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The progressive reduction in permissible tailpipe emissions levels from automobiles has been achieved through the adoption of ever more complex engine control systems and aftertreatment components. This, in turn, has resulted in the development of increasingly sophisticated monitoring systems that can detect the failure or gradual degradation of any of these components and thereby fulfill the requirements of the stringent On-Board Diagnostic (OBD) legislation. Traditional monitoring techniques involve a physical model approach, which describes the system under investigation. This approach has limitations, such as available knowledge base and computational load. Neural networks, on the other hand, have been recognized as a powerful tool for modeling systems which exhibit nonlinear relationships between measured variables, such as internal combustion engines.

The adoption of each new level of automotive emissions legislation often requires the introduction of additional emissions reduction techniques or the development of existing emissions control systems. This, in turn, usually requires the implementation of new sensors and hardware which must subsequently be monitored by the on-board fault detection systems. The reliable detection and diagnosis of faults in these systems or sensors, which result in the tailpipe emissions rising above the progressively lower failure thresholds, provides enormous challenges for OBD engineers. This paper gives a review of the field of fault detection and diagnostics as used in the automotive industry. Previous work is discussed and particular emphasis is placed on the various strategies and techniques employed. Methodologies such as state estimation, parity equations and parameter estimation are explained with their application within a physical model diagnostic structure.

The incorporation of one-dimensional simulation codes within engine modelling applications has proved to be a useful tool in evaluating unsteady gas flow through elements in the exhaust system. This paper reports on an experimental and theoretical investigation into the behaviour of unsteady gas flow through catalyst substrate elements. A one-dimensional (1-D) catalyst model has been incorporated into a 1-D simulation code to predict this behaviour. Experimental data was acquired using a ‘single pulse’ test rig. Substrate samples were tested under ambient conditions in order to investigate a range of regimes experienced by the catalyst during operation. This allowed reflection and transmission characteristics to be quantified in relation to both geometric and physical properties of substrate elements.

One-dimensional (1-D) modelling codes are now commonplace in engine simulation programs. Thermodynamic analysis associated with the unsteady gas flow through engine ducting is an important element within the modelling process. This paper reports on a new approach in analysing mass and energy transport through a pipe system using the mesh method. A new system has been developed for monitoring wave energy and gas properties, using a two-dimensional grid to represent the time-mesh boundary domain. This approach has allowed for refinement of the current mesh method by allowing more accurate monitoring of gas properties. The modified method was tested using measured results from a Single-Shot Rig. A CFD analysis was also conducted and compared with the new method. The new method performed very well on the range of pipe geometries tested.