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ISBN 10: 1025091418 ISBN 13: 9781025091419
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Paperback or Softback. Condizione: New. A Methodology for Simulating the Joint Strike Fighter's (JSF) Prognostics and Health Management System. Book.
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ISBN 10: 1025091418 ISBN 13: 9781025091419
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Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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ISBN 10: 102508909X ISBN 13: 9781025089096
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware.
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Da: Biblios, Frankfurt am main, HESSE, Germania
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Da: CitiRetail, Stevenage, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. The Autonomic Logistics System Simulation (ALSim) was developed to provide decision makers a tool to make informed decisions regarding the Joint Strike Fighter's (JSF) Autonomic Logistics System (ALS). The benefit to ALS is that it provides real time maintenance information to ground maintenance crews, supply depots, and air planners to efficiently manage the availability of JSF aircraft. This thesis effort focuses on developing a methodology to model the Prognostics and Health Management (PHM) component of ALS. The PHM component of JSF is what actually monitors the aircraft status. To develop a PHM methodology to use in ALSim a neural network approach is used. Notional JSF prognostic signals were generated using an interactive Java application, which were then used to build and train a neural network. The neural network is trained to predict when a component is healthy and/or failing. The results of the neural network analysis are meaningful failure detection times and false alarm rates. The analysis presents a batching approach to train the neural network, and looks at the sensitivity of the results to batch size and the neural network classification rule used. The final element of the research is implementing the PHM methodology in ALSim.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Da: CitiRetail, Stevenage, Regno Unito
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. The Autonomic Logistics System Simulation (ALSim) was developed to provide decision makers a tool to make informed decisions regarding the Joint Strike Fighter's (JSF) Autonomic Logistics System (ALS). The benefit to ALS is that it provides real time maintenance information to ground maintenance crews, supply depots, and air planners to efficiently manage the availability of JSF aircraft. This thesis effort focuses on developing a methodology to model the Prognostics and Health Management (PHM) component of ALS. The PHM component of JSF is what actually monitors the aircraft status. To develop a PHM methodology to use in ALSim a neural network approach is used. Notional JSF prognostic signals were generated using an interactive Java application, which were then used to build and train a neural network. The neural network is trained to predict when a component is healthy and/or failing. The results of the neural network analysis are meaningful failure detection times and false alarm rates. The analysis presents a batching approach to train the neural network, and looks at the sensitivity of the results to batch size and the neural network classification rule used. The final element of the research is implementing the PHM methodology in ALSim.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.