An Improved Spectral Subtraction Method for Eliminating Additive Noise in Condition Monitoring System Using Fiber Bragg Grating Sensors

Liu, Qi; Yu, Yongchao; Han, Boon Siew; Zhou, Wei

doi:10.3390/s24020443

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…Measuring the resonant wavelengths of fiber Bragg grating (FBG) sensors, finding their fingerprints or classifying FBGs itself in optical sensing systems require denoising the acquired FBG spectral peaks [1][2][3][4][5][6][7] Meanwhile, all important properties of FBG spectral peaks have to be preserved.…”

Section: Introductionmentioning

confidence: 99%

Impact of Reducing Statistically Small Population Sampling on 2 Threshold Detection in FBG Optical Sensing

Cibira,

Glesk,

Dubovan

et al. 2024

Preprint

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Statistical denoising techniques have been well studied, however many challenges still remain. This 9 article focuses on determining the least necessary number of significant samples in a statistical pop-10 ulation sampling, while minimizing the impact on the statistical detection of the fiber Bragg grating 11 (FBG) power spectra. This is achieved by using Bayesian decision making, small population statis-12 tics while excluding false detection. First, we investigate the impact of two-sided sliding window 13 technique applied near the cell under the test. The window size varies from 8 to 60 samples. The 14 considered maximum size of population sampling is connected with the number of wavelength 15 samples within the bandwidth of the symmetrical FBG power spectra peaks. Regarding the sym-16 metry of FBG power spectra peaks and the possible reduction of computational demands, we inves-17 tigate the impact of a one-sided sliding window. This enables to achieve less processing latency for 18 real time applications thus brings benefits to FBG-based fiber optical sensing. Both, two- and one-19 sided small population sampling techniques are then experimentally verified. Detection results are 20 analyzed in depth via calculating detection thresholds by using the small size window sliding along-21 side the waveband. From results achieved, it was found that the normality three-sigma rule does 22 not need to be followed while using the small population sampling. Experimental demonstrations 23 and analyses showed that the proposed concept of denoising and statistical threshold detection 24 based on the sliding window does not depend on the prior knowledge of probability distribution 25 functions of the FBG power spectra peaks level and background noise. We have also found that the 26 thresholds’ adaptability mainly depends on the statistical numerical characteristics of the above ad-27 ditive mixture of the FBG power spectra peaks signals and background noise.

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Section: Introductionmentioning

confidence: 99%

Impact of Reducing Statistically Small Population Sampling on 2 Threshold Detection in FBG Optical Sensing

Cibira,

Glesk,

Dubovan

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Measuring the resonant wavelengths of fiber Bragg grating (FBG) sensors, finding their fingerprints, or classifying FBGs themselves in optical sensing systems require denoising the acquired FBG spectral peaks [1][2][3][4][5][6][7]. Meanwhile, all important properties of FBG spectral peaks have to be preserved.…”

Section: Introductionmentioning

confidence: 99%

Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing

Cibira,

Glesk,

Dubovan

et al. 2024

Sensors

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Many techniques have been studied for recovering information from shared media such as optical fiber that carries different types of communication, sensing, and data streaming. This article focuses on a simple method for retrieving the targeted information with the least necessary number of significant samples when using statistical population sampling. Here, the focus is on the statistical denoising and detection of the fiber Bragg grating (FBG) power spectra. The impact of the two-sided and one-sided sliding window technique is investigated. The size of the window is varied up to one-half of the symmetrical FBG power spectra bandwidth. Both, two- and one-sided small population sampling techniques were experimentally investigated. We found that the shorter sliding window delivered less processing latency, which would benefit real-time applications. The calculated detection thresholds were used for in-depth analysis of the data we obtained. It was found that the normality three-sigma rule does not need to be followed when a small population sampling is used. Experimental demonstrations and analyses also showed that novel denoising and statistical threshold detection do not depend on prior knowledge of the probability distribution functions that describe the FBG power spectra peaks and background noise. We have demonstrated that the detection thresholds’ adaptability strongly depends on the mean and standard deviation values of the small population sampling.

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“…Для подавления помех предлагается использовать метод спектрального вычитания, широко применяющийся в цифровой обработке сигналов и многократно доказавший свою эффективность [4,5]. Данный подход является универсальным и эффективно использует вычислительные ресурсы [6].…”

Section: Introductionunclassified

Suppression of additive periodic low-frequency interference on eddy current defectograms

Bystrov,

Gladkov,

Kuzmin

2024

Model. anal. inf. sist.

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Traffic safety in railway transport requires regular inspection of the rail condition to identify and timely eliminate defects. Eddy current flaw detection is one of the popular methods of non-destructive rail testing. The data (defectograms) obtained from eddy current flaw detectors during rail testing are produced in large volume and therefore need in efficient automatic analysis. The analysis means the process of detecting on defectograms the presence of defective areas and identification of structural elements of the rail track, taking into account noise and possible interferences of various natures. The threshold noise level is found to isolate signals from defects and structural elements. Its value can be distorted by electromagnetic influences superimposed on the signals, which have pronounced low frequency and periodicity. This interferences raises the threshold noise level, complicating to detect useful signals. In this regard, there is a need to suppress the effects of the described type. Therefore, there is a need for described interferences reduction. In this paper spectral subtraction was used as a method for interference reduction on eddy current defectograms. The interference function was defined as the sum of the low-frequency harmonics of the Discrete Fourier Transform of the original signal. Then the interference-free signal can be found by subtracting of low-frequency range harmonics. The right boundary of this range was called the threshold harmonic frequency. It was found minimizing the distance of the signal's autocorrelation function and expected autocorrelation. For it two kinds were proposed: the noise autocorrelation and the reference autocorrelation. Both approaches allow to determine the threshold harmonic frequency so that periodic interferences will be best reduced. The based on the Gaussian noise autocorrelation method is somewhat universal for eddy current defectograms. Whereas the based on reference autocorrelation method depends on specific data and recording equipment. For the eddy current defectogram data under consideration, the most suitable threshold harmonic frequency was found. The described approaches to periodic low-frequency interference reduction, in addition to eddy current testing, can be successfully used for other areas.

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An Improved Spectral Subtraction Method for Eliminating Additive Noise in Condition Monitoring System Using Fiber Bragg Grating Sensors

Cited by 4 publications

References 44 publications

Impact of Reducing Statistically Small Population Sampling on 2 Threshold Detection in FBG Optical Sensing

Impact of Reducing Statistically Small Population Sampling on 2 Threshold Detection in FBG Optical Sensing

Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing

Suppression of additive periodic low-frequency interference on eddy current defectograms

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