Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring
In this paper, we studied the possibility of increasing the Brillouin frequency shift (BFS) detection accuracy in distributed fibre-optic sensors by the separate and joint use of different algorithms for finding the spectral maximum: Lorentzian curve fitting (LCF, including the Levenberg–Marquardt (...
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MDPI AG
2022-03-01
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2677 |
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| author | Nur Dalilla Nordin Fairuz Abdullah Mohd Saiful Dzulkefly Zan Ahmad Ashrif A Bakar Anton I. Krivosheev Fedor L. Barkov Yuri A. Konstantinov |
| author_facet | Nur Dalilla Nordin Fairuz Abdullah Mohd Saiful Dzulkefly Zan Ahmad Ashrif A Bakar Anton I. Krivosheev Fedor L. Barkov Yuri A. Konstantinov |
| author_sort | Nur Dalilla Nordin |
| collection | DOAJ |
| description | In this paper, we studied the possibility of increasing the Brillouin frequency shift (BFS) detection accuracy in distributed fibre-optic sensors by the separate and joint use of different algorithms for finding the spectral maximum: Lorentzian curve fitting (LCF, including the Levenberg–Marquardt (LM) method), the backward correlation technique (BWC) and a machine learning algorithm, the generalized linear model (GLM). The study was carried out on real spectra subjected to the subsequent addition of extreme digital noise. The precision and accuracy of the LM and BWC methods were studied by varying the signal-to-noise ratios (SNRs) and by incorporating the GLM method into the processing steps. It was found that the use of methods in sequence gives a gain in the accuracy of determining the sensor temperature from tenths to several degrees Celsius (or MHz in BFS scale), which is manifested for signal-to-noise ratios within 0 to 20 dB. We have found out that the double processing (BWC + GLM) is more effective for positive SNR values (in dB): it gives a gain in BFS measurement precision near 0.4 °C (428 kHz or 9.3 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">μ</mi><mi mathvariant="sans-serif">ε</mi></mrow></semantics></math></inline-formula>); for BWC + GLM, the difference of precisions between single and double processing for SNRs below 2.6 dB is about 1.5 °C (1.6 MHz or 35 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">μ</mi><mi mathvariant="sans-serif">ε</mi></mrow></semantics></math></inline-formula>). In this case, double processing is more effective for all SNRs. The described technique’s potential application in structural health monitoring (SHM) of concrete objects and different areas in metrology and sensing were also discussed. |
| first_indexed | 2024-03-09T11:25:29Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:25:29Z |
| publishDate | 2022-03-01 |
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| spelling | doaj.art-98351c7e628a4547ad0006e3332b46b82023-12-01T00:03:20ZengMDPI AGSensors1424-82202022-03-01227267710.3390/s22072677Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health MonitoringNur Dalilla Nordin0Fairuz Abdullah1Mohd Saiful Dzulkefly Zan2Ahmad Ashrif A Bakar3Anton I. Krivosheev4Fedor L. Barkov5Yuri A. Konstantinov6School of Engineering and Physical Sciences, Heriot-Watt University Malaysia, Putrajaya 62200, MalaysiaInstitute of Power Engineering, Universiti Tenaga Nasional, Kajang 43000, MalaysiaDepartment of Electrical, Electronic & Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, MalaysiaDepartment of Electrical, Electronic & Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, MalaysiaPerm Federal Research Center of the Ural Branch of the Russian Academy of Sciences (PFRC UB RAS), 13a, Lenin Street, 614990 Perm, RussiaPerm Federal Research Center of the Ural Branch of the Russian Academy of Sciences (PFRC UB RAS), 13a, Lenin Street, 614990 Perm, RussiaPerm Federal Research Center of the Ural Branch of the Russian Academy of Sciences (PFRC UB RAS), 13a, Lenin Street, 614990 Perm, RussiaIn this paper, we studied the possibility of increasing the Brillouin frequency shift (BFS) detection accuracy in distributed fibre-optic sensors by the separate and joint use of different algorithms for finding the spectral maximum: Lorentzian curve fitting (LCF, including the Levenberg–Marquardt (LM) method), the backward correlation technique (BWC) and a machine learning algorithm, the generalized linear model (GLM). The study was carried out on real spectra subjected to the subsequent addition of extreme digital noise. The precision and accuracy of the LM and BWC methods were studied by varying the signal-to-noise ratios (SNRs) and by incorporating the GLM method into the processing steps. It was found that the use of methods in sequence gives a gain in the accuracy of determining the sensor temperature from tenths to several degrees Celsius (or MHz in BFS scale), which is manifested for signal-to-noise ratios within 0 to 20 dB. We have found out that the double processing (BWC + GLM) is more effective for positive SNR values (in dB): it gives a gain in BFS measurement precision near 0.4 °C (428 kHz or 9.3 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">μ</mi><mi mathvariant="sans-serif">ε</mi></mrow></semantics></math></inline-formula>); for BWC + GLM, the difference of precisions between single and double processing for SNRs below 2.6 dB is about 1.5 °C (1.6 MHz or 35 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">μ</mi><mi mathvariant="sans-serif">ε</mi></mrow></semantics></math></inline-formula>). In this case, double processing is more effective for all SNRs. The described technique’s potential application in structural health monitoring (SHM) of concrete objects and different areas in metrology and sensing were also discussed.https://www.mdpi.com/1424-8220/22/7/2677Brillouin scatteringdistributed fibre-optic sensorsdata processingmachine learningBFS extractionBOTDA |
| spellingShingle | Nur Dalilla Nordin Fairuz Abdullah Mohd Saiful Dzulkefly Zan Ahmad Ashrif A Bakar Anton I. Krivosheev Fedor L. Barkov Yuri A. Konstantinov Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring Sensors Brillouin scattering distributed fibre-optic sensors data processing machine learning BFS extraction BOTDA |
| title | Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring |
| title_full | Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring |
| title_fullStr | Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring |
| title_full_unstemmed | Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring |
| title_short | Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring |
| title_sort | improving prediction accuracy and extraction precision of frequency shift from low snr brillouin gain spectra in distributed structural health monitoring |
| topic | Brillouin scattering distributed fibre-optic sensors data processing machine learning BFS extraction BOTDA |
| url | https://www.mdpi.com/1424-8220/22/7/2677 |
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