO<sub>2</sub> samples and the eigenvalues of the UV spectra, demonstrating a higher linear goodness with a coefficient of 0.99735. The denoising method proposed in this paper does not require the manual setting of various types of parameters, and has a better ability to deal with the noise of UV spectral signals in engineering sites with complex environments.https://www.mdpi.com/2076-3417/13/22/12301spectral denoisingsingular value decompositionfourier transformeffective ordersignal processing |
| spellingShingle | Zhaoyu Qin Zhaofan Wang Ruxing Wang Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method Applied Sciences spectral denoising singular value decomposition fourier transform effective order signal processing |
| title | Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method |
| title_full | Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method |
| title_fullStr | Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method |
| title_full_unstemmed | Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method |
| title_short | Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method |
| title_sort | research on an ultraviolet spectral denoising algorithm based on the improved svd method |
| topic | spectral denoising singular value decomposition fourier transform effective order signal processing |
| url | https://www.mdpi.com/2076-3417/13/22/12301 |
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