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Based on OMTDR technology, the first embedded Smart Connector (SmartCo) which could locate very small soft defects resulting from partial degradation of cables is introduced in this paper. To do so, the SmartCo injects the generated OMTDR signal at an extremity of the cable and then listens to the echoes created at each discontinuity of the cable c...
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... proposed approach, described in figure 1, includes several steps. After reflectograms construction, a difference between healthy and faulty cable reflectograms is performed to eliminate inhomgeneities related to cable manufac- turing, installation, etc. ...
Citations
... • A fusion algorithm [44] of several post processing methods (Signature Magnification by Selective Windowing (SMSW) [23], subtractive correlation method, and the method based on the integral of a reflectogram [45]) is applied to each realization. is calculated for each fault case, at a specific SNR. ...
During operation, cables may be subject to hard faults (open circuit, short circuit) or soft faults (isolation damage, pinching, etc.) due to misuse, environmental conditions, or aging. Even though several electric and non-electric wire diagnosis methods have been studied and developed throughout the last few decades, reflectometry-based techniques have provided effective results with hard faults. However, they have shown to be less effective for soft faults. Indeed, soft faults are characterized by a small impedance variation, resulting in a low amplitude signature in the reflectograms. Accordingly, the detection of these faults depends strongly on the test signal bandwidth. Although the increase of the maximal frequency of the test signal enhances the soft fault’s ”spatial” resolution, the performance is limited by signal attenuation and dispersion. This study proposes a method to select the best maximal frequency for soft fault detection. It is based on a combination of reflectometry with Principal Component Analysis (PCA), and the analysis of the Squared Prediction Error (SPE). Experimental validation is carried out, and performance analysis in the presence of noise is investigated. The results for shielding damage show that when the soft fault is near the injection point, the detection probability equals to one even for SNR values as low as 0 dB. As the fault position approaches the end of the cable, the performance is still acceptable, but for lower fault severities, the detection is almost impossible. The results also show that the selected frequency depends on the fault severity, the fault position, and the noise level.
... In [13], a fusion approach of several post-processing results is proposed where a probabilistic model is developed and used to detect the soft fault. The proposed approach described in Figure 2.3 includes several steps. ...
... Chapter -Soft Fault Diagnosis in Multi-branched Network Typologies: Methods and Limitations Table . -Summary table of The detailed mathematical calculations are found in [13]. The result of this method for a 30m shielded twisted pair TWINLINK 50 FA with a shield fault of −8mm long and 3mm wide present at 10.9m from the injection point, shows the efficiency of the developed demonstrator to detect and locate the soft fault with high accuracy (1%). ...
... • A fusion algorithm [13] introduced in chapter 2 section 2.3.1 of several post processing methods (Signature Magnification by Selective Windowing (SMSW) [29], subtractive correlation method [97], and the method based on the integral of a reflectogram [136]) is applied to each realization. P d is calculated for each fault case, at a specific SNR. ...
Electrical cables are used in all sectors to transfer energy or information. During operation, the cables may be subject to hard faults (open circuit, short circuit) or soft faults (isolation damage, pinching, etc.) due to misuse, environmental conditions, or aging. These faults must be detected at their earliest stage to avoid interruption of the function or more serious consequences. Even though several electric and non-electric wire diagnosis methods have been studied and developed throughout the last few decades, reflectometry-based techniques have provided effective results with hard faults. However, they have been shown to be less reliable whenever soft faults are addressed.Indeed, soft faults are characterized by a small impedance variation, resulting in a low amplitude signature on the corresponding reflectograms. Accordingly, the detection of these faults depends strongly on the test signal configuration, such as its bandwidth. Although the increase of the maximal frequency of the test signal enhances the soft fault's ''spatial'' resolution, its performance is limited by signal attenuation and dispersion. Moreover, although reflectometry offers good results in point-to-point topology networks, it suffers from ambiguity related to fault location in more complex wired networks (Multi-branched). As a solution, distributed reflectometry method, where sensors are implemented in the extremities of the network under test, is used. However, several issues are enforced, from the computing complexities and sensors fusion problems to the energy consumption.In this context, this Ph.D. dissertation proposes to develop two approaches: the first selects the best maximal frequency for soft fault detection, and the second selects the most relevant sensors to monitor and diagnose those faults in multi-branched wired networks. The proposed solution is based on a combination between reflectometry and Principal Component Analysis (PCA). The PCA model coupled with statistical analysis based on Hotelling’s T² and Squared Prediction Error (SPE) is used to detect the soft faults and select the required parameters. Experimental validation is carried out, and performance analysis in the presence of noise is investigated.
... La réflectométrie OMTDR (Orthogonal Multi-tone Time Domain Reflectometry) [5], [65] est une méthode de réflectométrie multiporteuses dans le domaine temporel. La génération du signal de test OMTDR est basée sur le principe du multiplexage par répartition en fréquences orthogonales (OFDM : Orthogonal Frequency Division Multiplexing). ...
Les recherches menées dans cette thèse portent sur le diagnostic de réseaux filaires complexes à l’aide de la réflectométrie distribuée. L’objectif est de développer de nouvelles technologies de diagnostic en ligne, distribuées des réseaux complexes permettant la fusion de données ainsi que la communication entre les réflectomètres pour détecter, localiser et caractériser les défauts électriques (francs et non francs). Cette collaboration entre les réflectomètres permet de résoudre le problème d’ambiguïté de localisation des défauts et d’améliorer la qualité du diagnostic. La première contribution concerne la proposition d’une méthode basée sur la théorie des graphes permettant la combinaison de données entre les réflectomètres distribués afin de faciliter la localisation d’un défaut. L’amplitude du signal réfléchi est ensuite utilisée pour identifier le type du défaut et estimer son impédance. Cette estimation est basée sur la régénération du signal en compensant la dégradation subie par le signal de diagnostic au cours de sa propagation à travers le réseau. La deuxième contribution permet la fusion des données de réflectomètres distribués dans des réseaux complexes affectés par de multiples défauts. Pour atteindre cet objectif, deux méthodes ont été proposées et développées : la première est basée sur les algorithmes génétiques (AG) et la deuxième est basée sur les réseaux de neurones (RN). Ces outils combinés avec la réflectométrie distribuée permettent la détection automatique, la localisation et la caractérisation de plusieurs défauts dans différents types et topologies des réseaux filaires. La troisième contribution propose d’intégrer la communication entre les réflectomètres via le signal de diagnostic porteur d’informations. Elle utilise adéquatement les phases du signal multiporteuses MCTDR pour transmettre des données. Cette communication assure l’échange d’informations utiles entre les réflectomètres sur l’état des câbles, permettant ainsi la fusion de données et la localisation des défauts sans ambiguïtés. Les problèmes d’interférence entre les réflectomètres sont également abordés lorsqu’ils injectent simultanément leurs signaux de test dans le réseau. Ces travaux de thèse ont montré l’efficacité des méthodes proposées pour améliorer les performances des systèmes de diagnostic filaire actuels en termes de diagnostic de certains défauts encore difficiles à détecter aujourd’hui, et d’assurer la sécurité de fonctionnement des systèmes électriques.
... Various works showed that the increase of the dielectric losses is directly linked with the degradation state of the cable [3][4][5][6][7]. On the other hand, the TDR technique is used to identify and localize defects in cables detecting discontinuities in the electrical impedance that occur due to inhomogeneities or local defects along the cable length [2,8,9,10,11]. ...
... A secondary test through TDR setup, permits the evaluation and identification of local aging defects and cable weak points. It is worth commenting that, anyway, global aging can be evaluated also through TDR with a different measurement protocol [9,10,11,12]. ...
In this paper the aging through high temperature of 10-meter long coaxial cables and its change in electrical properties have been investigated through non-destructive electrical techniques i.e. dielectric spectroscopy and time domain reflectometry. Both techniques allow changes of electrical properties to be revealed with aging, however, the coupling of these two techniques permits an effective cable aging assessment allowing also the recognition of local defects. Indeed, it has been demonstrated that dielectric spectroscopy is more sensitive when the cable is globally aged, while time domain reflectometry, in addition to a global investigation, can also single out aging occurring in limited portion of cable insulation (local aging).
... As a solution, further development is needed to make the reflectometry method sensitive enough to detect and locate soft faults. In this context, several post-processing methods have been proposed in [3][4][5]. A Self-Adaptive Correlation Method (SACM) where the gain is automatically adjusted depending on the fault signature is proposed in [3]. ...
... A Signature Magnification by Selective Windowing (SMSW) method is proposed in [4] to select the critical zone based on a predetermined window. In [5], a fusion approach of several post-processing results is proposed where a probabilistic model is developed and used to detect the soft fault. Although interesting methods have been proposed to enhance soft fault diagnosis, they are prone to test signal attenuation and dispersion phenomena. ...
p>Power and signaling infrastructure comprise of electrical installations, electronic systems and cables. A damage to these cables will be catastrophic or at the least significant downtime to the system. Hence, monitoring these cables in real-time not only improves the efficiency of the system but can also avoid fatal accidents. In this work, we develop a non-invasive composite diagnostic framework to identify cable damages such as insulation cuts. The framework can detect, classify, and locate faults. While the solution is general enough, we consider two use cases: (a) railway cables used in signalling applications and(b) symmetrical four-core cables used in residential buildings. In order to characterize the faults, we use three non-invasive sensors: (a) SNR sensor, (b) S-parameter, and (c) Correlation peak sensor. We use a single programmable hardware to implement each of these sensors. These sensors monitor a parameter change on the cable in real-time. The experimental insights gained are used to construct an a priori Bayesian network depicting the non-deterministic relationship between an effect and its causes. This uncertainty is due to inhibitors such as sensor calibration issues and coupling mismatch between sensor transceivers suitably handled through a Noisy-OR function. The results of Bayesian inference with belief propagation provides up to 97% match with the ground truth state. Regarding the cable fault, our experimental results show a best-case detection and localization accuracy of 98% & 97.2% respectively.</p
p>Power and signaling infrastructure comprise of electrical installations, electronic systems and cables. A damage to these cables will be catastrophic or at the least significant downtime to the system. Hence, monitoring these cables in real-time not only improves the efficiency of the system but can also avoid fatal accidents. In this work, we develop a non-invasive composite diagnostic framework to identify cable damages such as insulation cuts. The framework can detect, classify, and locate faults. While the solution is general enough, we consider two use cases: (a) railway cables used in signalling applications and(b) symmetrical four-core cables used in residential buildings. In order to characterize the faults, we use three non-invasive sensors: (a) SNR sensor, (b) S-parameter, and (c) Correlation peak sensor. We use a single programmable hardware to implement each of these sensors. These sensors monitor a parameter change on the cable in real-time. The experimental insights gained are used to construct an a priori Bayesian network depicting the non-deterministic relationship between an effect and its causes. This uncertainty is due to inhibitors such as sensor calibration issues and coupling mismatch between sensor transceivers suitably handled through a Noisy-OR function. The results of Bayesian inference with belief propagation provides up to 97% match with the ground truth state. Regarding the cable fault, our experimental results show a best-case detection and localization accuracy of 98% & 97.2% respectively.</p
The Embedding Wiring Interconnect System (EWIS) becomes the critical component in aging aircrafts due to the appearance of wiring faults that are mainly classified into hard faults (i.e. open and short circuit), incipient faults (i.e. shielding damage, pinching, etc.) and intermittent faults (i.e. arcing). Online wiring fault diagnosis is a very important domain of study since it enables monitoring the state of operating EWIS. Within this context, Multi-Carrier Reflectometry (MCR) and its variant Orthogonal Multi-Tone Time Domain Reflectometry (OMTDR) are suitable candidates since the frequency band is segregated into several frequency sub-bands using orthogonal sub-carriers in order to control the signal bandwidth and avoid forbidden bands. In fact, conventional reflectometry-based signals may disturb native signals active in the live network and lead to false alarms due to the cross-talk phenomena and loads’ variations in electronics. Significantly, OMTDR-based diagnosis permits to detect and locate the appearance of incipient faults as soon as possible thanks to adequate real-time processing. The analysis of the OMTDR-based reflectograms informs based on the amplitude of the corresponding peaks on the severity of the faults, but that a better quantification of their stage of evolution is necessary. This includes a characterization of incipient fault aiming at estimating its parameters such as length, resistance (R), inductance (L), capacitance (C), conductance (G) etc., is more interesting for fault prognosis solution. This permits estimating the fault’s severity thus allowing to predict its evolution with time and therefore planning a predictive maintenance. The literature related to precursor fault characterization is poor since available research work accomplished by NASA and other teams depend on complex and non-generic physics-based models. This paper aims at detecting, locating and characterizing incipient faults in shielded twisted pair cables using OMTDR and optimization-based algorithms. To do so, a three-dimensional numerical modeling of shielded-twisted pairs is developed and validated by a RLCG model for a stranded twisted pair including the pitch of twist and frequency dependent effects (i.e. proximity and skin effects). After that, a three-dimensional shielding damage fault is developed and integrated in the cable model. A Reflectometry-based approach is performed to obtain the fault’s signature. This is followed by applying an optimization algorithm that is capable of characterizing the shielding damage fault thus estimating its length and RLCG parameters variations. The evolution of the RLCG parameter variations according to the length, width and depth of the shielding damage are investigated in this paper. The proposed methodology is validated by experimental results using an electronic card including Xilinx Zynq 7010 FPGA to inject/receive OMTDR signals with the shielded twisted pair cables.
Several approaches have been proposed and applied for reconstructing the topology of an unknown network. Although, promising results have been obtained, offline passive testing was only accessible. On the other hand, a wide range of wiring networks embedded in critical systems as power grids and power-plants can not be easily shutdown for testing purposes. Accordingly, we will propose in this paper an approach for diagnosing obscured networks in an on-line live mode, thanks to the Orthogonal Multi-tone Time Domain Reflectometry (OMTDR). Optimization techniques namely the genetic algorithm will be integrated with the OMTDR method to enable revealing the topology of the black-boxed tested network. Practical real-life experimental setups are dedicated to validate the proposed approach.
