Propulsion IVHM technology experiment overview

Meyer, Claudia; Fulton, Chris; Maul, B.; Chicatelli, Amy; Cannon, Howard; Bajwa, Anupa; Balaban, Edward; Wong, Edmond

doi:10.1109/aero.2003.1235497

Cited by 10 publications

(9 citation statements)

References 4 publications

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“…In that context, the PITEX experiment has demonstrated the application of Livingstonebased diagnosis to the main propulsion feed subsystem of the X-34 space vehicle [1,8], and LPF has been successfully applied to the PITEX model of X-34. This particular Livingstone model consists of 535 components and 823 attributes, 250 transitions, compiling to 2022 propositional clauses.…”

Section: Resultsmentioning

confidence: 99%

“…-The PITEX baseline scenario combines one nominal and 29 failure scenarios, derived from those used by the PITEX team for testing Livingstone, as documented in [8]. This scenario covers 89 states.…”

Section: Resultsmentioning

confidence: 99%

“…In comparison, their verification approach is based on running a fixed, limited set of test cases, as detailed in [8]. LPF shows great potential for radically expanding the number of tested behaviors, with a modest additional effort from the users.…”

Section: Discussionmentioning

confidence: 99%

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Simulation-Based Verification of Autonomous Controllers via Livingstone PathFinder

Lindsey

Pecheur

2004

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. AI software is often used as a means for providing greater autonomy to automated systems, capable of coping with harsh and unpredictable environments. Due in part to the enormous space of possible situations that they aim to address, autonomous systems pose a serious challenge to traditional test-based verification approaches. Efficient verification approaches need to be perfected before these systems can reliably control critical applications. This publication describes Livingstone PathFinder (LPF), a verification tool for autonomous control software. LPF applies state space exploration algorithms to an instrumented testbed, consisting of the controller embedded in a simulated operating environment. Although LPF has focused on NASA's Livingstone model-based diagnosis system applications, the architecture is modular and adaptable to other systems. This article presents different facets of LPF and experimental results from applying the software to a Livingstone model of the main propulsion feed subsystem for a prototype space vehicle.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Simulation-Based Verification of Autonomous Controllers via Livingstone PathFinder

Lindsey

Pecheur

2004

Tools and Algorithms for the Construction and Analysis of Systems

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“…This weakness increased the amount of development required to deploy L2 on EO-1. The RTI software from PITEX [8] was revised for EO-1 to support timeouts for real-time delays, and the transient modeling methodology requires many additional transient modes and transitions in the model.…”

Section: Diagnosismentioning

confidence: 99%

“…L2 also has been used on several applications projects, including monitoring and diagnosis of the liquid propulsion feed system for the X-34 reusable launch vehicle [8]; and of the electro-mechanical actuators on the X-37 vehicle [9]. These X-vehicle programs were cancelled before reaching flight; however, the X-34 L2 experiment was completed using high-fidelity simulation data.…”

Section: Introductionmentioning

confidence: 99%

Lessons Learned in the Livingstone 2 on Earth Observing One Flight Experiment

Hayden

Sweet²,

Shulman

2005

Infotech@Aerospace

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The Livingtone 2 (L2) model-based diagnosis software is a reusable diagnostic tool for monitoring complex systems. In 2004, L2 was integrated with the JPL Autonomous Sciencecraft Experiment (ASE) and deployed on-board Goddard's Earth Observing One (EO-1) remote sensing satellite, to monitor and diagnose the EO-1 space science instruments and imaging sequence. This paper reports on lessons learned from this flight experiment.The goals for this experiment, including validation of minimum success criteria and of a series of diagnostic scenarios, have all been successfully met. Long-term operations in space are on-going, as a test of the maturity of the system, with L2 performance remaining flawless. L2 has demonstrated the ability to track the state of the system during nominal operations, detect simulated abnormalities in operations and isolate failures to their root cause fault. Specific advances demonstrated include diagnosis of ambiguity groups rather than a single fault candidate; hypothesis revision given new sensor evidence about the state of the system; and the capability to check for faults in a dynamic system without having to wait until the system is quiescent.The major benefits of this advanced health management technology are to increase mission duration and reliability through intelligent fault protection, and robust autonomous operations with reduced dependency on supervisory operations from Earth. The work-load for operators will be reduced by telemetry of processed state-of-health information rather than raw data. The long-term vision is that of making diagnoses available to the onboard planner or executive, allowing autonomy software to re-plan in order to work around known component failures.For a system that is expected to evolve substantially over its lifetime, as for the International Space Station, the model-based approach has definite advantages over rule-based expert systems and limitchecking fault protection systems, as these do not scale well. The model-based approach facilitates reuse of the L2 diagnostic software; only the model of the system to be diagnosed and telemetry monitoring software has to be rebuilt for a new system or expanded for a growing system. The hierarchical L2 model supports modularity and expandability, and as such is suitable solution for integrated system health management as envisioned for systems-of-systems.

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The livingstone model of a main propulsion system

Bajwa

Sweet²

2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652)

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Propulsion IVHM technology experiment overview

Cited by 10 publications

References 4 publications

Simulation-Based Verification of Autonomous Controllers via Livingstone PathFinder

Simulation-Based Verification of Autonomous Controllers via Livingstone PathFinder

Lessons Learned in the Livingstone 2 on Earth Observing One Flight Experiment

The livingstone model of a main propulsion system

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