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: https://rpsonline.com.sg/rps2prod/esrel22-epro/html/S29-05-616.xml

Loading

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: https://authors.elsevier.com/a/1eHH73OQ%7EfVtoN

Abstract:

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.

Loading

New Publication!

New Publication!

Title: Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning

Journal: Renewable Energy

Authors: Luca Pinciroli, Piero Baraldi, Guido Ballabio, Michele Compare, Enrico Zio

Citation: Pinciroli, L.; Baraldi, P.; Ballabio, G.; Compare, M.; Zio, E. Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning. Renewable Energy 2022, 183, 752-763. https://doi.org/10.1016/j.renene.2021.11.052

This is an open-access article that can be downloaded from the following link before Jan 14, 2022: https://authors.elsevier.com/a/1e8OO3QJ-dhrWR

Abstract:

Equipment of renewable energy systems are being supported by Prognostics & Health Management (PHM) capabilities to estimate their current health state and predict their Remaining Useful Life (RUL). The PHM health state estimates and RUL predictions can be used for the optimization of the systems Operation and Maintenance (O&M). This is an ambitious and challenging task, which requires to consider many factors, including the availability of maintenance crews, the variability of energy demand and production, the influence of the operating conditions on equipment performance and degradation and the long time horizons of renewable energy systems usage. We develop a novel formulation of the O&M optimization as a sequential decision problem and we resort to Deep Reinforcement Learning (DRL) to solve it. The proposed solution approach combines proximal policy optimization, imitation learning, for pre-training the learning agent, and a model of the environment which describes the renewable energy system behavior. The solution approach is tested by its application to a wind farm O&M problem. The optimal solution found is shown to outperform those provided by other DRL algorithms. Also, the approach does not require to select a-priori a maintenance strategy, but, rather, it discovers the best performing policy by itself.

Loading

New Publication!

Title: Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews

Journal: Energies

Authors: Luca Pinciroli, Piero Baraldi, Guido Ballabio, Michele Compare, Enrico Zio

This is an open-access article that can be downloaded from the following link: https://www.mdpi.com/1996-1073/14/20/6743/pdf

Abstract:

The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible to significantly optimize operation and maintenance (O&M) by combining the (uncertain) information provided by PHM with the other factors influencing O&M activities, including the limited availability of maintenance crews, the variability of energy demand and corresponding production requests, and the long-time horizons of energy systems operation. In this work, we consider the operation and maintenance optimization of wind turbines in wind farms woth multiple crews. A new formulation of the problem as a sequential decision problem over a long-time horizon is proposed and solved by deep reinforcement learning based on proximal policy optimization. The proposed method is applied to a wind farm of 50 turbines, considering the availability of multiple maintenance crews. The optimal O&M policy found outperforms other state-of-the-art strategies, regardless of the number of available maintenance crews.

Loading