An Accurate Reflection Evaluation Method for Cable Defects Based on Generalized S Transform Reconstruction and Compensation

Time-frequency domain reflectometry has proven to be an effective and sensitive method for locating cable defects. However, the traditional Wigner-Ville distribution used for time-frequency analysis can introduce cross-term interference, which can negatively impact defect detection. To overcome this limitation, this paper proposes a generalized S transform-based (GST) method, which can avoid cross-term interference and enhance time-frequency features by adjusting the parameters of GST. In addition, noise, signal attenuation, and refraction can affect the amplitude of the reflected signal from the defect, thereby influencing the severity assessment of the defect. To address this, a signal reflection evaluation method is proposed, which combines time-frequency filtering of the real part of GST and the propagation compensation. The method filters noise both within and outside the signal frequency range by exploiting the difference between the signal and noise in the FFT spectrum of the real part of GST. Next, the location curve and reconstructed signal are derived from the filtered GST. Finally, the reconstructed signal is compensated for attenuation and refraction using propagation coefficients extracted from the cable’s impedance spectrum, allowing the processed defect signal amplitude to accurately represent the defect’s reflection. The proposed method is validated through simulations and experiments, with the reflection evaluation errors all remaining below 6%.

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Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3582315
Citations
2
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
2.48

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article

An Accurate Reflection Evaluation Method for Cable Defects Based on Generalized S Transform Reconstruction and Compensation

Haibao Mu, Xianjun Shao, Xingyu Zou, Guanjun Zhang et al.
2 citations
IEEE Transactions on Instrumentation and Measurement
Industrial Vision Systems and Defect Detection
2.48
article

An Accurate Reflection Evaluation Method for Cable Defects Based on Generalized S Transform Reconstruction and Compensation

Haibao Mu, Xianjun Shao, Xingyu Zou, Guanjun Zhang, Kaixuan Fan, Junping Cao, Ci Song, Ziqian Cheng, Renjie Wang
article en
2 citations

Abstract

Time-frequency domain reflectometry has proven to be an effective and sensitive method for locating cable defects. However, the traditional Wigner-Ville distribution used for time-frequency analysis can introduce cross-term interference, which can negatively impact defect detection. To overcome this limitation, this paper proposes a generalized S transform-based (GST) method, which can avoid cross-term interference and enhance time-frequency features by adjusting the parameters of GST. In addition, noise, signal attenuation, and refraction can affect the amplitude of the reflected signal from the defect, thereby influencing the severity assessment of the defect. To address this, a signal reflection evaluation method is proposed, which combines time-frequency filtering of the real part of GST and the propagation compensation. The method filters noise both within and outside the signal frequency range by exploiting the difference between the signal and noise in the FFT spectrum of the real part of GST. Next, the location curve and reconstructed signal are derived from the filtered GST. Finally, the reconstructed signal is compensated for attenuation and refraction using propagation coefficients extracted from the cable’s impedance spectrum, allowing the processed defect signal amplitude to accurately represent the defect’s reflection. The proposed method is validated through simulations and experiments, with the reflection evaluation errors all remaining below 6%.

IEEE Transactions on Instrumentation and MeasurementVol. 74
Shanghai Electric (China) (CN), State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University (CN)
China Scholarship Council
Openalex Percentile: Top 10%
Industrial Vision Systems and Defect Detection
2.48
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