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 aerospace industry has been adopting avionics architectures to take advantage of advances in computer engineering. Integrated Modular Avionics (IMA), as described in ARINC 653, distributes functional modules into a robust configuration interconnected with a "virtual backplane" data communications network. Each avionics module's function is defined in software compliant with the APEX Application Program Interface. The Avionics Full-Duplex Ethernet (AFDX) network replaces the point-topoint connections used in previous distributed systems with "virtual links". This network creates a command and data path between avionics modules with the software and network defining the active virtual links over an integrated physical network. In the event of failures, the software and network can perform complex reconfigurations very quickly, resulting in a very robust system.In this paper, suitable architectures, standards and conceptual designs for IMA computational modules and the virtual backplane are defined and analyzed for applicability to spacecraft. The AFDX network standard is examined in detail and compared with IEEE 802.3 Ethernet. A reference design for the "Ancillary Sensor Network" (ASN) is outlined based on the IEEE 1451 "Standard for a Smart Transducer Interface for Sensors and Actuators" using realtime operating systems, time deterministic AFDX and wireless LAN technology. Strategies for flight test and operational data collection related to Systems Health Management are developed, facilitating vehicle ground processing. Finally, a laboratory evaluation defines performance metrics and test protocols and summarizes the results of AFDX network tests, allowing identification of design issues and determination of ASN subsystem scalability, from a few to potentially thousands of smart and legacy sensors. 12
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Underlying all operations in the National Airspace System (NAS) is the concept of safety. Safety, as defined by acceptable levels of risk, is to be maintained at all times. The real-time safety monitoring (RTSM) framework is under development to provide an automated system to quantify safety in the NAS, estimate the current level of safety, and predict the future evolution of safety and the occurrence of events that pose an increased risk to flights so that these occurrences can be managed strategically rather than mitigated reactively. This paper presents the mathematical framework, the models, and the monitoring and prediction algorithms used to achieve this. RTSM computes safety as expressed through a set of safety margins based on user-defined safety metrics, thresolds, and events. Sources of uncertainty are modeled and propagated through the predictions in order to compute the probabilistic evolution of safety and the probability of events that introduce increased risk to operations. A prototype implementation is discussed and results demonstrating feasibility are presented. The results highlight the kinds of predictions that can be computed and the fidelity that is currently achieved.
NASA's exploration program envisions the utilization of a Deep Space Habitat (DSH) for human exploration of the space environment in the vicinity of Mars and/or asteroids. Communication latencies with ground control of as long as 20+ minutes make it imperative that DSH operations be highly autonomous, as any telemetry-based detection of a systems problem on Earth could well occur too late to assist the crew with the problem. A DSH-based development program has been initiated to develop and test the automation technologies necessary to support highly autonomous DSH operations.One such technology is a fault management tool to support performance monitoring of vehicle systems operations and to assist with real-time decision making in connection with operational anomalies and failures. Toward that end, we are developing Advanced Caution and Warning System (ACAWS), a tool that combines dynamic and interactive graphical representations of spacecraft systems, systems modeling, automated diagnostic analysis and root cause identification, system and mission impact assessment, and mitigation procedure identification to help spacecraft operators (both flight controllers and crew) understand and respond to anomalies more effectively. In this paper, we describe four major architecture elements of ACAWS: Anomaly Detection, Fault Isolation, System Effects Analysis, and Graphic User Interface (GUI), and how these elements work in concert with each other and with other tools to provide fault management support to both the controllers and crew. We then describe recent evaluations and tests of ACAWS on the DSH testbed. The results of these tests support the feasibility and strength of our approach to failure management automation and enhanced operational autonomy.
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