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Electrical motors need specific parametrizations to run in highly specialized use cases. However, finding such parametrizations may need a lot of time and expert knowledge. Furthermore, the task gets more complex as multiple optimization goals interplay. Thus, we propose a novel approach using Bayesian Optimization to find optimal configuration parameters for an electric motor. In addition, a multi-objective problem is present as two different and competing objectives must be optimized. At first, the motor must reach a desired revolution per minute as fast as possible. Afterwards, it must be able to continue running without fluctuating currents. For this task, we utilize Bayesian Optimization to optimize parameters. In addition, the evolutionary algorithm NSGA-II is used for the multi-objective setting, as NSGA-II is able to find an optimal pareto front. Our approach is evaluated using three different motors mounted to a test bench. Depending on the motor, we are able to find good pa rameters in about 60-100%.
Modern embedded systems comprise more and more software. This yields novel challenges in development and quality assurance. Complex software interactions may lead to serious performance issues that can have a crucial economic impact if they are not resolved during development. Henceforth, we decided to develop and evaluate a machine learning-based approach to identify performance issues. Our experiments using real-world data show the applicability of our methodology and outline the value of an integration into modern software processes such as continuous integration.
