Towards a Data Analytics Framework to Support Prognostics & Health Management in Nuclear Power Plants PDF Download

Author: William Aylward
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description

Author: Katherine Marconi
Publisher: CRC Press
ISBN: 1482229250
Category : Business & Economics
Languages : en
Pages : 374

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Book Description
This book provides frameworks, use cases, and examples that illustrate the role of big data and analytics in modern health care, including how public health information can inform health delivery. Written for health care professionals and executives, this book presents the current thinking of academic and industry researchers and leaders from around the world. Using non-technical language, it includes case studies that illustrate the business processes that underlie the use of big data and health analytics to improve health care delivery.

Author: Jun Wang
Publisher: Frontiers Media SA
ISBN: 2889712990
Category : Technology & Engineering
Languages : en
Pages : 155

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Book Description

Author: Douglas Goodman
Publisher: John Wiley & Sons
ISBN: 1119356695
Category : Technology & Engineering
Languages : en
Pages : 384

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Book Description
A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life. Prognostics and Health Management provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improving system reliability using conditioned-based data (CBD) to the monitoring and management of health of systems. This proven approach uses electronic signatures extracted from conditioned-based electrical signals, including those representing physical components, and employs processing methods that include data fusion and transformation, domain transformation, and normalization, canonicalization and signal-level translation to support the determination of predictive diagnostics and prognostics. Written by noted experts in the field, Prognostics and Health Management clearly describes how to extract signatures from conditioned-based data using conditioning methods such as data fusion and transformation, domain transformation, data type transformation and indirect and differential comparison. This important resource: Integrates data collecting, mathematical modelling and reliability prediction in one volume Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes Presents information from a panel of experts on the topic Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods Written for system engineers working in critical process industries and automotive and aerospace designers, Prognostics and Health Management offers a guide to the application of condition-based data to produce signatures for input to predictive algorithms to produce prognostic estimates of functional health and life.

Author: Brigitte Chebel-Morello
Publisher: John Wiley & Sons
ISBN: 1119436850
Category : Technology & Engineering
Languages : en
Pages : 124

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Book Description
This book is the second volume in a set of books dealing with the evolution of technology, IT and organizational approaches and what this means for industrial equipment. The authors address this increasing complexity in two parts, focusing specifically on the field of Prognostics and Health Management (PHM). Having tackled the PHM cycle in the first volume, the purpose of this book is to tackle the other phases of PHM, including the traceability of data, information and knowledge, and the ability to make decisions accordingly. The book concludes with a summary analysis and perspectives regarding this emerging domain, since without traceability, knowledge and decision, any prediction of the health state of a system cannot be exploited.

Author: Peter K. Ghavami
Publisher: Createspace Independent Publishing Platform
ISBN: 9781500428594
Category : Big data
Languages : en
Pages : 0

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Book Description
This book offers concepts, methods and a framework to analyze healthcare data in new ways to improve patient health outcomes, improve population health and reduce costs. The framework takes the reader step-by-step through data warehouse and data management architectures, analytics algorithms and modeling techniques for applications in predictive medicine, optimization, machine learning, natural language processing, classification and data clustering. This book introduces the reader to data science for healthcare. It provides a clinical perspective to data including sources of open healthcare data, clinical prediction rules and much more.

Author: PHM Society
Publisher: Lulu.com
ISBN: 1936263122
Category : Technology & Engineering
Languages : en
Pages : 127

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Book Description
PHM Society established International Journal of Prognostics and Health Management (IJPHM) in 2009 to facilitate archival publication of peer-reviewed results from research and development in the area of PHM. As a journal solely dedicated to the emerging field of PHM IJPHM is the first of its kind and has been a focal point for dissemination of peer-reviewed PHM knowledge. While for the first few years the journal maintained only an online presence, the printed volumes will now be available and can be obtained upon request.

Author: Nam-Ho Kim
Publisher: Springer
ISBN: 3319447424
Category : Technology & Engineering
Languages : en
Pages : 355

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Book Description
This book introduces the methods for predicting the future behavior of a system’s health and the remaining useful life to determine an appropriate maintenance schedule. The authors introduce the history, industrial applications, algorithms, and benefits and challenges of PHM (Prognostics and Health Management) to help readers understand this highly interdisciplinary engineering approach that incorporates sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering. It is ideal for beginners because it introduces various prognostics algorithms and explains their attributes, pros and cons in terms of model definition, model parameter estimation, and ability to handle noise and bias in data, allowing readers to select the appropriate methods for their fields of application.Among the many topics discussed in-depth are:• Prognostics tutorials using least-squares• Bayesian inference and parameter estimation• Physics-based prognostics algorithms including nonlinear least squares, Bayesian method, and particle filter• Data-driven prognostics algorithms including Gaussian process regression and neural network• Comparison of different prognostics algorithms divThe authors also present several applications of prognostics in practical engineering systems, including wear in a revolute joint, fatigue crack growth in a panel, prognostics using accelerated life test data, fatigue damage in bearings, and more. Prognostics tutorials with a Matlab code using simple examples are provided, along with a companion website that presents Matlab programs for different algorithms as well as measurement data. Each chapter contains a comprehensive set of exercise problems, some of which require Matlab programs, making this an ideal book for graduate students in mechanical, civil, aerospace, electrical, and industrial engineering and engineering mechanics, as well as researchers and maintenance engineers in the above fields.

Author: Xianping Zhong
Publisher: Frontiers Media SA
ISBN: 2889767086
Category : Technology & Engineering
Languages : en
Pages : 142

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Book Description

Author: Nilanjan Dey
Publisher: Academic Press
ISBN: 9780128153680
Category : Science
Languages : en
Pages : 0

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Book Description
Healthcare Data Analytics and Management help readers disseminate cutting-edge research that delivers insights into the analytic tools, opportunities, novel strategies, techniques and challenges for handling big data, data analytics and management in healthcare. As the rapidly expanding and heterogeneous nature of healthcare data poses challenges for big data analytics, this book targets researchers and bioengineers from areas of machine learning, data mining, data management, and healthcare providers, along with clinical researchers and physicians who are interested in the management and analysis of healthcare data.