Resumen:
The development of data-driven models for the identification of the degradation state of industrial components is challenged by several issues, such as the unavailability of large datasets containing historical data, the presence of measurement noise, and the intrinsic stochasticity of the degradation process. The proposed method merges the degradation state assessments provided by self-organizing maps (SOMs) trained using data collected from the component under test (component-based) with that provided by SOMs trained using data collected from a fleet of similar components (population-based). Within an ensemble approach, the outcomes of the SOM models are then aggregated using a dynamic weights proportional method based on the individual model local performances in situations similar to the one under test. The proposed SOM-based ensemble approach has been verified with respect to an experimental case study concerning the identification of the degradation state of insulated-gate bipolar transistors, which are known as one of the most critical components in power systems.
