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With an initial focus on critical fight management systems, the committee will evaluate applications for AI usage in aviation and aerospace and will propose standards to support implementation and certification

The new Advanced Technology and Training Center (ATTC), which opened yesterday after a ribbon-cutting ceremony, will focus on breakthroughs in condition-based maintenance and the intersection of artificial intelligence (AI), robotics, and automation within that domain.

This document discusses guidelines for the development of Aircraft Systems leveraging AI capabilities, taking into account the overall aircraft operating environment and functions. This includes validation of requirements and verification of the design implementation for certification and product assurance and guidelines with the assessment of safety.

A “toolbox” approach to systematizing the application of AI tools is described. A recommended overall approach for successful application development is suggested.

The recent emergence of powerful artificial intelligence (AI) software development tools promises to reduce the time and effort associated with transforming an expert's knowledge into the operational software system required to implement automatic shutdown avoidance. ...To conduct an evaluation of the applicability of using AI techniques for life support processes, the National Aeronautics and Space Administration (NASA) developed a prototype expert system for automated fault detection, isolation, and correction of a regenerative carbon dioxide (CO2) removal subsystem.

Artificial intelligence (AI), specifically machine learning technology, shows future promise in the VHM space, but it is not currently adequate by itself for high-accuracy analytics.

Innovations in Mobility: Aerospace Digital Summitaerospace mobility leaders convene leverage cutting-edge technology, design, develop safety measures, integrate current regulations, suggest future policies, expand markets, diversify revenue streams.

MIL-STD-1553, Aircraft Internal Time Division Command/Response Multiplex Data Bus, is a military standard which has become one of the basic standardization tools being used by the DOD for avionic interfaces. The standard describes the method of communication and the electrical interface requirements for devices using this standard. The 1 Mbps serial communication bus is the prime integration scheme for electronic devices in military avionics today. This paper addresses the application of the standard to military avionic integrations, describes the scope of the standard, the technical issues resolved in selecting the particular approach, and describes several military avionic examples using the standard.

The exploration of substantial money-saving areas of suitable usage for ECM (Electrochemical Metallizing) on aircraft components which are not in the purview of the original equipment manufacturer. The focus of the paper is upon the less spectacular maintenance opportunities rather than the much discussed repair of landing gear and engine components, all of which are strictly controlled by manufacturer's specifications because of safety considerations. Non-critical parts only require documented engineering specifications which are controlled by internal corporate engineering management. Our objective is to present areas of cost savings available to maintenance departments which have frequently been overlooked in the past. The perceived need is for some presentation of examples of applications which can be found in the normal day-to-day operations of aircraft maintenance activities so that similar opportunities may be identified and acted upon.

The Raytheon Company is testing a new artificial intelligence (AI) tool developed to help determine when the multi-mode radar installed on U.S. Air Force CV-22 tiltrotor aircraft is in need of service.

Major improvements in gas-turbine engine life management and diagnostics can be achieved using artificial intelligence. More effective use of data collected in flight will be possible, post flight maintenance will be expedited, and unnecessary engine removals eliminated. Most importantly, real-time measurement of life consumption would enable the safe life of fracture critical components to be fully used before their retirement. A comprehensive on-board life measurement and diagnostics system for gas-turbine engines is necessary to support probabilistic design and life prediction methodologies for gas turbine engines and the introduction of two-level maintenance in the Air Force.

This SAE Aerospace Recommended Practice (ARP) examines the whole construct of an Engine Health Management (EHM) system. This keystone document gives a top-level view and addresses EHM description, benefits, and capabilities, and provides examples. This ARP purposely addresses a wide range of EHM architectures to demonstrate possible EHM design options. This ARP is not intended as a legal document and does not provide detailed implementation steps, but does address general implementation concerns and potential benefits. Other SAE documents (Aerospace Standards, Aerospace Recommended Practices, and Aerospace Information Reports) address specific component specifications, procedures and "lessons learned".

Space Station preliminary design studies examined the role of advanced automation and artificial intelligence to facilitate ECLSS control and diagnostics. Advanced automation techniques using independent embedded controllers were selected for the initial station, especially for centralized processes such as water recovery, air revitalization, and waste management. This paper examines the role that advanced computer capabilities can play to enhance advanced missions such as growth station, Moon and Mars exploration. The benefits of artificial intelligence are presented from both the expert system and learned system perspective.

This SAE Aerospace Information Report (AIR) offers an overview of the aspects of intellectual property (IP) protection, legislative compliance, business model, and technologies which need to be considered and addressed to implement a data interoperability, secure business model and technology platform to enable prognostics and health management (PHM) in the digital age. While this information report is restricted to the aerospace domain and also to commercial aviation, the concepts are applicable to any other domain that employs data for supporting health management functionality.

The growth in global economies has led to a world that has become much more mobile in the last few decades. The number of enplanements has increased and is expected to continue to do so at an annual average rate of 1.8% through 2039 [1]. Prior to the COVID-19 pandemic, the number of aircraft in service was expected to increase annually to meet the travel demand. Next-generation, more-complex aircraft were scheduled to replace the older aircraft at a pace that still allowed sufficient capacity to meet the increasing demand. The events of 2020 have driven the industry to accelerate retirement of older aircraft while deferring the introduction of new aircraft. While the length of the industry recovery period cannot be predicted, most analysts believe that demand for travel will return once a vaccine is widely available.

During the last year, the principles and predictive tools for this analyzer have been advanced beyond the nominal understanding considered heretofore acceptable for specialized uses of IMS. The importance and need for these advances reside in the creation of automated intelligence (to minimize the need for human resources) and in the formation of tools for the identification of chemicals not previously characterized by IMS. In our findings, IMS spectra were found to arise from considerations both of proton affinities in the initial event of ion creation and of ion stabilities in the non-equilibrium, uni-polar drift region. Aliphatic amines were systematically explored and kinetic measurements were made on ion stabilities and used in modeling the origins of ion mobility spectra. Neural networks were used to explore an existing data base and results revealed hitherto unknown properties of ion mobility spectra.

This paper shows the methods, techniques and codes developed as Artificial Intelligence tools for turboprop maintenance. Diagnostics is the main area of maintenance considered while Expert Systems and Back Propagation Neural Networks are the Artificial Intelligence subjects discussed. The codes allow consideration of the turboprop with different configurations. The Knowledge Base of Expert Systems and the training patterns of Neural Networks are obtained from the matrices of influence obtained through massive use of engine simulation programs. The paper shows examples of both Expert System and Neural Network utilization.

Urban Air Mobility (UAM) vehicles are set to revolutionize the mobility of man and material in near future. One of the key areas to explore is how operational efficiency can be maximized through the next generation Maintenance framework. The UAM operational models may fundamentally change how aircraft maintenance is conducted. An important aspect of UAM is low altitude flying. This enables cellular technologies to be leveraged for air-to-ground as well as air-to-air communication. This implies a low cost, high speed air-to-ground data pipe is available, which leads to the concept of Digital Twin to be leveraged to optimize the operation and maintenance. This enables near real time availability of the state of UAM vehicle and its subsystems, even when it is flying. This paper captures the Next-Gen Maintenance framework along with several value-added services that can be built around it.

This SAE Aerospace Recommended Practice (ARP) offers an overview of the many key processes that are being transformed as the aerospace industry is rapidly digitalizing. The G-31 Electronic Transactions in Aerospace committee has been established to develop standards related to these processes. This report, also known as the “cornerstone” document for the committee, is a comprehensive look at processes associated with commercial aviation. Because of universal convergence of these technologies, the technologies described here are applicable to other domains as well.

The Rocketdyne Division of Rockwell International Corporation is currently researching and evaluating the use of Artificial Intelligence and in particular Expert System technologies for the monitoring of large space-based electric power systems such as NASA's Space Station Freedom (SSF). Power System Security of space borne and lunar based electrical power systems provide unique challenges to power system software design engineers. The major responsibility of Power System Security is the monitoring of the state of health of the Power Distribution System. The role of system security is to ensure that uninterrupted electrical power of high quality is distributed to all the load centers [1]. Voltage, current, power source reliability, and power quality are main components that describe the integrity of an electrical power system and fall into the area of security control.