Interacting multiple-models, state augmented Particle Filtering for fault diagnostics

International audienceParticle Filtering (PF) is a model-based, filtering technique, which has drawn the attention of the Prognostic and Health Management (PHM) community due to its applicability to nonlinear models with non-additive and non-Gaussian noise. When multiple physical models can describe the evolution of the degradation of a component, the PF approach can be based on Multiple Swarms (MS) of particles, each one evolving according to a different model, from which to select the most accurate a posteriori distribution. However, MS are highly computational demanding due to the large number of particles to simulate. In this work, to tackle the problem we have developed a PF approach based on the introduction of an augmented discrete state identifying the physical model describing the component evolution, which allows to detect the occurrence of abnormal conditions and identifying the degradation mechanism causing it. A crack growth degradation problem has been considered to prove the effectiveness of the proposed method in the detection of the crack initiation and the identification of the occurring degradation mechanism. The comparison of the obtained results with that of a literature MS method and of an empirical statistical test has shown that the proposed method provides both an early detection of the crack initiation, and an accurate and early identification of the degradation mechanism. A reduction of the computational cost is also achieved.

An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems

open3noThe end states reached by an engineered system during an accident scenario depend not only on the sequences of the events composing the scenario, but also on their timing and magnitudes. Including these additional features within an overarching framework can render the analysis infeasible in practical cases, due to the high dimension of the system state-space and the computational effort correspondingly needed to explore the possible system evolutions in search of the interesting (and very rare) ones of failure. To tackle this hurdle, in this article we introduce a framework for efficiently probing the space of event sequences of a dynamic system by means of a guided Monte Carlo simulation. Such framework is semi-automatic and allows embedding the analyst's prior knowledge about the system and his/her objectives of analysis. Specifically, the framework allows adaptively and intelligently allocating the simulation efforts preferably on those sequences leading to outcomes of interest for the objectives of the analysis, e.g., typically those that are more safety-critical (and/or rare). The emerging diversification in the filling of the state-space by the preference-guided exploration allows also the retrieval of critical system features, which can be useful to analysts and designers for taking appropriate means of prevention and mitigation of dangerous and/or unexpected consequences. A dynamic system for gas transmission is considered as a case study to demonstrate the application of the method.openTurati, Pietro; Pedroni, Nicola; Zio, EnricoTurati, Pietro; Pedroni, Nicola; Zio, Enric

Psychological impact of Covid-19 pandemic on oncological patients: a survey in Northern Italy

The psychological impact of the Covid 19 pandemic on cancer patients, a population at higher risk of fatal consequences if infected, has been only rarely evaluated. This study was conducted at the Departments of Oncology of four hospitals located in the Verona area in Italy to investigate the psychological consequences of the pandemic on cancer patients under active anticancer treatments. A 13-item ad hoc questionnaire to evaluate the psychological status of patients before and during the pandemic was administered to 474 consecutive subjects in the time frame between April 27th and June 7th 2020. Among the 13 questions, 7 were considered appropriate to elaborate an Emotional Vulnerability Index (EVI) that allows to separate the population in two groups (low versus high emotional vulnerability) according to observed median values. During the emergency period, the feeling of high vulnerability was found in 246 patients (53%) and was significantly associated with the following clinical variables: female gender, being under chemotherapy treatment, age 64 65 years. Compared to the pre-pandemic phase, the feeling of vulnerability was increased in 41 patients (9%), remained stably high in 196 (42%) and, surprisingly, was reduced in 10 patients (2%). Overall, in a population characterized by an high level of emotional vulnerability the pandemic had a marginal impact and only a small proportion of patients reported an increase of their emotional vulnerability

Simulation-based exploration of high-dimensional system models for identifying unexpected events

Mathematical numerical models are increasingly employed to simulate system behavior and identify sequences of events or configurations of the system's design and operational parameters that can lead the system to extreme conditions (Critical Region, CR). However, when a numerical model is: i) computationally expensive, ii) high-dimensional, and iii) complex, these tasks become challenging. In this paper, we propose an adaptive framework for efficiently tackling this problem: i) a dimensionality reduction technique is employed for identifying the factors and variables that most affect the system behavior; ii) a meta-model is sequentially trained to replace the computationally expensive model with a computationally cheap one; iii) an adaptive exploration algorithm based on Markov Chain Monte Carlo is introduced for exploring the system state-space using the meta-model; iv) clustering and other techniques for the visualization of high dimensional data (e.g., parallel coordinates plot) are employed to summarize the retrieved information. The method is employed to explore a power network model involving 20 inputs. The CRs are properly identified with a limited computational cost, compared to another exploration technique of literature (i.e., Latin Hypercube Sampling)

Dimensionality reduction of the resilience model of a critical infrastructure network by means of elementary effects sensitivity analysis

International audienceModern Critical Infrastructures (CIs) are typically characterized by a large number of elements interconnected and interdependent. Their mathematical representation reflects these characteristics in models that typically turn out to be: 1) complex, since the relation between the variables can be nonlinear; 2) large, since a high number of variables is typically involved in the model; 3) dynamic, because the behavior of the system evolves in time. For this reason, the opportunities of exploring these models in order to extract information, such as identifying the most critical events, is conditioned by the computational cost of a simulation run and by the number of variables to explore. In this paper, we investigate the possibility of reducing the dimensionality of a model by identifying the variables that affect it most, by means of the Elementary Effects (EEs) method, which is a sensitivity analysis method capable of screening the input variables resorting to a limited number of model evaluations. Since the performance of the method relies on its settings, we analyze them proposing at the same time possible improvements. A hybrid network for gas and power distribution is considered as case study. The objective is to rank the importance of some uncertain parameters of the network (e.g., its failure and recovery characteristics) with respect to the system resilience properties (i.e., the capability of mitigating the effect of components failures and/or recovering its performance)

Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems

none3noThe efficient estimation of system reliability characteristics is of paramount importance for many engineering applications. Real world system reliability modeling calls for the capability of treating systems that are: i) dynamic, ii) complex, iii) hybrid and iv) highly reliable. Advanced Monte Carlo (MC) methods offer a way to solve these types of problems, which are feasible according to the potentially high computational costs. In this paper, the REpetitive Simulation Trials After Reaching Thresholds (RESTART) method is employed, extending it to hybrid systems for the first time (to the authors' knowledge). The estimation accuracy and precision of RESTART highly depend on the choice of the Importance Function (IF) indicating how close the system is to failure: in this respect, proper IFs are here originally proposed to improve the performance of RESTART for the analysis of hybrid systems. The resulting overall simulation approach is applied to estimate the probability of failure of the control system of a liquid hold-up tank and of a pump-valve subsystem subject to degradation induced by fatigue. The results are compared to those obtained by standard MC simulation and by RESTART with classical IFs available in the literature. The comparison shows the improvement in the performance obtained by our approach.Turati, Pietro; Pedroni, Nicola; Zio, EnricoTurati, Pietro; Pedroni, Nicola; Zio, Enric

Prediction Capability Assessment of Data-Driven Prognostic Methods for Railway Applications

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