A framework and methods for on-board network level fault diagnostics in automobiles
2008
A significant number of electronic control units (ECUs) are nowadays networked
in automotive vehicles to help achieve advanced vehicle control and eliminate
bulky electrical wiring. This, however, inevitably leads to increased complexity in
vehicle fault diagnostics. Traditional off-board fault diagnostics and repair at
service centres, by using only diagnostic trouble codes logged by conventional onboard
diagnostics, can become unwieldy especially when dealing with intermittent
faults in complex networked electronic systems. This can result in inaccurate and
time consuming diagnostics due to lack of real-time fault information of the
interaction among ECUs in the network-wide perspective.
This thesis proposes a new framework for on-board knowledge-based
diagnostics focusing on network level faults, and presents an implementation of a
real-time in-vehicle network diagnostic system, using case-based reasoning. A
newly developed fault detection technique and the results from several practical
experiments with the diagnostic system using a network simulation tool, a
hardware- in-the- loop simulator, a disturbance simulator, simulated ECUs and real
ECUs networked on a test rig are also presented. The results show that the new
vehicle diagnostics scheme, based on the proposed new framework, can provide
more real-time network level diagnostic data, and more detailed and self-explanatory
diagnostic outcomes. This new system can provide increased diagnostic capability when compared with conventional diagnostic methods in
terms of detecting message communication faults. In particular, the underlying
incipient network problems that are ignored by the conventional on-board
diagnostics are picked up for thorough fault diagnostics and prognostics which can
be carried out by a whole-vehicle fault management system, contributing to the
further development of intelligent and fault-tolerant vehicles.
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