Integrating Disparate Nuclear Data Sources for Improved Predictive Maintenance Modeling PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Integrating Disparate Nuclear Data Sources for Improved Predictive Maintenance Modeling PDF full book. Access full book title Integrating Disparate Nuclear Data Sources for Improved Predictive Maintenance Modeling by Zachary Allen Welz. Download full books in PDF and EPUB format.
Author: Zachary Allen Welz Publisher: ISBN: Category : Economic life of fixed assets Languages : en Pages : 107
Book Description
The United States (US) nuclear industry is one of the most heavily regulated businesses in the world, creating a culture of world-class design, operation, and maintenance. In an article published on modern maintenance technologies, Terrence OHanlon (past Chief Asset Manager for Reliabilityweb.com) stated, "world class companies often devote up to 50 percent of their entire maintenance resources to condition based monitoring and the planned work that is required as a result of the findings" [1]. One would expect US nuclear power plants to constantly upgrade, improve, and expand their operations and maintenance departments and tactics. Since the early 1990s, US nuclear plant expenses due to operations and maintenance have increased by over 10% and were estimated at $20.62/MWhr (>16 billion USD) in 2015 [2]. While costs are increasing, and supporting technologies are more readily available than ever, plants commonly rely on reactive and basic preventive maintenance techniques. This dissertation investigates improved maintenance practices by establishing baseline performance capabilities only possible with advanced maintenance strategies. A method of extracting plant data to facilitate predictive modeling is introduced. This method utilizes information (condition data and maintenance data) from disparate sources within modern nuclear plants to extract failure cycles. These data sources will help transition plants from reactive to preventive maintenance through establishment of maintenance intervals, improvement of existing preventive maintenance intervals using better-quality failure cycle information, and/or transition from preventive to predictive maintenance. To extract this information, digital maintenance records are essential; therefore, a formal discussion of digital maintenance systems and related implementation standards is given. To support the need for maintenance data, a framework for utilization of failure cycles in predictive maintenance models is provided. Three different applications are examined, where maintenance dependent models are compared to traditional models to quantify the capacity for improvement in failure-time predictions. This work shows that the utilization of process and maintenance data in prognostic modeling results in significantly improved failure predictions. This additional time to respond will help organizations avoid or plan for failures before they occur, which supports more effective maintenance and capital replacement policies.
Author: Zachary Allen Welz Publisher: ISBN: Category : Economic life of fixed assets Languages : en Pages : 107
Book Description
The United States (US) nuclear industry is one of the most heavily regulated businesses in the world, creating a culture of world-class design, operation, and maintenance. In an article published on modern maintenance technologies, Terrence OHanlon (past Chief Asset Manager for Reliabilityweb.com) stated, "world class companies often devote up to 50 percent of their entire maintenance resources to condition based monitoring and the planned work that is required as a result of the findings" [1]. One would expect US nuclear power plants to constantly upgrade, improve, and expand their operations and maintenance departments and tactics. Since the early 1990s, US nuclear plant expenses due to operations and maintenance have increased by over 10% and were estimated at $20.62/MWhr (>16 billion USD) in 2015 [2]. While costs are increasing, and supporting technologies are more readily available than ever, plants commonly rely on reactive and basic preventive maintenance techniques. This dissertation investigates improved maintenance practices by establishing baseline performance capabilities only possible with advanced maintenance strategies. A method of extracting plant data to facilitate predictive modeling is introduced. This method utilizes information (condition data and maintenance data) from disparate sources within modern nuclear plants to extract failure cycles. These data sources will help transition plants from reactive to preventive maintenance through establishment of maintenance intervals, improvement of existing preventive maintenance intervals using better-quality failure cycle information, and/or transition from preventive to predictive maintenance. To extract this information, digital maintenance records are essential; therefore, a formal discussion of digital maintenance systems and related implementation standards is given. To support the need for maintenance data, a framework for utilization of failure cycles in predictive maintenance models is provided. Three different applications are examined, where maintenance dependent models are compared to traditional models to quantify the capacity for improvement in failure-time predictions. This work shows that the utilization of process and maintenance data in prognostic modeling results in significantly improved failure predictions. This additional time to respond will help organizations avoid or plan for failures before they occur, which supports more effective maintenance and capital replacement policies.
Author: Brian Gabriel Gaudio Publisher: ISBN: Category : Languages : en Pages : 128
Book Description
Over the past one hundred years, maintenance concepts have evolved from a simple “fix when broken” approach to advanced prognostic methods used today that leverage large amounts of historical, operational, and primary sensor data to predict when and how failures will occur. For firms that produce complex assets, the ability to predict with accuracy when maintenance overhauls should occur can provide both an operational and economic competitive advantage. This research evaluates the hypothesis that the accuracy of predictive maintenance models for complex assets can be improved with the addition of historical operational data and failure modes can be more clearly identified by examining primary sensor data. This hypothesis is tested through data analysis on predictive maintenance models used by commercial turbofan jet engines. Because some engines have operated for decades, their entire operational records are not in the appropriate digital format and not utilized by current models. This research identifies alternate, available sources of this data. The additional data sources were processed and incorporated into the existing predictive maintenance models. The addition of the operational data sources did not reduce the error in the model used to forecast the useful life of assets for preventative maintenance, which suggests that the current coverage provided by existing data is sufficient. The examination of primary sensor data isolated one component that displayed age-related degradation and maintenance costs.
Author: Gary Hardiman Publisher: MDPI ISBN: 3039287443 Category : Science Languages : en Pages : 202
Book Description
A “genotype" is essentially an organism's full hereditary information which is obtained from its parents. A "phenotype" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.
Author: Al-Humairi, Safaa Najah Saud Publisher: IGI Global ISBN: Category : Technology & Engineering Languages : en Pages : 537
Book Description
In the tumultuous period of Industrial Revolution 5.0, a pressing challenge confronts our global community: exploring the intricate interplay between technology, education, and renewable energy. As we stand at the cusp of transformative change, the relentless pace of technological evolution, coupled with the imperative to foster sustainable practices, demands a profound understanding of the synergies and challenges inherent in this dynamic landscape. Utilizing Renewable Energy, Technology, and Education for Industry 5.0 emerges as a compelling solution, offering a comprehensive guide tailored for academic scholars seeking clarity amidst the complexities of this revolutionary wave. The rapid convergence of technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and automation, alongside the critical need for renewable energy integration and a paradigm shift in education, presents a multifaceted challenge. Industry leaders grapple with the transformation of processes, educators seek to align curricula with the demands of Industry 5.0, and environmental advocates strive for sustainable solutions. This intricate dance of innovation, education reform, and environmental consciousness requires a comprehensive approach to unraveling complexities, fostering collaboration, and navigating ethical considerations.
Author: R. Keith Mobley Publisher: Elsevier ISBN: 0080478697 Category : Technology & Engineering Languages : en Pages : 451
Book Description
This second edition of An Introduction to Predictive Maintenance helps plant, process, maintenance and reliability managers and engineers to develop and implement a comprehensive maintenance management program, providing proven strategies for regularly monitoring critical process equipment and systems, predicting machine failures, and scheduling maintenance accordingly. Since the publication of the first edition in 1990, there have been many changes in both technology and methodology, including financial implications, the role of a maintenance organization, predictive maintenance techniques, various analyses, and maintenance of the program itself. This revision includes a complete update of the applicable chapters from the first edition as well as six additional chapters outlining the most recent information available. Having already been implemented and maintained successfully in hundreds of manufacturing and process plants worldwide, the practices detailed in this second edition of An Introduction to Predictive Maintenance will save plants and corporations, as well as U.S. industry as a whole, billions of dollars by minimizing unexpected equipment failures and its resultant high maintenance cost while increasing productivity. A comprehensive introduction to a system of monitoring critical industrial equipment Optimize the availability of process machinery and greatly reduce the cost of maintenance Provides the means to improve product quality, productivity and profitability of manufacturing and production plants
Author: Carlos Guedes Soares Publisher: CRC Press ISBN: 1482233002 Category : Technology & Engineering Languages : en Pages : 1136
Book Description
Covering recent developments in maritime transportation and exploitation of sea resources, encompassing ocean and coastal areas, this book is intended for academics and professionals involved in the development of marine transportation and the exploitation of sea resources.
Author: Valentine Fontama Publisher: Apress ISBN: 148420445X Category : Computers Languages : en Pages : 178
Book Description
Data Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis. The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book also provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented descriptions and concrete end-to-end examples, sufficient to ensure the reader can immediately begin using this important new service. It describes all aspects of the service from data ingress to applying machine learning and evaluating the resulting model, to deploying the resulting model as a machine learning web service. Finally, this book attempts to have minimal dependencies, so that you can fairly easily pick and choose chapters to read. When dependencies do exist, they are listed at the start and end of the chapter. The simplicity of this new service from Microsoft will help to take Data Science and Machine Learning to a much broader audience than existing products in this space. Learn how you can quickly build and deploy sophisticated predictive models as machine learning web services with the new Azure Machine Learning service from Microsoft.
Author: International Atomic Energy Agency Publisher: IAEA ISBN: 9789201039071 Category : Business & Economics Languages : en Pages : 178
Book Description
There is a need to optimise the maintenance of nuclear power plants, both to improve reliability and increase competitiveness. The tendency is to move from preventative (time based) maintenance to one dependent on the condition of plant and its components. This publication collects and analyses proven condition based maintenance strategies and techniques in Member States as well as selected papers on maintenance optimisation.
Author: Aleksandar Nikolic Publisher: BoD – Books on Demand ISBN: 9535133578 Category : Technology & Engineering Languages : en Pages : 202
Book Description
Since first AC current high-power hydropower plant was put in operation, built by Nikola Tesla and George Westinghouse in 1895 on Niagara Falls, electrification of the world has dramatically changed. The growing power demand and energy consumption in the last decades require fundamental changes in the process, power production, and services. These requirements tend to use both conventional and nonconventional energy generation in order to have power plants economically useful and environmentally friendly to the society. The goal of this textbook is to provide an up-to-date review of this important topic with specific emphasis on the current guidelines for improving overall efficiency, lowering emissions, and using large share of renewable energy.
Author: Zachary Allen Welz
Author: Zachary Allen Welz
Author: Brian Gabriel Gaudio
Author: Jun Wang
Author: Gary Hardiman
Author: Al-Humairi, Safaa Najah Saud
Author: R. Keith Mobley
Author: Carlos Guedes Soares
Author: Valentine Fontama
Author: International Atomic Energy Agency
Author: Aleksandar Nikolic