1,720,953 research outputs found
A compound novel data-driven and reliability-based predictive maintenance framework for ship machinery systems
Get PDFShipping is a major driving force of the global economy, as seen by the 90% of the volume of the yearly trade transported by ships. As a result, shipping has a significant financial, environmental impact. Maritime maintenance can be used to safeguard shipping’s impact by improving safety and avoiding accidents. This is especially true, when considering that nearly 22% of all the accidents between 2011 and 2017 were attributed to improper maintenance. Consequently, maritime maintenance can be used as a hazard mitigation tool, improve ship safety by reducing accidents. Modern maritime maintenance is best applied through predictive maintenance schemes, which take advantage of the developments of Shipping 4.0. Under this scope, the goal of this thesis is the development of a compound novel data-driven and reliability-based predictive maintenance framework for ship machinery system. The novel framework tackles the areas of maritime predictive maintenance holistically by addressing the topics of critical equipment selection, data preparation, fault detection and diagnostics. Each of the framework’s topics are developed in individual methodologies and assessed in unique case studies demonstrating their effectiveness in the respective tasks. Initially, the methodology for the critical equipment selection includes the novel combination of Fault Tree Analysis with data clustering for the identification of critical equipment, as applied in the case of an LNG Carrier. As a result, the most critical components are identified by taking into account reliability indices and repair costs for the considered components. Identifying critical components improves safety, as it focuses the maintenance efforts in items whose failures can have economic consequences and safety implications. Next, the methodology for the data preparation is developed, which includes the novel integration of the kNN and MICE algorithms for the imputation of missing data. Combining these two algorithms al lows for the novel integration a data-driven approach with domain knowledge in a single imputation model. The imputation methodology is applied in the case of a Chemical Tanker, showcasing the effectiveness of the novel method against a pure MICE and pure kNN approach. The treatment of missing values can improve ship safety, as it safeguards information contained within datasets and leads to more accurate condition assessing models. Following that, a novel Fault Detection methodology is established based on Expected Behaviour models, using Machine Learning, and Exponentially Weighted Moving Average control charts. This methodology aims at detecting developing faults in their early stages while avoiding the shortcomings of black-box approaches and having reasonable data requirements for training. Lastly, the diagnostics methodology is formed, which includes the novel integration of pre-processing and Machine Learning-based Fault detection with a diagnostic network using Bayesian Networks. The resulting methodology can identify the root cause of a detected fault, without using black-box Neural Network approaches, nor complicated and time-consuming physics-based models. Even though the Fault Detection and diagnostics methodologies are developed individually, they are both evaluated in the same case of a Bulk Carrier. The use of the same case study was dictated by restrictions in collecting additional data and by the use of the output of the Fault Detection methodology in the diagnostics. The detection of developing faults and the identification of their root-cause has a profound effect on ship safety, while also allowing for targeted maintenance actions.Shipping is a major driving force of the global economy, as seen by the 90% of the volume of the yearly trade transported by ships. As a result, shipping has a significant financial, environmental impact. Maritime maintenance can be used to safeguard shipping’s impact by improving safety and avoiding accidents. This is especially true, when considering that nearly 22% of all the accidents between 2011 and 2017 were attributed to improper maintenance. Consequently, maritime maintenance can be used as a hazard mitigation tool, improve ship safety by reducing accidents. Modern maritime maintenance is best applied through predictive maintenance schemes, which take advantage of the developments of Shipping 4.0. Under this scope, the goal of this thesis is the development of a compound novel data-driven and reliability-based predictive maintenance framework for ship machinery system. The novel framework tackles the areas of maritime predictive maintenance holistically by addressing the topics of critical equipment selection, data preparation, fault detection and diagnostics. Each of the framework’s topics are developed in individual methodologies and assessed in unique case studies demonstrating their effectiveness in the respective tasks. Initially, the methodology for the critical equipment selection includes the novel combination of Fault Tree Analysis with data clustering for the identification of critical equipment, as applied in the case of an LNG Carrier. As a result, the most critical components are identified by taking into account reliability indices and repair costs for the considered components. Identifying critical components improves safety, as it focuses the maintenance efforts in items whose failures can have economic consequences and safety implications. Next, the methodology for the data preparation is developed, which includes the novel integration of the kNN and MICE algorithms for the imputation of missing data. Combining these two algorithms al lows for the novel integration a data-driven approach with domain knowledge in a single imputation model. The imputation methodology is applied in the case of a Chemical Tanker, showcasing the effectiveness of the novel method against a pure MICE and pure kNN approach. The treatment of missing values can improve ship safety, as it safeguards information contained within datasets and leads to more accurate condition assessing models. Following that, a novel Fault Detection methodology is established based on Expected Behaviour models, using Machine Learning, and Exponentially Weighted Moving Average control charts. This methodology aims at detecting developing faults in their early stages while avoiding the shortcomings of black-box approaches and having reasonable data requirements for training. Lastly, the diagnostics methodology is formed, which includes the novel integration of pre-processing and Machine Learning-based Fault detection with a diagnostic network using Bayesian Networks. The resulting methodology can identify the root cause of a detected fault, without using black-box Neural Network approaches, nor complicated and time-consuming physics-based models. Even though the Fault Detection and diagnostics methodologies are developed individually, they are both evaluated in the same case of a Bulk Carrier. The use of the same case study was dictated by restrictions in collecting additional data and by the use of the output of the Fault Detection methodology in the diagnostics. The detection of developing faults and the identification of their root-cause has a profound effect on ship safety, while also allowing for targeted maintenance actions
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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