Development of a Data-driven Injector Model for Condition Monitoring and Advanced Control Concepts
Diesel fuel injectors play a central role in the performance and robustness of large diesel engines. By instrumenting these injectors and using an appropriate condition monitoring system, it is possible to detect signs of wear and damage to the injectors and to counteract these through interventions in the engine operating strategy, thereby maintaining the highest level engine performance.
The target of this thesis is to develop a data-driven and real-time capable injector model that predicts key injection parameters as a function of other engine parameters for an unworn and fully functional injector. Condition monitoring is implemented by comparing predicted to measured parameter values. To generate a measurement database for modeling, diesel engine tests that involved a specially instrumented prototype fuel injector were carried out on a single-cylinder research engine at the Large Engines Competence Center (LEC).
Familiarization with engine and injection technology and corresponding measurement
technology and measurement parameters
Preprocessing of relevant engine and injection system measurement data
Investigation of the interrelationship between engine performance and injection system
behavior through an explorative data analysis
Development of a data-driven “Injector Model” for condition monitoring
Composition of the master’s thesis
Prerequisites: Programming skills in Python and/or R; experience in data analysis
Earliest possible start date: Immediately
Duration: Approximately 6 months
Ao. Univ.-Prof. Dr. Andreas Wimmer, +43 (316) 873-30101, firstname.lastname@example.org
Dr. Constantin Kiesling, +43 (316) 873-30092, email@example.com
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