Good news for Calvin Stephen from the iAMP-Hydro project coordination team.
His paper: "Evaluation of supervised machine learning techniques for cavitation detection and diagnosis in a pump-as-turbine system" has been accepted and is published.
What's in it?
The paper proposes an answer for predictive maintenance, using Machine Learning (ML) techniques for cavitation detection in pump-as-turbine (PAT) systems using
vibration data.
What is the result?
This hybrid framework supports informed decision-making, reduced downtime, and improved diagnostics in digital hydropower systems.
This is good news for the hydropower sector.
Read the full paper here. https://doi.org/10.1016/j.eswa.2025.129167
