All GREYDIENT ESRs at POLIMI’s Energy Department

The GREYDIENT consortium gathered in person for the fourth time since the start of the project. This time, the event was held in Milan at the headquarters of the POLIMI’s Energy Department, and the LASAR team was in charge of the organization.

During the first two days, the ESRs shared updates about their work and got insightful feedback from their colleagues. Leonardo Miqueles (ESR 5) presented about grey-box digital twins and their applications to the risk monitoring and control of nuclear power plants. Juan-Pablo Futalef (ESR 6) explained how to “greyfy” cyber-physical systems and generate scenarios efficiently.

Prof. Di Maio talks about research collaboration and thanks all GREYDIENT attendants for their cooperation

Besides the updates, the ESRs attended trainings on topics such as multi-agent simulations, sensitivity analysis, Bayesian networks, and design thinking. Professionals in charge of these trainings were Prof. Gyunyoung Heo (Kyung Hee Uni.) , Prof. Stefano Magistretti (POLIMI), Prof. Emanuele Borgonovo (Bocconi Uni.), and Prof. Ibrahim Ahmed (POLIMI).

The ESRs also shared experiences and brought up ideas about improving collaboration and the overall experience within the network… and visited the National Museum of Science and Technology Leonardo Da Vinci!

Attendants watching the GREYDIENT movie during the dinner’s event

The next GREYDIENT event will be in Thessaloniki, Greece, and Aristotle University of Thessaloniki is in charge of the organization.

For more GREYDIENT information, check out it’s website.

Happy Year of the Rabbit

Chinese buffet

With China the largest community in LASAR, the Chinese New Year was everything but quiet. Everyone gathered to celebrate the upcoming year of the Rabbit by bringing a collection of traditional Chinese plates.

This small break was a great chance to exchange smiles and reflect about next events in our group, which is constantly evolving.

Indeed, the year of the Rabbit is expected to bring quietness, yet powerful reflections after times of contemplation.

Happy 2023 from LASAR

2022 Christmas party

2022 was an exciting year for LASAR. Now, we would like to start 2023 with some highlights!

We are welcoming two new PhD students: Nicolás Cárdenas from Chile and Stefano Marchetti from Italy. We wish you a successful PhD journey!

Bin Wang just joined us as a Visiting PhD student from Wuhan University of Technology.

On January 17th, LASAR will host the GREYDIENT NWE4. The preparations are ready to welcome people from all around Europe and discuss Grey-box models for an entire week.

Stay tuned for more news from LASAR!

Congratulations to Dario Valcamonico!!!

He won the PhD award sponsored by PayPal Ireland at the ESREL2022 conference, Dublin, 28th August – 1st September 2022.

The work “A Taxonomy for Modelling Reports of Process Safety Events in the Oil and Gas Industry” has received the award in the category “Innovation in Energy Sector Applications”. It proposes an innovative methodology for quantitative risk assessment, which combines Natural Language Processing with Bayesian Networks for the systematic analysis of textual reports in hydrocarbon production assets.

The developed work is part of the multidisciplinary PhD program between the Energy Department and Department of Electronics, Information and Bioengineering of Politecnico di Milano and it has been developed within the ongoing PhD research of Dario Valcamonico under the supervision of Prof. Piero Baraldi and Prof. Enrico Zio in collaboration with our industrial partners Anna Crivellari, Luca Decarli and Laura La Rosa from Eni Natural Resources.

 Abstract and full paper are available at:

New Publication!

Title: A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks

Journal: Reliability Engineering & System Safety

Authors: Zhe Yang, Piero Baraldi, Enrico Zio

This is an open-access article that can be downloaded from the following link before February 08, 2022:


In multi-component systems, degradation, maintenance, renewal and operational mode change continuously the operating conditions. The identification of the onset of abnormal conditions from signal measurements taken in such evolving environments can be quite challenging, due to the difficulty of distinguishing the real cause of the signal variations. In this work, we present a method for fault detection in evolving environments that uses a Sparse Autoencoder-based Deep Neural Network (SAE-DNN) and a novel procedure that remarkably reduces the computational burden for setting the values of the hyperparameters. The method is applied to a synthetic case study and to a bearing vibration dataset. The results show that it is able to accurately detect faults in multi-component systems, outperforming other state-of-the-art methods.