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New i-EM paper on hydro condition monitoring and predictive maintenance

The international scientific journal Science Direct has recently published a new paper on “Condition monitoring and predictive maintenance methodologies for hydropower plants equipment” on its website. We are proud that the Head of data Science at i-EM, Alessandro Betti, together with our Senior Data Scientist, Fabrizio Ruffini, and our our Data Scientist, Antonio Piazzi, are among the authors of this paper, made in collaboration with Gianluca Paolinelli (alpitronic GmbH / Srl), Mauro Tucci and Emanuele Crisostomi (University of Pisa).

Here’s the paper’s abstract:

Hydropower plants are one of the most convenient option for power generation, as they generate energy exploiting a renewable source, they have relatively low operating and maintenance costs, and they may be used to provide ancillary services, exploiting the large reservoirs of available water. The recent advances in Information and Communication Technologies (ICT) and in machine learning methodologies are seen as fundamental enablers to upgrade and modernize the current operation of most hydropower plants, in terms of condition monitoring, early diagnostics and eventually predictive maintenance. While very few works, or running technologies, have been documented so far for the hydro case, in this paper we propose a novel Key Performance Indicator (KPI) that we have recently developed and tested on operating hydropower plants. In particular, we show that after more than one year of operation it has been able to identify several faults, and to support the operation and maintenance tasks of plant operators. Also, we show that the proposed KPI outperforms conventional multivariable process control charts, like the Hotelling index“.