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, was 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.