A Multi-Objective Evolutionary Approach to Discover Explainability Tradeoffs when Using Linear Regression to Effectively Model the Dynamic Thermal Behaviour of Electrical Machines
Sprache des Titels:
Englisch
Original Kurzfassung:
Modelling and controlling heat transfer in rotating electrical machines is very important as it enables the
design of assemblies (e.g., motors) that are efficient and durable under multiple operational scenarios. To
address the challenge of deriving accurate data-driven estimators of key motor temperatures, we propose a
multi-objective strategy for creating Linear Regression (LR) models that integrate optimised synthetic fea
tures. The main strength of our approach is that it provides decision makers with a clear overview of the
optimal tradeoffs between data collection costs, the expected modelling errors and the overall explainability
of the generated thermal models. Moreover, as parsimonious models are required for both microcontroller de
ployment and domain expert interpretation, our modelling strategy contains a simple but effective step-wise
regularisation technique that can be applied to outline domain-relevant mappings between LR variables and
thermal profiling capabilities. Results indicate that our approach can generate accurate LR-based dynamic
thermal models when training on data associated with a limited set of load points within the safe operating
area of the electrical machine under study.
Forschungs-