Distributed Sensor Fusion for Wire Fault Location Using Sensor Clustering Strategy
Author(s) -
Wafa Hassen,
Fabrice Auzanneau,
Luca Incarbone,
François Pérès,
Ayeley Tchangani
Publication year - 2015
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1155/2015/538643
Subject(s) - computer science , cluster analysis , fault (geology) , wireless sensor network , interference (communication) , real time computing , topology control , reliability (semiconductor) , sensor fusion , reflectometry , transmission (telecommunications) , time domain , computer network , key distribution in wireless sensor networks , telecommunications , wireless , artificial intelligence , wireless network , channel (broadcasting) , power (physics) , physics , quantum mechanics , seismology , computer vision , geology
From reflectometry methods, this work aims at locating accurately electrical faults in complex wiring networks. Increasing demand for online diagnosis has imposed serious challenges on interference mitigation. In particular, diagnosis has to be carried out while the target system is operating. The interference becomes more even critical in the case of complex networks where distributed sensors inject their signals simultaneously. The objective of this paper is to develop a new embedded diagnosis strategy in complex wired networks that would resolve interference problems and eliminate ambiguities related to fault location. To do so, OMTDR (Orthogonal Multi-tone Time Domain Reflectometry) method is used. For better coverage of the network, communication between sensors is integrated using the transmitted part of the OMTDR signal. It enables data control and transmission for fusion to facilitate fault location. In order to overcome degradation of diagnosis reliability and communication quality, we propose a new sensor clustering strategy based on network topology in terms of distance and number of junctions. Based on CAN bus network, we prove that data fusion using sensor clustering strategy permits to improve the diagnosis performance.
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