Model-based approaches have proven fruitful in the design and implementation of intelligent systems that provide automated diagnostic functions. A wide variety of models are used in these approaches to represent the particular domain knowledge, including analytic state-based models, input-output transfer function models, fault propagation models, and qualitative and quantitative physics-based models. Diagnostic applications are built around three main steps: observation, comparison, and diagnosis. If the modeling begins in the early stages of system development, engineering models such as fault propagation models can be used for testability analysis to aid definition and evaluation of instrumentation suites for observation of system behavior. Analytical models can be used in the design of monitoring algorithms that process observations to provide information for the second step in the process, comparison of expected behavior of the system to actual measured behavior. In the final diagnostic step, reasoning about the results of the comparison can be performed in a variety of ways, such as dependency matrices, graph propagation, constraint propagation, and state estimation. Realistic empirical evaluation and comparison of these approaches is often hampered by a lack of standard data sets and suitable testbeds. In this paper we describe the Advanced Diagnostics and Prognostics Testbed (ADAPT) at NASA Ames Research Center. The purpose of the testbed is to measure, evaluate, and mature diagnostic and prognostic health management technologies. This paper describes the testbed's hardware, software architecture, and concept of operations. A simulation testbed that

The Earth Observing One satellite, launched in November 2000, is an active earth science observation platform. This paper reports on the development of an infusion experiment in which the Livingstone 2 Model-Based Diagnostic engine is deployed on Earth Observing One, to demonstrate the capability of monitoring the nominal operation of the spacecraft under command of an on-board planner, and to demonstrate on-board diagnosis of spacecraft failures. Design and development of the experiment, specification and validation of diagnostic scenarios and models, and the integration and test approach are presented.

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An Autonomous Science Agent has been flying onboard the Earth Observing One Spacecraft since 2003.This software enables the spacecraft to autonomously detect and responds to science events occurring on the Earth such as volcanoes, flooding, and snow melt. The package includes AI-based software systems that perform science data analysis, deliberative planning, and run-time robust execution. This software is in routine use to fly the EO-1 mission. In this paper we briefly review the agent architecture and discuss lessons learned from this multi-year flight effort pertinent to deployment of software agents to critical applications.

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.