As fault diagnosis and prognosis systems in aerospace applications become more capable, the ability to utilize information supplied by them becomes increasingly important. While certain types of vehicle health data can be effectively processed and acted upon by crew or support personnel, others, due to their complexity or time constraints, require either automated or semi-automated reasoning. Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. The newly developed PDM algorithms require suitable software and hardware platforms for testing under realistic fault scenarios. The paper describes the development of such a platform, based on the K11 planetary rover prototype. A variety of injectable fault modes are being investigated for electrical, mechanical, and power subsystems of the testbed, along with methods for data collection and processing. In addition to the hardware platform, a software simulator with matching capabilities has been developed. The simulator allows for prototyping and initial validation of the algorithms prior to their deployment on the K11. The simulator is also available to the PDM algorithms to assist with the reasoning process. A reference set of diagnostic, prognostic, and decision making algorithms is also described, followed by an overview of the current test scenarios and the results of their execution on the simulator.

Rover missions typically involve visiting a set of predetermined waypoints to perform science functions, such as sample collection. Given the communication delay between Earth and the rover, and the possible occurrence of faults, an autonomous decision making system is essential to ensure that the rover maximizes the scientific operations performed without damaging itself further or stalling. This paper presents a modular software architecture for autonomous decision making for rover operations that uses diagnostic and prognostic information to influence mission planning and decision making to maximize the completion of mission objectives. The decision making system consists of separate modules that perform the functions of control, diagnosis, prognosis, and decision making.We demonstrate our implementation of this architecture on a simulated rover testbed.

A proactive approach to identifying and alleviating life-threatening conditions in the aviation system entails a well-defined process of identifying threats, evaluating causes, assessing risks, and implementing appropriate solutions. This process is not a trivial undertaking. It requires continuous monitoring of system performance in a non-punitive culture; learning from normal operational experience; comparing actual performance to expected performance; identifying the precursor events and conditions that foreshadow most accidents; designing appropriate interventions to minimize the risk of their occurrence; and having a system in place to monitor the efficacy of the interventions.

A prognostic system makes it possible to anticipate loss of functionality before it occurs with sufficient lead time to take actions that mitigate the impact of this loss. We focus on the forms of mitigation within the flight vehicle that influence the operational dynamics but do not directly amend the mission plan. Thus, we focus upon the reconfiguration of the feedback control strategy for the flight system. The high degree of complexity in the design and dynamics of modern aircraft is typically handled using a hierarchical control scheme in which there are several levels of control at increasing levels of responsibility: the component level, the subsystem level, and the system level. Our reconfiguration strategy involves mitigating problems that are detected at the component level at both the level in which the fault is detected and higher levels as well. There are, thus, two subproblems to the reconfiguration: (a) an adaptive control problem at the lower level to extend component life and derive new component performance limits, and (b) a supervisory control problem at the higher level to adapt the system controller to maximize system capability while respecting the performance limitations. Since our reconfiguration occurs in the context of a dynamic system, we need to respect the stability implications of the reconfiguration. To address this, we apply bandwidth analyses at the component level and the systems level in a robust performance context. A conservative criterion for stability is to impose rate limits for reconfiguration that insure that undesired, and possibly unmodeled, modes of behavior are not driven by reconfiguration activities. For specific hardware, extensions beyond this conservative approach may be warranted (e.g. to catch faulty behavior) and validated on a case-by-case basis, essentially by extending the component modeling to include a model of behavior under certain types of reconfiguration.

This is a report on a continuing study of automated analyses of experiential textual reports to gain insight into the causal factors of human errors in aviation operations. The intent of this research is to better understand the quantitative and qualitative attributes of an aviation incident, and to identify the respective contributions of their interaction to incident occurrence.

The ability to utilize prognostic system health information in operational decision making, especially when fused with information about future operational, environmental, and mission requirements, is becoming desirable for both manned and unmanned aerospace vehicles. A vehicle capable of evaluating its own health state and making (or assisting the crew in making) decisions with respect to its system health evolution over time will be able to go further and accomplish more mission objectives than a vehicle fully dependent on human control. This paper describes the development of a hardware testbed for integration and testing of prognostics-enabled decision making technologies. Although the testbed is based on a planetary rover platform (K11), the algorithms being developed on it are expected to be applicable to a variety of aerospace vehicle types, from unmanned aerial vehicles and deep space probes to manned aircraft and spacecraft. A variety of injectable fault modes is being investigated for electrical, mechanical, and power subsystems of the testbed. A software simulator of the K11 has been developed, for both nominal and off-nominal operating modes, which allows prototyping and validation of algorithms prior to their deployment on hardware. The simulator can also aid in the decision-making process. The testbed is designed to have interfaces that allow reasoning software to be integrated and tested quickly, making it possible to evaluate and compare algorithms of various types and from different sources. Currently, algorithms developed (or being developed) at NASA Ames - a diagnostic system, a prognostic system, a decision-making module, a planner, and an executive - are being used to complete the software architecture and validate design of the testbed.

Modern aircraft are producing data at an unprecedented rate with hundreds of parameters being recorded on a second by second basis. The data can be used for studying the condition of the hardware systems of the aircraft and also for studying the complex interactions between the pilot and the aircraft. NASA is developing novel data mining algorithms to detect precursors to aviation safety incidents from these data sources. This talk will cover the theoretical aspects of the algorithms and practical aspects of implementing these techniques to study one of the most complex dynamical systems in the world: the national airspace.

The LEARN Project explores the creation of novel concepts and processes with the potential to create new capabilities in aeronautics research through awards to the external community including university and industry teams. The LEARN Project incorporates a competitive review process of the external teams’ proposals to develop integrated solutions for complex technical problems captured in the ARMD strategic thrusts, followed by short duration activities for feasibility assessment. Follow-on phases of the most promising ideas are also funded. LEARN also utilizes challenges and prizes to the external community.  With these processes, NASA funds also help catalyze investments from the aerospace and non-aerospace communities toward solving problems aligned with NASA interests.

The NASA Aeronautics Research Institute (NARI) has been established to achieve the LEARN Project’s goals.  NARI will complement other ARMD efforts in seeking early-stage innovative concepts applicable to a broad spectrum of aeronautical challenges in the nation’s air transportation system by sponsoring research solicitations and by hosting future competitive challenges. The Institute will coordinate these efforts and communicate the outcome of the research conducted to interested parties both internal and external to NASA. ARMD’s goal is to mature the new concepts in order to infuse them into current ARMD research programs, to enable new avenues of aeronautics research that are not currently supported by ARMD program and project funds, or to achieve practical application by the aeronautics community.

This report describes a cooperative experiment conducted by ONERA and NASA, with the support of Airbus S.A.S. and easyJet Airline Company, Ltd. The study evaluated the benefits of two distinctly different methodologies for analyzing the same set of digital flight-recorded data. The experiment analyzed a set of easyJet commercial-flight data with both typical Flight Operational Quality Assur-ance (FOQA) software of an airline (in this case, AirFASE, developed by Airbus and Teledyne) and The Morning Report of Atypical Flights (developed by NASA). The study demonstrated the feasibility and potential value of using The Morning Report tool in conjunction with the FOQA airline tool and also showed the complementarities of the results produced by the two approaches.