Method to compensate dispersion effect applied to time domain reflectometry

Sommervogel, Laurent; Sahmarany, Lola El; Incarbone, Luca

doi:10.1049/el.2013.1042

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…We note that the post-processing improves the fault location, and the relative error is equal to 0%. Now, let's take the same example, with 2000 m instead of 500 m. If we look at the location of the peak maximum of the curve (Figure 5), we find 2177 m for the reflectogram without dispersion Furthermore, we make a comparison with the dynamic cross-correlation method proposed by [6]. For the same cable model (2000 m length), we find 2004 m (relative error 0.2%) with the new approach and 2073 m (relative error 3.7%) with the dynamic cross-correlation method.…”

Section: Simulation Resultsmentioning

confidence: 98%

Method to Improve Fault Location Accuracy Against Cables Dispersion Effect

Osman¹,

Sallem²,

Sommervogel³

et al. 2019

PIER Letters

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Section: Simulation Resultsmentioning

confidence: 98%

Method to Improve Fault Location Accuracy Against Cables Dispersion Effect

Osman¹,

Sallem²,

Sommervogel³

et al. 2019

PIER Letters

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“…In order to overcome the drawbacks of the TDR method, the chirp signal that has characteristics in both the time and frequency domains is used to obtain robustness under the noise environment [10][11][12][13][14][15][16]. The TFDR adopted a linear chirp signal.…”

Section: Multiple Resolution Chirp Reflectometry For Fault Localizati...mentioning

confidence: 99%

Multiple resolution chirp reflectometry for fault localization and diagnosis in a high voltage cable in automotive electronics

Chang

Lee

Shin³

et al. 2016

Meas. Sci. Technol.

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A multiple chirp reflectometry system with a fault estimation process is proposed to obtain multiple resolution and to measure the degree of fault in a target cable. A multiple resolution algorithm has the ability to localize faults, regardless of fault location. The time delay information, which is derived from the normalized cross-correlation between the incident signal and bandpass filtered reflected signals, is converted to a fault location and cable length. The in-phase and quadrature components are obtained by lowpass filtering of the mixed signal of the incident signal and the reflected signal. Based on in-phase and quadrature components, the reflection coefficient is estimated by the proposed fault estimation process including the mixing and filtering procedure. Also, the measurement uncertainty for this experiment is analyzed according to the Guide to the Expression of Uncertainty in Measurement. To verify the performance of the proposed method, we conduct comparative experiments to detect and measure faults under different conditions. Considering the installation environment of the high voltage cable used in an actual vehicle, target cable length and fault position are designed. To simulate the degree of fault, the variety of termination impedance (10 , 30 , 50 , and 1 ) are used and estimated by the proposed method in this experiment. The proposed method demonstrates advantages in that it has multiple resolution to overcome the blind spot problem, and can assess the state of the fault.

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“…Over the last decade, reflectometry-based methods—such as time domain reflectometry (TDR), frequency domain reflectometry (FDR), and (joint) time–frequency domain reflectometry (TFDR)—have been introduced as effective methods for cable diagnostics [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. TDR uses an impulse signal as an incident signal and analyzes the reflected signal to diagnose and locate faults in the time domain.…”

Section: Introductionmentioning

confidence: 99%

Online and Offline Diagnosis of Motor Power Cables Based on 1D CNN and Periodic Burst Signal Injection

Kim

Jeong

Lee

et al. 2021

Sensors

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We introduce a new approach for online and offline soft fault diagnosis in motor power cables, utilizing periodic burst injection and nonintrusive capacitive coupling. We focus on diagnosing soft faults because local cable modifications or soft faults that occur without any indication while the cable is still operational can eventually develop into hard faults; furthermore, advance diagnosis of soft faults is more beneficial than the later diagnosis of hard faults, with respect to preventing catastrophic production stoppages. Both online and offline diagnoses with on-site diagnostic ability are needed because the equipment in the automated lines operates for 24 h per day, except during scheduled maintenance. A 1D CNN model was utilized to learn high-level features. The advantages of the proposed method are that (1) it is suitable for wiring harness cables in automated factories, where the installed cables are extremely short; (2) it can be simply and identically applied for both online and offline diagnoses and to a variety of cable types; and (3) the diagnosis model can be directly established from the raw signal, without manual feature extraction and prior domain knowledge. Experiments conducted with various fault scenarios demonstrate that this method can be applied to practical cable faults.

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Method to compensate dispersion effect applied to time domain reflectometry

Cited by 9 publications

References 4 publications

Method to Improve Fault Location Accuracy Against Cables Dispersion Effect

Method to Improve Fault Location Accuracy Against Cables Dispersion Effect

Multiple resolution chirp reflectometry for fault localization and diagnosis in a high voltage cable in automotive electronics

Online and Offline Diagnosis of Motor Power Cables Based on 1D CNN and Periodic Burst Signal Injection

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