Reduction of the blind spot in the time-frequency domain reflectometry

Kwak, Ki Seok; Doo, Seung Ho; Lee, Chun Ku; Park, Jin Bae; Yoon, Tae Sung

doi:10.1587/elex.5.265

Cited by 11 publications

(14 citation statements)

References 6 publications

(7 reference statements)

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“…Detection methods based on the electromagnetic wave, called reflectometry methods, are useful for fault localization and monitoring of the health of a cable with insulator. Reflectometry methods can be categorized into time domain reflectometry (TDR), frequency domain reflectometry (FDR), and time-frequency domain reflectometry (TFDR) [2][3][4][5][6][7] depending on the incident signal type. TDR and FDR use a step pulse and sinusoidal pulse defined in the time domain and frequency domain, respectively.…”

Section: Introductionmentioning

confidence: 99%

Compensation for Group Velocity of Polychromatic Wave Measurement in Dispersive Medium

Chang

Moon

2017

Applied Sciences

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Abstract:The estimation of instantaneous frequency (IF) method is introduced to compensate for the group velocity of electromagnetic wave in dispersive medium. The location of the reflected signal can be obtained by using the time-frequency cross correlation (TFCC), following which it is used to extract the transmitted signal from the total signal acquired. The signal propagated in the dispersive medium is attenuated and distorted by the attenuation characteristics, which depend on the frequency of the medium. By using the IF curve calculated for the transmitted signal, the changed center frequency and time terms can be obtained. The obtained terms are used to compensate for the group velocity error induced by signal distortion and attenuation. Through experiments and simulation, the accuracy of the proposed method is 2% higher than that of the conventional method when the signal propagates over a long distance.

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Section: Introductionmentioning

confidence: 99%

Compensation for Group Velocity of Polychromatic Wave Measurement in Dispersive Medium

Chang

Moon

2017

Applied Sciences

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“…The reflectometry method, which is nondestructive, is mainly used for diagnosing problem with cables installed in automobiles because they are vulnerable to shock without insulation. Reflectometry methods can be divided into time domain reflectometry (TDR), frequency domain reflectometry (FDR), and TFDR [1,2,3,4] depending on the reference signal type. TDR and FDR have been widely used for cable diagnostics, because they are easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Air gap measurement in cable of automotive electronics based on electromagnetic wave

Chang

Park

2017

IEICE Electron. Express

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Time-frequency domain reflectometry (TFDR) based on electromagnetic theory is first introduced to measure air gap in cables. By using the relationship between propagation velocity and permittivity in electromagnetic theory, the new relationship between the air gap and the propagation velocity in cables can be obtained. The proposed method adopts a chirp signal as an incident signal and uses a normalized cross-correlation function for deriving propagation velocity. To reduce signal distortion caused by cable attenuation characteristics, a modified overcomplete wavelet transform is applied. The air gap volume, which can be measured by the proposed method, can be used as an indicator of poor contact between the cable and connector. The performance of the proposed method is verified through experiments.

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“…Time-frequency domain reflectometry (TFDR), which is used to detect and localise a cable fault, is a well-known method and the method has been applied to various applications such as coaxial cable [1][2][3], high voltage power cable [4][5][6], electric power cable in nuclear power plant and ship power system [7][8][9]. Among the reflectometries, time domain reflectometry (TDR) and frequency domain reflectometry (FDR), which are heavily affected by the noise, have a limitation for the resolution and the accuracy of the fault detection because of the rise/ fall time and the frequency sweep bandwidth, respectively [1].…”

Section: Introductionmentioning

confidence: 99%

Blind spot reduction in wavelet transform‐based time–frequency domain reflectometry using Gaussian chirp as mother wavelet

Lee¹,

Park²,

Choi³

2014

IET signal process.

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In this study, the authors propose a blind spot reduction method in wavelet transform-based time-frequency domain reflectometry (WTFDR) by using the Gaussian chirp as the mother wavelet. The blind spot is one of the intrinsic weak points in reflectometry and it means the overlapping ranges of the reference and the reflected signals when the fault is generated at a close distance. Owing to the blind spot, it is difficult to localise the close range fault. Thus, many researchers study the blind spot which is generated in various cables such as electric cable in flight, network cable and power cable. In this study, two methods are used to reduce the blind spot. Firstly, by using the linearity of a complex wavelet transform, the overlapped reference signal at the measured signal is separated and the blind spot is reduced by obtaining the difference of the moduli of the wavelet coefficients for the reference and the reflected signals. Secondly, by using the Gaussian chirp as the mother wavelet, which is designed by considering the characteristics of the cable, the wavelet analysis and the resolution of the WTFDR are improved. Finally, the computer simulations and the real experiments are performed to confirm the effectiveness and the accuracy of the proposed method.

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Reduction of the blind spot in the time-frequency domain reflectometry

Cited by 11 publications

References 6 publications

Compensation for Group Velocity of Polychromatic Wave Measurement in Dispersive Medium

Compensation for Group Velocity of Polychromatic Wave Measurement in Dispersive Medium

Air gap measurement in cable of automotive electronics based on electromagnetic wave

Blind spot reduction in wavelet transform‐based time–frequency domain reflectometry using Gaussian chirp as mother wavelet

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