Reliability importance measures for a health-aware control of drinking water networks

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This work focuses on a health-aware Model Predictive Control (MPC) scheme, which aims at enhancing the availability of the system. The objective is to extend the uptime of the system by delaying, as much as possible the system reliability decay. The weights of the MPC cost function are set according to some reliability importance measures. This work describes the main reliability importance measures and studies which of them are best suited for a health-aware MPC strategy applied to a Drinking Water Network. The overall system reliability as well as the reliability importance measures are computed online through a Dynamic Bayesian Network.Peer ReviewedPostprint (author's final draft

An RCN guide to the Private Finance Initiative

SIGLEAvailable from British Library Document Supply Centre-DSC:m00/43194 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

MPC framework for system reliability optimization

This book is devoted to the demands of research and industrial centers for diagnostics, monitoring and decision making systems that result from the increasing complexity of automation and systems, the need to ensure the highest level of reliability and safety, and continuing research and the development of innovative approaches to fault diagnosis. The contributions combine domains of engineering knowledge for diagnosis, including detection, isolation, localization, identification, reconfiguration and fault-tolerant control. The book is divided into six parts: (I) Fault Detection and Isolation; (II) Estimation and Identification; (III) Robust and Fault Tolerant Control; (IV) Industrial and Medical Diagnostics; (V) Artificial Intelligence; (VI) Expert and Computer Systems

MPC design based on a DBN reliability model: Application to drinking water networks

This paper presents a Model Predictive Control (MPC) strategy taking into account system and component reliability for drinking water networks. The objective is to deal from an availability point of view with a closed-loop system combining a deterministic part related to the system dynamics and a stochastic part related to the actuators and system reliability. The main contribution of this work consists in integrating the reliability assessment computed on-line using a Dynamic Bayesian Network (DBN) into the MPC algorithm.Peer Reviewe

System reliability aware model predictive control framework

This paper presents a Model Predictive Control (MPC) framework taking into account the usage of the actuators to preserve system reliability while maximizing control performance. Two approaches are proposed to preserve system reliability: a global approach that integrates in the control algorithm a representation of system reliability, and a local approach that integrates a representation of component reliability. The trade-off between the system reliability and the control performance should be taken into account. A methodology for MPC tuning is proposed to handle this trade-off. System and component reliability are computed based on Dynamic Bayesian Network. The effectiveness and benefits of the proposed control framework are discussed through its application to an over-actuated system.Peer Reviewe

Reliability importance measures for a health-aware control of drinking water networks

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This work focuses on a health-aware Model Predictive Control (MPC) scheme, which aims at enhancing the availability of the system. The objective is to extend the uptime of the system by delaying, as much as possible the system reliability decay. The weights of the MPC cost function are set according to some reliability importance measures. This work describes the main reliability importance measures and studies which of them are best suited for a health-aware MPC strategy applied to a Drinking Water Network. The overall system reliability as well as the reliability importance measures are computed online through a Dynamic Bayesian Network.Peer Reviewe

Reliability computation within an MPC health-aware framework

This paper presents a comparison between two different approaches for reliability consideration within a Health-Aware Control framework which takes into account system and component reliability by means of reliability importance measures. The two different approaches for reliability assessment consideration are the instantaneous reliability and the expected one. The system reliability performance under both approaches is compared in a control strategy applied to a drinking water network.Peer Reviewe

Research article - Comet 81P/Wild 2 under a microscope

The Stardust spacecraft collected thousands of particles from comet 81P/Wild 2 and returned them to Earth for laboratory study. The preliminary examination of these samples shows that the nonvolatile portion of the comet is an unequilibrated assortment of materials that have both presolar and solar system origin. The comet contains an abundance of silicate grains that are much larger than predictions of interstellar grain models, and many of these are high-temperature minerals that appear to have formed in the inner regions of the solar nebula. Their presence in a comet proves that the formation of the solar system included mixing on the grandest scales