Congratulations to Dario Valcamonico!!!

esrel 2022 photo award dario

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


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


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.


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

CitationPinciroli, 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


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.


!!!Halloween Welcome Party!!! 




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


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.


New Publication!

Title: Metamodeling and On-Line Clustering for Loss-of-Flow Accident Precursors Identification in a Superconducting Magnet Cryogenic Cooling Circuit

Journal: Energies


This is an open-access article that can be downloaded from the following link: https://www.mdpi.com/1996-1073/14/17/5552/pdf


In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at the cryogenic temperature of 4.5 K by one or more Superconducting Magnet Cryogenic Cooling Circuits (SMCCC). To guarantee cooling, Loss-of-Flow Accidents (LOFAs) in the SMCCC are to be avoided. In this work, we develop a three-step methodology for the prompt detection of LOFA precursors (i.e., those combinations of component failures causing a LOFA). First, we randomly generate accident scenarios by Monte Carlo sampling of the failures of typical SMCCC components and simulate the corresponding transient system response by a deterministic thermal-hydraulic code. In this phase, we also employ quick-running Proper Orthogonal Decomposition (POD)-based Kriging metamodels, adaptively trained to reproduce the output of the long-running code, to decrease the computational time. Second, we group the generated scenarios by a Spectral Clustering (SC) employing the Fuzzy C-Means (FCM), in order to identify the main patterns of system evolution towards abnormal states (e.g., a LOFA). Third, we develop an On-line Supervised Spectral Clustering (OSSC) technique to associate time-varying parameters measured during plant functioning to one of the prototypical groups obtained, which may highlight the related LOFA precursors (in terms of SMCCC components failures). We apply the proposed technique to the simplified model of a cryogenic cooling circuit of a single module of the ITER Central Solenoid Magnet (CSM). The framework developed promptly detects 95% of LOFA events and around 80% of the related precursors.



New Publication!

Title: A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System

Journal: Reliability Engineering & System Safety

Authors: L. Puppo, N. Pedroni, Francesco di Maio, A. Bersano, C. Bertani, Enrico Zio

This is an open-access article that can be downloaded from the following link before October 17: https://authors.elsevier.com/c/1dfEy3OQ~fVt7D


In the safety analyses of passive systems for nuclear energy applications, computationally demanding models can be substituted by fast-running surrogate models coupled with adaptive sampling techniques; for speeding up the exploration of the components and system state-space and the characterization of the conditions leading to failure (i.e., the system Critical failure Regions, CRs). However, in some cases of non-smoothness and multimodality of the state-space, the existing approaches do not suffice. In this paper, we propose a novel methodological framework, based on Finite Mixture Models (FMMs) and Adaptive Kriging (AK-MCS) for CRs characterization in case of non-smoothness and/or multimodality of the output. The framework contains three main steps: 1) dimensionality reduction through FMMs to tackle the output non-smoothness and multimodality, while focusing on its clusters defining the system failure; 2) adaptive training (AK-MCS) of the metamodel on the reduced space to mimic the time-demanding model and, finally, 3) use of the trained metamodel provide the output for new input combinations and retrieve information about the CRs.

The framework is applied to the case study of a generic Passive Safety System (PSS) for Decay Heat Removal (DHR) designed for advanced Nuclear Power Plants (NPPs). The PSS operation is modelled through a time-demanding Thermal-Hydraulic (T-H) model and the pressure selected for characterizing the PSS response to accidental conditions shows a strong non-smooth and multimodal behavior. A comparison with an alternative approach of literature relying on the use of Support Vector Classifier (SVC) to cluster the output domain is presented to support the framework as a valid approach in challenging CRs characterization.

Title: Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks

Journal: Reliability Engineering & System Safety


This is an open-access article that can be downloaded from the following link before September 22: https://authors.elsevier.com/a/1dWEL3OQ%7EfVt4Z


In this paper, a multistate Bayesian Network (BN) is proposed to model and evaluate the functional performance of safety barriers in Oil and Gas plants. The nodes of the BN represent the safety barriers Health States (HSs) and the corresponding conditional Failure Probability (FP) values are assigned. HSs are assessed on the basis of specific Key Performance Indicators (KPIs) related to the barrier characteristics (i.e., technical, procedural or organizational, continuously monitored or event-based characterized). FP values are estimated from failure datasets (for technical barriers), evaluated by Human Reliability Analysis (HRA) (for operational and organizational barriers) and assigned by expert elicitation (for barriers lacking data or information). For illustration, the multistate BN model is developed for preventive barriers and applied to a case study related to the potential release of flammable material in the slug catcher of a representative O&G Upstream plant which may lead to major accident scenarios (fire, explosion, toxic dispersion). The results from the case study demonstrate that the multistate BN model is able to account for the safety barriers HS and their associated functional performance.


New Publication!

Title: Multi-State Reliability Assessment Model of Base-Load Cyber-Physical Energy Systems (CPES) during Flexible Operation Considering the Aging of Cyber Components

Journal: Energies

Authors: Zhaojun Hao, Francesco di Maio, Enrico Zio

This is an open-access article that can be downloaded from the following link: https://www.mdpi.com/1996-1073/14/11/3241/htm

Abstract: Cyber-Physical Energy Systems (CPESs) are energy systems which rely on cyber components for energy production, transmission and distribution control, and other functions. With the penetration of Renewable Energy Sources (RESs), CPESs are required to provide flexible operation (e.g., load-following, frequency regulation) to respond to any sudden imbalance of the power grid, due to the variability in power generation by RESs. This raises concerns on the reliability of CPESs traditionally used as base-load facilities, such as Nuclear Power Plants (NPPs), which were not designed for flexible operation, and more so, since traditionally only hardware components aging and stochastic failures have been considered for the reliability assessment, whereas the contribution of the degradation and aging of the cyber components of CPSs has been neglected. In this paper, we propose a multi-state model that integrates the hardware components stochastic failures with the aging of cyber components, and quantify the unreliability of CPES in load-following operations under normal/emergency conditions. To show the application of the reliability assessment model, we consider the case of the Control Rod System (CRS) of a NPP typically used for a base-load energy supply.


New Publication!

Title: Failure identification in a nuclear passive safety system by Monte Carlo simulation with adaptive Kriging

Journal: Nuclear Engineering and Design

Authors: Puppo, L., Pedroni, N., Bersano, A., Di Maio, F., Bertani, C., Zio, E.

This is an open-access article that can be downloaded from the following link before July 22, 2021: https://authors.elsevier.com/c/1dAUO6j-yo1ve

Abstract: Passive Safety Systems (PSSs) are increasingly employed in advanced Nuclear Power Plants (NPPs). Their safety performance is evaluated through computationally expensive Thermal-Hydraulic (T-H) simulations models and the identification of the operational conditions which lead to unsafe conditions (the so-called Critical failure Regions, CRs) may be challenging.

In the present paper, a computational framework is proposed to identify the CRs of a generic passive Decay Heat Removal (DHR) system of a NPP. A time-demanding Best-Estimate Thermal-Hydraulic (BE-TH) model of the system is used to train a fast-running metamodel embedded within an adaptive sampling technique of literature, namely Adaptive Kriging Monte Carlo Sampling (AK-MCS), so as to provide increased accuracy in proximity of the failure threshold and identify which input values lead the PSS to failure. To the best authors’ knowledge this is the first time that the metamodel-based AK-MCS technique is applied for the identification of the CRs of a PSS of an NPP.



New Publication!

Title: Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production

Journal: Sustainability (Special Issue in Renewable Energy Sources for Electrical Power: Reliability Assessment, Condition Monitoring, Prognostics and Health Management, Production Prediction)

Authors: Sameer Al-Dahidi, Piero Baraldi, Enrico Zio, Lorenzo Montelatici

This is an open-access article that can be downloaded from the following link: https://www.mdpi.com/2071-1050/13/11/6417

Abstract: The accurate prediction of wind energy production is crucial for an affordable and reliable power supply to consumers. Prediction models are used as decision-aid tools for electric grid operators to dynamically balance the energy production provided by a pool of diverse sources in the energy mix. However, different sources of uncertainty affect the predictions, providing the decision-makers with non-accurate and possibly misleading information for grid operation. In this regard, this work aims to quantify the possible sources of uncertainty that affect the predictions of wind energy production provided by an ensemble of Artificial Neural Network (ANN) models. The proposed Bootstrap (BS) technique for uncertainty quantification relies on estimating Prediction Intervals (PIs) for a predefined confidence level. The capability of the proposed BS technique is verified, considering a 34 MW wind plant located in Italy. The obtained results show that the BS technique provides a more satisfactory quantification of the uncertainty of wind energy predictions than that of a technique adopted by the wind plant owner and the Mean-Variance Estimation (MVE) technique of literature. The PIs obtained by the BS technique are also analyzed in terms of different weather conditions experienced by the wind plant and time horizons of prediction.



Title: A Bayesian framework of inverse uncertainty quantification with principal component analysis and Kriging for the reliability analysis of passive safety systems

Journal: Nuclear Engineering and Design

Authors: Giovanni Roma, Francesco Di Maio, Andrea Bersano, Nicola Pedroni, Cristina Bertani, Fulvio Mascari, Enrico Zio

Free download until June 8th, 2021: https://authors.elsevier.com/c/1cx276j-yo1jX

Abstract: In this work, we propose an Inverse Uncertainty Quantification (IUQ) approach to assigning Probability Density Functions (PDFs) to uncertain input parameters of Thermal-Hydraulic (T-H) models used to assess the reliability of passive safety systems. The approach uses experimental data within a Bayesian framework. The application to a RELAP5-3D model of the PERSEO (In-Pool Energy Removal System for Emergency Operation) facility located at SIET laboratory (Piacenza, Italy) is demonstrated. Principal Component Analysis (PCA) is applied for output dimensionality reduction and Kriging meta-modeling is used to emulate the reduced set of RELAP5-3D code outputs. This is done to decrease the computational cost of the Markov Chain Monte Carlo (MCMC) posterior sampling of the uncertain input parameters, which requires a large number of model simulations.



Title: Association rules extraction for the identification of functional dependencies in complex technical infrastructures

Journal: Reliability Engineering & System Safety

Authors: Federico Antonello, Piero Baraldi, Ahmed Shokry, Enrico Zio, U. Gentile, L. Serio

Free download: https://authors.elsevier.com/a/1cZf%7E3OQ%7EfTD0i

Abstract: This work proposes a method for identifying functional dependencies among components of complex technical infrastructures using databases of alarm messages. The developed method is based on the representation of the alarm database by a binary matrix, the use of the Apriori algorithm for mining association rules and a new algorithm for identifying groups of functionally dependent components. The effectiveness of the proposed method is shown by means of its application to an artificial case study and a real large-scale database of alarms generated by different supervision systems of the complex technical infrastructure of CERN (European Organization for Nuclear Research).



Title: Optimal sensor positioning on pressurized equipment based on Value of Information

Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

Authors: Seyed Mojtaba Hoseyni, Francesco Di Maio, Enrico Zio

Free download: https://journals.sagepub.com/eprint/UV5DRSZSNEB3BC9ZCTWU/full

Abstract: In this work, we apply a simulation-based framework that makes use of the Value of Information (VoI) for identifying the optimal spatial positioning of sensors on pressurized equipment. VoI is a utility-based Figure of Merit (FoM) which quantifies the benefits/losses of acquiring information. Sensors are typically positioned on pressurized equipment in line with specific recommendations based on operational experience, like UNI 11096 in Italy. We show that the recommendations in UNI 11096 are, indeed, justified and that, incidentally, relying on VoI for the optimization of the sensor positioning, one can achieve the same monitoring performance, as measured by VoI, where following UNI 11096, but with a reduced number of sensors. The proposed VoI-based approach can, thus, be used to confirm or revise recommendations coming from operational experience.



Title: Identification of LOFA precursors in ITER superconducting magnet cryogenic cooling circuit

Journal: Reliability Engineering & System Safety

Authors: V. Destino, R. Bonifetto, F. Di Maio, N. Pedroni, R. Zanino, E. Zio

Free download until March 09, 2021: https://authors.elsevier.com/c/1cR1j_Lf6GyXK-

Abstract: In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at cryogenic temperature by a Superconducting Magnet Cryogenic Cooling Circuit (SMCCC). To guarantee cooling, Loss-Of-Flow Accidents (LOFAs) in the SMCCC are to be avoided. In this work, an approach to identify LOFA precursors (i.e., those component failures leading to a LOFA) is presented. The approach is based on a Spectral Clustering (SC) method using the Fuzzy C-Means (FCM) algorithm and is applied to the SMCCC of a single module of the ITER Central Solenoid (CS).



Title: A semi-supervised method for the characterization of degradation of nuclear power plants steam generators

Journal: Progress in Nuclear Energy

Authors: Luca Pinciroli, Piero Baraldi, Ahmed Shokry, Enrico Zio, Redouane Seraoui, Carole Mai

Free download until January 23rd , 2021: https://authors.elsevier.com/c/1cBEaXx6ZPO~l

Abstract: The digitalization of nuclear power plants, with the rapid growth of information technology, opens the door to the development of new methods of condition-based maintenance. In this work, a semi-supervised method for characterizing the level of degradation of nuclear power plant components using measurements collected during plant operational transients is proposed. It is based on the fusion of selected features extracted from the monitored signals. Feature selection is formulated as a multi-objective optimization problem. The objectives are the maximization of the feature monotonicity and trendability, and the maximization of a novel measure of correlation between the feature values and the results of non-destructive tests performed to assess the component degradation. The features of the Pareto optimal set are normalized and the component degradation level is defined as the median of the obtained values. The developed method is applied to real data collected from steam generators of pressurized water reactors. It is shown able to identify degradation level with errors comparable to those obtained by ad-hoc non-destructive tests.


Join us for an “Aperitivo at ESREL2020 PSAM15” during the originally scheduled conference week of  June 22-24.

It is a prelibate taster of the actual ESREL2020 PSAM15 Conference, which will be held in Venice next 1-6 November.

Experts in the field of risk and reliability assessment will share their knowledge in three interesting webinars.


Register in advance for this webinar:


After registering, you will receive a confirmation email with the link for joining the webinar.


June 22, 16:30 – 17:30

The importance of risk calculations in Co-Vid 19

George Boustras
European University Cyprus, Cyprus


June 23, 16:30 – 17:30

How can we make quick and informed decisions despite uncertainty and complexity? – some ideas and an engineering problem

Michael Beer
Institute for Risk and Reliability, Leibniz Universität Hannover, Germany
Institute for Risk and Uncertainty, University of Liverpool, UK
International Joint Research Center for Engineering Reliability and Stochastic Mechanics (ERSM), Tongji University, China


June 24, 16:30 – 17:30

A short introduction to the theory of Belief Reliability

Rui Kang
School of Reliability and Systems Engineering, Beihang University, China


Download the webinar flyer.

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