New Pubblication

Title: Sensitivity Analysis by Differential Importance Measure for Unsupervised Fault Diagnostics

Authors: G. Floreale, P. Baraldi, X. Lu, P. Rossetti, E. Zio

Abstract: Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: 1) it can be developed using only normal condition data 2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; 3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; 4) it can be used in combination with any fault detection technique.


New Publication

Title: Combining Natural Language Processing and Bayesian Networks for the Probabilistic Estimation of the Severity of Process Safety Events in Hydrocarbon Production Assets

Authors: Dario VALCAMONICO, Piero BARALDI, Enrico ZIO, Luca DECARLI, Anna CRIVELLARI and Laura LA ROSA

This is an open-access article that can be downloaded here

Abstract:This work investigates the possibility of using the information contained in reports describing Process Safety Events (PSEs) occurred in hydrocarbon production assets to support Quantitative Risk Assessment (QRA). Specifically, a novel methodology combining Natural Language Processing (NLP) and Bayesian Networks (BNs) is proposed to estimate the probabilities of having PSEs of various classes of severity and identifying the factors that have mostly influenced their variation along the monitored period. A repository of reports of PSEs of hydrocarbons plants is considered to show the potentialities of the developed methodology. An application to a repository of reports of PSEs of hydrocarbons plants is considered to show the potentialities of the developed methodology. The results obtained in the application show that the proposed methodology allows identifying the critical factors for the severity of the consequences of PSEs. These results show that the framework can be used to inform and guide decisions about possible improvements of the system safety by mitigative and preventive barriers.


Congratulations to Dr. Bingsen Wang

Auguri Dr. Wang!

A couple of weeks ago, now ex-LASAR member, Dr. Binseng Wang, succeeded in completing his Ph.D. thesis, under the supervision of Prof. Baraldi and Prof. Zio. Now, he’s on his way back to China, where he will continue pursuing more academic achievements.

On his last day in Italy, the entire LASAR team gathered for a football match, following an amazing celebration where everyone had the opportunity to say farewell to our ex-colleague… with real MOUTAI!