A significant number of electronic control units (ECUs) are nowadays networked\ud in automotive vehicles to help achieve advanced vehicle control and eliminate\ud bulky electrical wiring. This, however, inevitably leads to increased complexity in\ud vehicle fault diagnostics. Traditional off-board fault diagnostics and repair at\ud service centres, by using only diagnostic trouble codes logged by conventional onboard\ud diagnostics, can become unwieldy especially when dealing with intermittent\ud faults in complex networked electronic systems. This can result in inaccurate and\ud time consuming diagnostics due to lack of real-time fault information of the\ud interaction among ECUs in the network-wide perspective.\ud This thesis proposes a new framework for on-board knowledge-based\ud diagnostics focusing on network level faults, and presents an implementation of a\ud real-time in-vehicle network diagnostic system, using case-based reasoning. A\ud newly developed fault detection technique and the results from several practical\ud experiments with the diagnostic system using a network simulation tool, a\ud hardware- in-the- loop simulator, a disturbance simulator, simulated ECUs and real\ud ECUs networked on a test rig are also presented. The results show that the new\ud vehicle diagnostics scheme, based on the proposed new framework, can provide\ud more real-time network level diagnostic data, and more detailed and self-explanatory\ud diagnostic outcomes. This new system can provide increased diagnostic capability when compared with conventional diagnostic methods in\ud terms of detecting message communication faults. In particular, the underlying\ud incipient network problems that are ignored by the conventional on-board\ud diagnostics are picked up for thorough fault diagnostics and prognostics which can\ud be carried out by a whole-vehicle fault management system, contributing to the\ud further development of intelligent and fault-tolerant vehicles
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