A Generic Sensor Network Design Framework Based on a Detectability Measure

2010 ◽  
Author(s):  
Pingfeng Wang ◽  
Byeng D. Youn ◽  
Chao Hu
Keyword(s):  
Sensor Network ◽  
Health Management ◽  
Power Transformer ◽  
Relevant Information ◽  
Mixed Integer ◽  
Health State ◽  
Design Framework ◽  
Engineering Structures ◽  
State Classification ◽  
Technological Advances

Significant technological advances in sensing and communication promote the use of large sensor networks to monitor structural systems, identify damages, and quantify damage levels. Prognostics and health management (PHM) technique has been developed and applied for a variety of safety-critical engineering structures, given the critical needs of the structure health state awareness. The PHM performance highly relies on real-time sensory signals which convey the structural health relevant information. Designing an optimal structural sensor network (SN) with high detectability is thus of great importance to the PHM performance. This paper proposes a generic SN design framework using a detectability measure while accounting for uncertainties in material properties and geometric tolerances. Detectability is defined to quantify the performance of a given SN. Then, detectability analysis will be developed based on structural simulations and health state classification. Finally, the generic SN design framework can be formulated as a mixed integer nonlinear programming (MINLP) using the detectability measure and genetic algorithms (GAs) will be employed to solve the SN design optimization problem. A power transformer study will be used to demonstrate the feasibility of the proposed generic SN design methodology.

Probabilistic Design of Smart Sensing Functions for Structural Health Monitoring and Prognosis

2013 ◽  
Author(s):  
Abdulaziz T. Almaktoom ◽  
Zequn Wang ◽  
Pingfeng Wang
Keyword(s):  
Health Management ◽  
Power Transformer ◽  
Relevant Information ◽  
Mixed Integer ◽  
Health State ◽  
Design Framework ◽  
Engineering Structures ◽  
Structural Health ◽  
State Classification ◽  
Technological Advances

Significant technological advances in sensing and communication promote the use of large sensor networks to monitor structural systems, identify damages, and quantify damage levels. Prognostics and health management (PHM) technique has been developed and applied for a variety of safety-critical engineering structures, given the critical needs of the structure health state awareness. The PHM performance highly relies on real-time sensory signals which convey the structural health relevant information. Designing an optimal structural sensor network (SN) with high detectability is thus of great importance to the PHM performance. This paper proposes a generic SN design framework using a detectability measure while accounting for uncertainties in material properties and geometric tolerances. Detectability is defined to quantify the performance of a given SN. Then, detectability analysis will be developed based on structural simulations and health state classification. Finally, the generic SN design framework can be formulated as a mixed integer nonlinear programming (MINLP) using the detectability measure and genetic algorithms (GAs) will be employed to solve the SN design optimization problem. A power transformer study will be used to demonstrate the feasibility of the proposed generic SN design methodology.

A Hybrid Inference Approach for Health Diagnostics With Unexampled Faulty States

2012 ◽  
Author(s):  
Prasanna Tamilselvan ◽  
Pingfeng Wang
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Power Transformer ◽  
Roller Bearing ◽  
Health State ◽  
Health States ◽  
System Failures ◽  
State Classification ◽  
Testing Stage ◽  
Health Diagnosis

System health diagnostics provides diversified benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of engineered systems. Successful health diagnostics requires the knowledge of system failures. However, with an increasing complexity it is extraordinarily difficult to have a well-tested system so that all potential faulty states can be realized and studied at product testing stage. Thus, real time health diagnostics requires automatic detection of unexampled faulty states through the sensory signals to avoid sudden catastrophic system failures. This paper presents a hybrid inference approach (HIA) for structural health diagnosis with unexampled faulty states, which employs a two-fold inference process comprising of preliminary statistical learning based anomaly detection and artificial intelligence based health state classification for real time condition monitoring. The HIA is able to identify and isolate the unexampled faulty states through interactively detecting the deviation of sensory data from the known health states and forming new health states autonomously. The proposed approach takes the advantages of both statistical approaches and artificial intelligence based techniques and integrates them together in a unified diagnosis framework. The performance of proposed HIA is demonstrated with a power transformer and roller bearing health diagnosis case studies, where Mahalanobis distance serves as a representative statistical inference approach.

scholarly journals Online Health Management for Complex Nonlinear Systems Based on Hidden Semi-Markov Model Using Sequential Monte Carlo Methods

2012 ◽  
Vol 2012 ◽  
pp. 1-22
Author(s):  
Qinming Liu ◽  
Ming Dong
Keyword(s):  
Monte Carlo ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Markov Model ◽  
Sequential Monte Carlo ◽  
Health Management ◽  
Health State ◽  
Health States ◽  
Complex Nonlinear System ◽  
Complex Nonlinear Systems

Health management for a complex nonlinear system is becoming more important for condition-based maintenance and minimizing the related risks and costs over its entire life. However, a complex nonlinear system often operates under dynamically operational and environmental conditions, and it subjects to high levels of uncertainty and unpredictability so that effective methods for online health management are still few now. This paper combines hidden semi-Markov model (HSMM) with sequential Monte Carlo (SMC) methods. HSMM is used to obtain the transition probabilities among health states and health state durations of a complex nonlinear system, while the SMC method is adopted to decrease the computational and space complexity, and describe the probability relationships between multiple health states and monitored observations of a complex nonlinear system. This paper proposes a novel method of multisteps ahead health recognition based on joint probability distribution for health management of a complex nonlinear system. Moreover, a new online health prognostic method is developed. A real case study is used to demonstrate the implementation and potential applications of the proposed methods for online health management of complex nonlinear systems.

scholarly journals La comunicación institucional de contenidos sanitarios mediante notas de prensa y su reflejo en la prensa diaria. El caso de Aragón = Institutional communication of health contents through press releases and its presence in the daily press. The case of Aragón

2017 ◽  
Vol 8 (1) ◽  
pp. 38
Author(s):  
Florencio-Jesús García-Latorre ◽  
Carlos Aibar-Remón ◽  
Maite Gobantes-Bilbao
Keyword(s):  
Political Communication ◽  
Health Management ◽  
Relevant Information ◽  
Monitoring And Control ◽  
Press Releases ◽  
Health Related ◽  
The Media ◽  
The Government ◽  
Institutional Communication ◽  
One Year

Resumen: Introducción: La publicación de notas de prensa es una práctica habitual de los gabinetes de comunicación de los Departamentos de Salud autonómicos mediante la que ofrecen información relevante para un mejor conocimiento y utilización del sistema, la difusión de sus actividades y la rendición de cuentas. Objetivo: Analizar las características de los comunicados de prensa emitidos por la Dirección de Comunicación del Gobierno de Aragón y verificar el grado en que los temas tratados en estas informaciones obtienen visibilidad en los medios impresos. Material y método: Revisión de las notas de prensa de contenido sanitario durante un año y comprobación de si los temas propuestos han sido llevados a las páginas de los dos periódicos de ámbito autonómico de la comunidad. Resultados: Se encontraron 190 notas de prensa, generalmente centradas en aspectos de la gestión sanitaria. Un 43% no obtuvieron reflejo en la prensa. Entre los dos medios estudiados se observa una concordancia moderada en cuanto a los temas publicados. Conclusiones: La comunicación institucional puede ser considerada un tipo de comunicación política, con unas características particulares, que es filtrada y contrapesada por los medios dentro de su labor de control de las instituciones públicas.Palabras clave: Salud, Comunicación institucional, Notas de prensa, PrensaAbstract: Introduction: The publication of press releases is a common practice of press offices of the Regional Departments of Health to offer relevant information for a better knowledge and use of the health system, to publicize their activities and for accountability purposes. Objective: To analyze some features of the press releases issued by the Directorate of Communication of the Government of Aragon and also verify the extent to which the subjects covered in these informations obtain visibility in the print media. Material and method: A review of the health-related press releases during one year was carried out; we also checked whether the proposed issues were brought to the pages of the two regional newspapers. Results: 190 press releases were found, mainly focused on aspects of health management. 43% of those reports were not mentioned in the newspapers. Between the two dailies studied, a moderate level of agreement in the selection of the subjects that were translated into news was observed. Conclusions: Institutional communication can be considered a type of political communication, with particular features, that is filtered and counterbalanced by the media, given that one of its tasks is the monitoring and control of the performance of public institutions.Keywords: Health, Institutional communication, Press releases, Press 

Optimal sensor placement to detect ruptures in pipeline systems subject to uncertainty using an Adam-mutated genetic algorithm

2021 ◽  
pp. 147592172110565
Author(s):  
Chungeon Kim ◽  
Hyunseok Oh ◽  
Byung Chang Jung ◽  
Seok Jun Moon
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Sensor Network ◽  
Sensor Placement ◽  
Mixed Integer ◽  
Working Fluid ◽  
Small Scale ◽  
Pipeline System ◽  
Optimal Sensor Placement ◽  
Pipeline Network

Pipelines in critical engineered facilities, such as petrochemical and power plants, conduct important roles of fire extinguishing, cooling, and related essential tasks. Therefore, failure of a pipeline system can cause catastrophic disaster, which may include economic loss or even human casualty. Optimal sensor placement is required to detect and assess damage so that the optimal amount of resources is deployed and damage is minimized. This paper presents a novel methodology to determine the optimal location of sensors in a pipeline network for real-time monitoring. First, a lumped model of a small-scale pipeline network is built to simulate the behavior of working fluid. By propagating the inherent variability of hydraulic parameters in the simulation model, uncertainty in the behavior of the working fluid is evaluated. Sensor measurement error is also incorporated. Second, predefined damage scenarios are implemented in the simulation model and estimated through a damage classification algorithm using acquired data from the sensor network. Third, probabilistic detectability is measured as a performance metric of the sensor network. Finally, a detectability-based optimization problem is formulated as a mixed integer non-linear programming problem. An Adam-mutated genetic algorithm (AMGA) is proposed to solve the problem. The Adam-optimizer is incorporated as a mutation operator of the genetic algorithm to increase the capacity of the algorithm to escape from the local minimum. The performance of the AMGA is compared with that of the standard genetic algorithm. A case study using a pipeline system is presented to evaluate the performance of the proposed sensor network design methodology.

Answer First or Google First? Using the Internet in ways that Enhance, not Impair, One’s Subsequent Retention of Needed Information

2020 ◽  
pp. 147572572096159
Author(s):  
Saskia Giebl ◽  
Stefany Mena ◽  
Benjamin C. Storm ◽  
Elizabeth Ligon Bjork ◽  
Robert A. Bjork
Keyword(s):  
Relevant Information ◽  
Choice Test ◽  
Cognitive Resources ◽  
Related Information ◽  
Needed Information ◽  
Technological Advances ◽  
New Information ◽  
Subsequent Recall ◽  
Programming Knowledge ◽  
Problem Solving Task

Technological advances have given us tools—Google, in particular—that can both augment and free up our cognitive resources. Research has demonstrated, however, that some cognitive costs may arise from our reliance on such external memories. We examined whether pretesting—asking participants to solve a problem before consulting Google for needed information—can enhance participants’ subsequent recall for the searched-for content as well as for relevant information previously studied. Two groups of participants, one with no programming knowledge and one with some programming knowledge, learned several fundamental programming concepts in the context of a problem-solving task. On a later multiple-choice test with transfer questions, participants who attempted the task before consulting Google for help out-performed participants who were allowed to search Google right away. The benefit of attempting to solve the problem before googling appeared larger with some degree of programming experience, consistent with the notion that some prior knowledge can help learners integrate new information in ways that benefit its learning as well as that of previously studied related information.

Gas Turbine Health State Prognostics by Means of Bayesian Hierarchical Models

2019 ◽  
Author(s):  
Enzo Losi ◽  
Mauro Venturini ◽  
Lucrezia Manservigi
Keyword(s):  
Gas Turbine ◽  
Field Data ◽  
Health Management ◽  
Theoretical Background ◽  
Health State ◽  
Prediction Errors ◽  
Probabilistic Prediction ◽  
Future Health ◽  
Bayesian Hierarchical ◽  
Prediction Reliability

Abstract The prediction of the time evolution of gas turbine performance is an emerging requirement of modern prognostics and health management (PHM), aimed at improving system reliability and availability, while reducing life cycle costs. In this work, a data-driven Bayesian Hierarchical Model (BHM) is employed to perform a probabilistic prediction of gas turbine future health state thanks to its capability to deal with fleet data from multiple units. First, the theoretical background of the predictive methodology is outlined to highlight the inference mechanism and data processing for estimating BHM predicted outputs. Then, BHM is applied to both simulated and field data representative of gas turbine degradation to assess its prediction reliability and grasp some rules of thumb for minimizing BHM prediction error. For the considered field data, the average values of the prediction errors were found to be lower than 1.0 % or 1.7 % for single- or multi-step prediction, respectively.

Methods for obtaining health state utility values: generic preference-based measures of health

2016 ◽  
Author(s):  
John Brazier ◽  
Julie Ratcliffe ◽  
Joshua A. Salomon ◽  
Aki Tsuchiya
Keyword(s):  
Health State Utility ◽  
Health State ◽  
Multiattribute Utility ◽  
Health States ◽  
Utility Values ◽  
Published Evidence ◽  
State Classification ◽  
Health State Utility Values ◽  
Generic Measures ◽  
Health State Classification

This chapter describes the six most widely used generic preference-based measures of health (GPBMs) (also known as multiattribute utility scales): EQ-5D, SF-6D, HUI, AQoL, 15D, and QWB. GPBMs have become the most widely used method for obtaining health state utility values. They contain a health state classification with multilevel dimensions that together describe a universe of health states and a set of values (where full health = 1 and dead = 0) for each health state obtained by eliciting the preferences (typically) of members of the general population. These measures are reviewed in terms of their content, methods of valuation, the scores they generate, and the possible reasons for the differences found. Their performance is reviewed using published evidence on their validity across conditions, and the implications for their use in policy making discussed. The chapter also reviews the generic measures available for use in populations of children and adolescents.

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