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In-vehicle network level fault diagnostics using fuzzy inference systems
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Suwatthikul, Jittiwut, McMurran, Ross and Jones, R. P.. (2011) In-vehicle network level fault diagnostics using fuzzy inference systems. Applied Soft Computing, Vol.11 (No.4). pp. 3709-3719. ISSN 1568-4946
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Official URL: http://dx.doi.org/10.1016/j.asoc.2011.02.001
Abstract
This paper presents an application of an Adaptive-Network-based Fuzzy Inference System (ANFIS) for pre-diagnosing incipient underlying in-vehicle network problems which possibly could cause further failures. An experiment on ANFIS-based pre-diagnosis of network level faults on Controller Area Network (CAN) by utilising available network protocol signals, such as error frames, is reported. The experimental results show that the pre-diagnostic system can efficiently classify causes of error frames transmitted on a CAN bus, and identify "network health" which indicates healthiness of the network when being used for message communication. The potential causes of the faults can be narrowed down, and further network diagnostics and prognostics can be performed. (C) 2011 Elsevier B.V. All rights reserved.
| Item Type: | Journal Article |
|---|---|
| Divisions: | Faculty of Science > Engineering |
| Journal or Publication Title: | Applied Soft Computing |
| Publisher: | Elsevier BV |
| ISSN: | 1568-4946 |
| Date: | 2011 |
| Volume: | Vol.11 |
| Number: | No.4 |
| Page Range: | pp. 3709-3719 |
| Identification Number: | 10.1016/j.asoc.2011.02.001 |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Funder: | National Electronics and Computer Technology Centre, Royal Thai Government , Jaguar , Land Rover UK |
| URI: | http://wrap.warwick.ac.uk/id/eprint/41491 |
Data sourced from Thomson Reuters' Web of Knowledge
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