4.7 Article

Bus network decomposition for fault detection and isolation through power line communication

期刊

ISA TRANSACTIONS
卷 137, 期 -, 页码 492-505

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2023.01.023

关键词

Network decomposition; Power line communication; Fault detection; Fault isolation; Residual

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This paper proposes a novel fault detection and isolation (FDI) method for complex embedded wired communication networks. The method is based on power line communication (PLC) transmission systems and uses orthogonal frequency division multiplexing (OFDM) to estimate transmission coefficients. The health indicators and residuals are computed by comparing the online estimated coefficients with the reference coefficients. A methodology for handling complex networks, such as bus networks, is also proposed. The FDI method is validated using real data from a Y-shaped network test bench and simulated data from a more complex network.
This paper deals with fault detection and isolation (FDI) in complex embedded wired communication networks. The considered faults are soft faults which do not prevent the communication, but may evolve into hard faults, i.e. short or open circuit. A novel FDI method based on power line commu-nication (PLC) transmission systems is proposed. In these PLC systems, the transmission coefficients between the source and each receiver are estimated for communication purposes using orthogonal frequency division multiplexing (OFDM). Health indicators and residuals are computed by comparing the online estimated transmission coefficients with the reference coefficients. A methodology for dealing with complex networks, such as bus networks, is proposed. It is based on the decomposition of the network into several Y-shaped sub-networks. Each of these sub-networks is monitored to detect the presence of a fault. The FDI method is first validated using real data extracted from a Y-shaped network test bench. Then, the proposed approach is validated on a more complex network using realistic simulated data. & COPY; 2023 ISA. Published by Elsevier Ltd. All rights reserved.

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