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Improving fuel efficiency while meeting relevant emission limits set by emissions legislation is among the main objectives of engine development. Simultaneously the development costs and development time have to be steadily reduced. For these reasons, the high demands in terms of quality and validity of measurements at the engine test bench are continuously rising. This paper will present a new methodology for efficient testing of an industrial combustion engine in order to improve the process of decision making for combustion-relevant component setups. The methodology includes various modules for increasing measurement quality and validity. Modules like stationary point detection to determine steady state engine behavior, signal quality checks to monitor the signal quality of chosen measurement signals and plausibility checks to evaluate physical relations between several measurement signals ensure a high measurement quality over all measurements.

Due to the increasing number of engine setting parameters to be optimized, model based calibration techniques have been introduced to medium speed engine testing to keep the number of engine tests low. Polynomials in combination with d-optimal test plans have been proven to be a good choice for modeling the stationary behavior of selected engine outputs. Model approaches like artificial neural networks (ANNs) have been rarely used for medium speed purposes since they require quite high amounts of testing data for model training. To evaluate the potential of these model approaches radial basis function networks, a subclass of neural networks, as well as Gaussian processes have been investigated as alternatives to polynomials. A manageable amount of tests according to an adapted d-optimal test plan was carried out at a test bench.