Short-Time Fourier Transform Based on Stimulated Brillouin Scattering

Zuo, Pengcheng; Ma, Dong; Chen, Yang

doi:10.1109/jlt.2022.3174552

Cited by 18 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Frequency domain analysis is one of the common methods to extract the characteristics of flow noise signals in natural gas pipelines. Zuo, Ma & Chen (2022) can extract characteristic information such as frequency, power spectral density and so on by transforming the time domain signal into frequency domain signal through Fourier transform. The research shows that frequency domain analysis can effectively distinguish signals from different noise sources, but the processing effect of complex background noise is limited.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature extraction and pattern recognition of gas pipeline flow noise signals in a strong noisy background

Liu,

Lu,

Wen

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

The purpose of this study is to put forward a feature extraction and pattern recognition method for the flow noise signal of natural gas pipelines in view of the complex situation brought by the rapid development and expansion of urban natural gas infrastructure in China, especially in the case that there are active and abandoned pipelines, metal and nonmetal pipelines, and natural gas, water and power pipelines coexist in the underground of the city. Because the underground situation is unknown, gas leakage incidents caused by natural gas pipeline rupture occur from time to time, posing a threat to personal safety. Therefore, the motivation of this study is to provide a feasible method to accelerate the aging, renewal and transformation of urban natural gas pipelines to ensure the safe operation of urban natural gas pipeline network and promote the high-quality development of urban economy. Through the combination of experimental test and numerical simulation, this study establishes a database of urban natural gas pipeline flow noise signals, and uses principal component analysis (PCA) to extract the characteristics of flow noise signals, and develops a mathematical model for feature extraction. Then, a classification and recognition model based on backpropagation neural network (BPNN) is constructed, which realizes the detection and recognition of convective noise signals. The research results show that the theoretical method based on acoustic feature analysis provides guidance for the orderly and safe construction of urban natural gas pipeline network and ensures its safe operation. The research conclusion shows that through the simulation analysis of 75 groups of gas pipeline flow noise under different working conditions. Combined with the experimental verification of ground flow noise signals, the feature extraction and pattern recognition method proposed in this study has a recognition accuracy of up to 97% under strong noise background, which confirms the accuracy of numerical simulation and provides theoretical basis and technical support for the detection and recognition of urban gas pipeline flow noise.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Feature extraction and pattern recognition of gas pipeline flow noise signals in a strong noisy background

Liu,

Lu,

Wen

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…In this work, time-frequency transform systems reported recently by us, including STFT and wavelet-like transform, are introduced [7][8][9]. These systems avoid the use of dispersions and are implemented by adding a time dimension to the filter-and frequency-to-time mapping (FTTM)-based frequency measurement system.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2. Principle of the photonic-assisted analog STFT and wavelet-like transformThe system is first configured to implement STFT by using a frequency-sweep optical signal with a linearly varying time-frequency characteristic, and the sweep period and sweep bandwidth of the frequency-sweep optical signal are set to 1 μs and 4 GHz[7]. Here, the narrowband optical filter is implemented by using the gain spectrum excited by the stimulated Brillouin scattering (SBS), which has a natural 3-dB bandwidth of around 20 MHz.…”

mentioning

confidence: 99%