In August 2014, the committee on Defense Materials Manufacturing and Infrastructure convened a workshop to discuss issues related to applying materials state awareness to condition-based maintenance and system life cycle management. The workshop was structured around three focal topics: (1) advances in metrology and experimental methods, (2) advances in physics-based models for assessment, and (3) advances in databases and diagnostic technologies. This report summarizes the discussions and presentations from this workshop.
In August 2014, the committee on Defense Materials Manufacturing and Infrastructure convened a workshop to discuss issues related to applying materials state awareness to condition-based maintenance and system life cycle management. The workshop was structured around three focal topics: (1) advances in metrology and experimental methods, (2) advances in physics-based models for assessment, and (3) advances in databases and diagnostic technologies. This report summarizes the discussions and presentations from this workshop.
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.
The first complete introduction to health monitoring, encapsulating both technical information and practical case studies spanning the breadth of the subject.Written by a highly-respected figure in structural health monitoring, this book provides readers with the technical skills and practical understanding required to solve new problems encountered in the emerging field of health monitoring. The book presents a suite of methods and applications in loads identification (usage monitoring), in-situ damage identification (diagnostics), and damage and performance prediction (prognostics). Concepts in modelling, measurements, and data analysis are applied through real-world case studies to identify loading, assess damage, and predict the performance of structural components, as well as examine engine components, automotive accessories, aircraft parts, spacecraft components, civil structures and defence system components.In particular the book, provides the reader with a fundamental and practical understanding of the material, discusses models demonstrating the physical basis for health monitoring techniques, gives a detailed review of the best practices in dynamic measurements including sensing, presents numerous data analysis techniques using model- and signal-based methods, discusses case studies involving real-world applications of health monitoring, offers end-of-chapter problems to enhance the study of the topic for students and instructors; and includes an accompanying website with MATLAB programs providing hands-on training to readers for writing health monitoring model simulation and data analysis algorithms.Health Monitoring of Structural Materials and Components is an excellent introductory text for newcomers to the subject as well as an excellent study tool for students and lecturers. Practitioners and researchers, those with a greater understanding and application of the technical skills involved, will also find this essential reading as a reference text to address current and future challenges in this field. The wide variety of case studies will appeal to a broad spectrum of engineers in the aerospace, civil, mechanical, machinery and defence communities. In particular the book: provides the reader with a fundamental and practical understanding of the material; discusses models demonstrating the physical basis for health monitoring techniques; gives a detailed review of the best practices in dynamic measurements including sensing; presents numerous data analysis techniques using model- and signal-based methods; discusses case studies involving real-world applications of health monitoring; offers end-of-chapter problems to enhance the study of the topic for students and instructors; and includes an accompanying website with MATLAB programs providing hands-on training to readers for writing health monitoring model simulation and data analysis algorithms.
This contributed volume presents state-of-the-art advances in logistics theory in various fields as well as case studies. The book reports on a number of recently conducted studies in the Dinalog and the EffizienzCluster LogistikRuhr, thus bridging the gap between different perspectives of theoretical and applied research. A selection of theoretical topics, practical examples, case studies and project reports is presented in this volume. The editors carefully selected contributions from a wide variety of projects, which were carried out in both the Dinalog cluster and the Effizienzcluster LogistikRuhr. The contributions are grouped in five main sections, each representing key domains in the evolution of logistics and supply chain management: sustainability, urban logistics, value chain management, IT-based innovation, knowledge management. This book is intended for both researchers and practitioners in the field of logistics and supply chain management, to serve as an important source of information for further research as well as to stimulate further innovation.
The ability of the United States Air Force (USAF) to keep its aircraft operating at an acceptable operational tempo, in wartime and in peacetime, has been important to the Air Force since its inception. This is a much larger issue for the Air Force today, having effectively been at war for 20 years, with its aircraft becoming increasingly more expensive to operate and maintain and with military budgets certain to further decrease. The enormously complex Air Force weapon system sustainment enterprise is currently constrained on many sides by laws, policies, regulations and procedures, relationships, and organizational issues emanating from Congress, the Department of Defense (DoD), and the Air Force itself. Against the back-drop of these stark realities, the Air Force requested the National Research Council (NRC) of the National Academies, under the auspices of the Air Force Studies Board to conduct and in-depth assessment of current and future Air Force weapon system sustainment initiatives and recommended future courses of action for consideration by the Air Force. Examination of the U.S. Air Force's Aircraft Sustainment Needs in the Future and Its Strategy to Meet Those Needs addresses the following topics: Assess current sustainment investments, infrastructure, and processes for adequacy in sustaining aging legacy systems and their support equipment. Determine if any modifications in policy are required and, if so, identify them and make recommendations for changes in Air Force regulations, policies, and strategies to accomplish the sustainment goals of the Air Force. Determine if any modifications in technology efforts are required and, if so, identify them and make recommendations regarding the technology efforts that should be pursued because they could make positive impacts on the sustainment of the current and future systems and equipment of the Air Force. Determine if the Air Logistics Centers have the necessary resources (funding, manpower, skill sets, and technologies) and are equipped and organized to sustain legacy systems and equipment and the Air Force of tomorrow. Identify and make recommendations regarding incorporating sustainability into future aircraft designs.
This book introduces Industrial AI in multiple dimensions. Industrial AI is a systematic discipline which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance. Combined with the state-of-the-art sensing, communication and big data analytics platforms, a systematic Industrial AI methodology will allow integration of physical systems with computational models. The concept of Industrial AI is in infancy stage and may encompass the collective use of technologies such as Internet of Things, Cyber-Physical Systems and Big Data Analytics under the Industry 4.0 initiative where embedded computing devices, smart objects and the physical environment interact with each other to reach intended goals. A broad range of Industries including automotive, aerospace, healthcare, semiconductors, energy, transportation, mining, construction, and industrial automation could harness the power of Industrial AI to gain insights into the invisible relationship of the operation conditions and further use that insight to optimize their uptime, productivity and efficiency of their operations. In terms of predictive maintenance, Industrial AI can detect incipient changes in the system and predict the remains useful life and further to optimize maintenance tasks to avoid disruption to operations.
