Changing selected engine structure parameters, especially fuel system parameters, affects the emission of harmful
compounds in the exhaust gas. Changes in harmful compound emission are frequently ambiguous, as they highly
depend on parameters controlling the combustion process. An additional problem is that simple interactions are
frequently accompanied with mutual influence of these parameters. Therefore, we can say about different sensitivity
of diagnostic parameters to the same excitations coming from the engine structure but executed at different loading
states. When the set of diagnostic parameters is numerous and the values of these parameters are similar, there is a real
problem with their correct classification, frequently based on subjective assessment by the analyst. In the article, the
authors propose a methodology to classify the recorded diagnostic parameters. In earlier works by the authors [4,6,7],
the information capacity index method (the Hellwig method) was proposed as the measure of diagnostic parameter
sensitivity. Based on this method, a rankling of diagnostic parameters can be created which divides the set of diagnostic
variables into stimulators and destimulators. Novel authors’ approach to the presented problem consists in including
nominants, i.e. variables with the most favourable value for the analysed aspect of the research, in the set of diagnostic
variables. This normalisation of the set is believed to be helpful for making a diagnostic decision free from analyst’s
arbitrariness. The zero unitarization method can also be helpful in creating diagnostic tests.
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