Automotive Engine FDI by Application of an Automated Model-Based and Data-Driven Design Methodology
Fault detection and isolation (FDI) in automotive diesel engines is
important in order to achieve and guarantee low exhaust emissions,
high vehicle uptime, and efficient repair and maintenance. This paper
illustrates how a set of general methods for model-based sequential
residual generation and data-driven statistical residual evaluation
can be combined into an automated design methodology. The automated
design methodology is then utilized to create a complete FDI-system
for an automotive diesel engine. The performance of the obtained
FDI-system is evaluated using measurements from road drives and engine
test-bed experiments. The overall performance of the FDI-system is
good in relation to the required design effort. In particular no
specific tuning of the FDI-system, or any adaption of the design
methodology, were needed. It is illustrated how estimations of the
statistical powers of the fault detection tests in the FDI-system can
be used to further increase the performance, specifically in terms of
fault isolability.
Carl Svärd, Mattias Nyberg, Erik Frisk and Mattias Krysander
Control Engineering Practice,
2013
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