most citedMachine learning-based condition monitoring of powertrains in modern electric drives

14 citations · 20 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

System Identification Using Kolmogorov-Arnold Networks: A Case Study on Buck Converters

Nart Gashi, Panagiotis Kakosimos, George Papafotiou

Kolmogorov-Arnold Networks (KANs) are emerging as a powerful framework for interpretable and efficient system identification in dynamic systems. By leveraging the Kolmogorov-Arnold…

cs.LG2025

Reliable Thermal Monitoring of Electric Machines through Machine Learning

Panagiotis Kakosimos

The electrification of powertrains is rising as the objective for a more viable future is intensified. To ensure continuous and reliable operation without undesirable malfunctions,…

cs.LG20254 cited

Temperature Estimation in Induction Motors using Machine Learning

Dinan Li, Panagiotis Kakosimos

The number of electrified powertrains is ever increasing today towards a more sustainable future; thus, it is essential that unwanted failures are prevented, and a reliable operati…

cs.LG202514 cited

Machine learning-based condition monitoring of powertrains in modern electric drives

Dinan Li, Panagiotis Kakosimos, Luca Peretti

The recent technological advances in digitalization have revolutionized the industrial sector. Leveraging data analytics has now enabled the collection of deep insights into the pe…

cs.LG20252 cited

An Adaptive ML Framework for Power Converter Monitoring via Federated Transfer Learning

Panagiotis Kakosimos, Alireza Nemat Saberi, Luca Peretti

This study explores alternative framework configurations for adapting thermal machine learning (ML) models for power converters by combining transfer learning (TL) and federated le…