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.