A Modeling Framework for Efficacy Assessment and Preventive Maintenance of Torrential Protection Works

International audienceNatural phenomena in mountains put people and assets at risk. Risk reduction measures can be either structural (protection works) or non-structural (risk zoning maps). In torrential watersheds of the French mountains, many checkdams have been built since the 19th century. As any civil engineering structure, those dams age and their failures may have severe effects on protected areas. Thus, preserving their level of efficacy is of a high interest. In a context of decreasing public budgets, it is necessary to assess their structural, functional and economic efficacy in order to quantify the residual risk and to choose the best maintenance strategies. Recently, a global approach has been proposed to integrate safety and reliability analysis, multicriteria decision-making methods, and information imperfection processing. However, it does not help in choosing the best strategy to maintain protection devices mainly because it does not cover all aspects related to protection works management making the balance between investment, preventive maintenance costs, and risk evolution. This paper develops a contribution addressing all those issues and proposes a new modeling approach based on Petri nets, whose main steps are: 1) to describe multi-scale protection works systems interaction between both natural and technological systems’ components; 2) to analyze structural and functional failure modes; 3) to develop a Petri net model for deterioration and maintenance modeling; 4) to compare different maintenance strategies under different hypothesis on degradation and damage processes

Uncertainty in Engineering

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners

Uncertainty in Engineering

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

The use of a modern technological system requires a good engineering approach, optimized operations, and proper maintenance in order to keep the system in an optimal state. Predictive maintenance focuses on the organization of maintenance actions according to the actual health state of the system, aiming at giving a precise indication of when a maintenance intervention will be necessary. Predictive maintenance is normally implemented by means of specialized computational systems that incorporate one of several models to fulfil diagnostics and prognostics tasks. As complexity of technological systems increases over time, single-model approaches hardly fulfil all functions and objectives for predictive maintenance systems. It is increasingly common to find research studies that combine different models in multi-model approaches to overcome complexity of predictive maintenance tasks, considering the advantages and disadvantages of each single model and trying to combine the best of them. These multi-model approaches have not been extensively addressed by previous review studies on predictive maintenance. Besides, many of the possible combinations for multi-model approaches remain unexplored in predictive maintenance applications; this offers a vast field of opportunities when architecting new predictive maintenance systems. This systematic survey aims at presenting the current trends in diagnostics and prognostics giving special attention to multi-model approaches and summarizing the current challenges and research opportunities

Initialization Requirement in Developing of Mobile Learning 'Molearn' for Biology Students Using Inquiry-based learning

Inquiry-based learning is kind of learning activities that involves students’ entire capabilities in exploring and investigating particular objects or phenomenon using critical thinking skills. Recently, information technology tangibly contributes in any education aspects, including the existence of e-learning, a widely spreading learning model in the 21st century education. This study aims at initializing needs of developing mobile learning ‘Molearn’ based on inquiry-based method. By cooperating with Biology teacher community in senior high school, ‘Molearn’ provides IT-based medium in Biology learning process

Monte Carlo based Petri net simulation for maintenance strategies assessment in series-parallel-series multi-physic systems

International audienceThe authors propose a methodology to assess the effectiveness of a maintenance strategy on the availability of a serial-parallel multi-physic system, using Monte Carlo simulation embedded in a Petri net model. The systems are composed of heterogenous components that are characterized by specific degradations and failure mechanisms. Building an effective maintenance strategy to improve the availability of such a system requires to monitoring the degradation of each component. We assume that each component is subject to stochastic degradations. Also, we consider that each component might have three health status, according to degradation thresholds, function of the component reliability: “healthy”, “degraded” and “failed”. The health condition of the overall system relies on the health status of each component. A model for tracking the status of each component has been worked out using a colored stochastic Petri net (CSPN). Each health status is modeled by a place within the CSPN model, where each component is modeled by a colored token. The degradation of each component of the system is evaluated based on the Monte Carlo simulation technique. Transition firing regarding a given color model the evolution of the associated component from a health condition to another due to the degradation mechanism or to a maintenance action aimed to restore partially or totally its performance. However, the degradation of each component does not have the same influence on the performance of the overall system. Operational performance indicators are introduced to quantify the influence of each component on the performance of the entire system. Furthermore, maintenance actions are defined taking into account the degradation level of each component, the influence that each component has on the performance of the system and the available repairman. The effectiveness of the maintenance strategy on the system availability is evaluated through simulation

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications