A new Bayesian technique for readjusting LOLIMOT models: example with diesel engine emissions

Constraints on diesel engine emissions have increased dramatically over the past ten years. In this situation, design of experiments (DoE) are generally used to model the engine's exhaust emissions (EE) behaviour. The main drawback of parametric modelling is that, if the system evolves (e.g. new product development), then the model is no longer valid. Our proposition, focused on change management, is based on the Bayesian theory and presents two new algorithms. The aim of this paper is to outline a method for readjusting LOLIMOT models resulting from the DoE with as little data as possible, in order to optimise the EE of new engines. Two algorithms are presented: one use new data to readjust the model and the other, use both new data and expert judgement. We prove that Bayesian theory could be used to reduce the number of required test points and so, the cost of new product development.