Fault Diagnostics Using Network Motif Signature

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Published Sep 23, 2012
Tsai-Ching Lu David L. Allen Yilu Zhang Mutasim A. Salman

Abstract

In modern vehicles, controls are distributed over multiple Electronic Control Units (ECUs) that are connected through in-vehicle communication networks. Fault diagnostics for such a distributed control system is very challenging, which has resulted in many no-trouble-found (NTF) cases during warranty repairs. To address this problem, we propose a novel network-theoretic approach that detects, identifies, and localizes faults using both the structure of the communication network (topological information) and message flow information. The proposed method not only enables the characterization of normal operation and a-priori known faults across communication networks, which is already beyond the current practice of individual ECU centric diagnostics, but also the diagnostics of unknown or cascading failures emerging from unexpected operational environments.

How to Cite

Lu, T.-C. ., L. Allen, D. ., Zhang, Y. ., & A. Salman, M. (2012). Fault Diagnostics Using Network Motif Signature. Annual Conference of the PHM Society, 4(1). https://doi.org/10.36001/phmconf.2012.v4i1.2158
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Keywords

fault diagnostics, network-theory based IVHM, net-motif signature

References
Bastian, M., Heymann, S., and Jacomy, M. (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks, International AAAI Conference on Weblogs and Social Media.
Cordella, L.P., Pasquale, F., Sansone, and C., Vento, M. (2004). A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs, , IEEE Transaction on Pattern Analysis and Machine Intelligence, 26(10), 1367-1372.
Djidjev, H., Sandine. G., and Storlie, C. (2011). Graph Based Statistical Analysis of Network Traffic, MLG.
Lu, T.-C., Zhang, Y., Allen, D. L. and Salman, S. M. (2011). Design for Fault Analysis Using Multi-Partite, Multi-Attribute Between Centrality Measures, PHM.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., and Alon, U. (2002), Network-motifs: Simple Building Blocks of Complex Networks, Science, vol. 298, no. 5594, pp. 824–827.
Wang, T., Srivatsa, M., Agrawal, D., and Liu, L. (2009), Learning, Indexing, and Diagnosing Network Faults, KDD.
Wernicke, S. (2006), Efficient Detection of Network Motifs, IEEE/ACM Transaction on Computational Biology and Bioinformatics, 3(4), 347-359.
Section
Technical Research Papers

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