Hierarchical Fault Diagnosis and Health Monitoring in Satellites Formation Flight

Current spacecraft health monitoring and fault-diagnosis practices involve around-the-clock limit-checking and trend analysis on large amount of telemetry data. They do not scale well for future multiplatform space missions due the size of the telemetry data and an increasing need to make the long-duration missions cost-effective by limiting the operations team personnel. The need for efficient utilization of telemetry data achieved by employing machine learning and reasoning algorithms has been pointed out in the literature for enhancing diagnostic performance and assisting the less-experienced personnel in performing monitoring and diagnosis tasks. In this paper, we develop a systematic and transparent fault-diagnosis methodology within a hierarchical fault-diagnosis framework for a satellites formation flight. We present our proposed hierarchical decomposition framework through a novel Bayesian network, whose structure is developed from the knowledge of component health-state dependencies. We have developed a methodology for specifying the network parameters that utilizes both node fault-diagnosis performance data and domain experts' beliefs. Our proposed model development procedure reduces the demand for expert's time in eliciting probabilities significantly. Our proposed approach provides the ground personnel with an ability to perform diagnostic reasoning across a number of subsystems and components coherently. Due to the unavailability of real formation flight data, we demonstrate the effectiveness of our proposed methodology by using synthetic data of a leader-follower formation flight architecture. Although our proposed approach is developed from the satellite fault-diagnosis perspective, it is generic and is targeted toward other types of cooperative fleet vehicle diagnosis problems

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Towards Real-Time, On-Board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

For unmanned aerial systems (UAS) to be successfully deployed and integrated within the national airspace, it is imperative that they possess the capability to effectively complete their missions without compromising the safety of other aircraft, as well as persons and property on the ground. This necessity creates a natural requirement for UAS that can respond to uncertain environmental conditions and emergent failures in real-time, with robustness and resilience close enough to those of manned systems. We introduce a system that meets this requirement with the design of a real-time onboard system health management (SHM) capability to continuously monitor sensors, software, and hardware components. This system can detect and diagnose failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; and (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software. We call this approach rt-R2U2, a name derived from its requirements. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual flight data from the NASA Swift UAS

Assessment team report on flight-critical systems research at NASA Langley Research Center

The quality, coverage, and distribution of effort of the flight-critical systems research program at NASA Langley Research Center was assessed. Within the scope of the Assessment Team's review, the research program was found to be very sound. All tasks under the current research program were at least partially addressing the industry needs. General recommendations made were to expand the program resources to provide additional coverage of high priority industry needs, including operations and maintenance, and to focus the program on an actual hardware and software system that is under development