Upgrading military engine test cells with advanced diagnostic and troubleshooting capabilities will play a critical role in increasing aircraft availability and test cell effectiveness while simultaneously reducing engine opening and maintenance costs. Sophisticated performance and mechanical anomaly detection and fault classification algorithms utilizing thermodynamic, statistical, and empirical engine models are now being implmented as part of a United States Air Force Advanced Test Cell Upgrade Initiative. Under this program, a comprehensive set of real- time and post-test diagnostic software modules, including sensor validation algorithms, performance fault classification techniques and vibration feature analysis are being developed. An automated troubleshooting guide is also being implemented to streamline the troubleshooting process for both inexperienced and experienced technicians. This artificial intelligence based tool enhances the conventional troubleshooting tree architecture by incorporating probability of occurrence statistics to optimize the troubleshooting path. This paper describes the development and implementation of the F404 engine test cell upgrade at the Jacksonville Naval Air Station.

Annotation This is Volume 1 of five volumes that comprise the proceedings of the June 2002 conference, sponsored by the International Gas Turbine Institute (IGTI), a technical institute of the American Society of Mechanical Engineers. The purpose of the conference was to facilitate international exchange and development of educational and technical information related to the design, application, manufacture, operation, maintenance, and environmental impact of all types of gas engines. With an emphasis upon the need for more efficient, cleaner, and more reliable gas turbines, the approximately 130 articles cover various technical aspects of aircraft engines; coal, biomass, and alternative fuels; combustion and fuels; education; electric power; and vehicular and small turbomachines. There is no subject index. Annotation c. Book News, Inc., Portland, OR (booknews.com).

Integrated Vehicle Health Management (IVHM) is a relatively new subject, with its roots back in the space sector of the early 1990s. Although many of the papers written around that time did not refer to it as IVHM, the fundamental principles of considering an integrated end-to-end system to monitor the overall health of the asset were clearly visible. As the subject of Integrated Vehicle Health Management (IVHM) and its associated technologies have grown up, businesses are making the transformation from selling a product to selling a service. This can be viewed as a positive disruption, as a relatively small technology breakthrough is being brought to market for a large business benefit. The sequence “sense—acquire—transfer—analyze—act “ feeds the information (processed data) on the asset’s health into the Operations or Management control center. Here, decisions can be made on maintenance actions with knowledge of the supply chain status, MRO loading, etc., provided by Maintenance and Logistics systems. Undoubtedly, a much more efficient and economical modus operandi. This book brings together a collection of twenty –two SAE International Technical papers on this very theme, organized according to specific areas of interest: • Engines • Airframes • Electrical Power Systems • Supporting Systems • Architecture They were selected by Dr. Ian K. Jennions, Director of IVHM Center at Cranfield University, in the UK. Dr. Jennions was also the editor of three other books on Integrated Vehicle Health Management, published by SAE International: • IVHM: Perspectives on an Emerging Field • IVHM: Business Case Theory and Practice • IVHM: The Technology

Integrated Vehicle Health Management (IVHM) is the unified capability of a system of systems (SoS) to assess the current or future state of the member system health, and integrate it within a framework of available resources and operational demand. As systems complexities have increased, so have system support costs, driven by more frequent and often enigmatic subsystem failures. IVHM strategies can be used to mitigate these issues by taking a Systems of Systems view. Combined with advanced decision support methods, this approach can be used to more effectively predict, isolate, schedule, and repair failed subsystems, reducing platform support costs and minimizing platform down time. Integrated Vehicle Health Management- System of Systems Integration brings together ten seminal SAE technical papers addressing the challenges and solutions to maintaining highly complex vehicles. The strategy requires that the IVHM system must provide actionable decision support to operators and maintainers, informing platform operational capabilities and maintenance procedures. The goal is to prevent a given component from degrading to the point of failure or predictable impending failure. Specifications should also reflect a common means for communicating this information to other health- ready IVHM system components.

"Foundations and Practical Applications of Cognitive Systems and Information Processing" presents selected papers from the First International Conference on Cognitive Systems and Information Processing, held in Beijing, China on December 15-17, 2012 (CSIP2012). The aim of this conference is to bring together experts from different fields of expertise to discuss the state-of-the-art in artificial cognitive systems and advanced information processing, and to present new findings and perspectives on future development. This book introduces multidisciplinary perspectives on the subject areas of Cognitive Systems and Information Processing, including cognitive sciences and technology, autonomous vehicles, cognitive psychology, cognitive metrics, information fusion, image/video understanding, brain-computer interfaces, visual cognitive processing, neural computation, bioinformatics, etc. The book will be beneficial for both researchers and practitioners in the fields of Cognitive Science, Computer Science and Cognitive Engineering. Fuchun Sun and Huaping Liu are both professors at the Department of Computer Science & Technology, Tsinghua University, China. Dr. Dewen Hu is a professor at the College of Mechatronics and Automation, National University of Defense Technology, Changsha, China.