Requirements for an Autonomous Control Architecture for Advanced Life Support Systems

Biswas, Gautam; Bonasso, Pete; Abdelwahed, Sherif; Manders, Eric; Wu, Jian; Kortenkamp, David; Bell, Scott

doi:10.4271/2005-01-3010

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…This allows for many hours of physiological data to be collected on these individuals to train automation to recognize their physiological patterns and tendencies. Similar concepts for automated monitoring have already been proposed for other parameters during space operations (Kitts and Swartout, 1998;Biswas et al, 2005), including life support (Schreckenghost et al, 1998).…”

Section: Discussionmentioning

confidence: 94%

Assessment of Physiological Responses During Field Science Task Performance: Feasibility and Future Needs

Hill

Caldwell

2022

Front. Physiol.

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ObjectiveBy understanding the physiological demands of different types of tasks that will be performed during extravehicular activity (EVA) on Mars, human performance safety risks can be mitigated. In addition, such understanding can assist in planning EVAs with an appropriate balance of human health and safety with scientific mission return.BackgroundThis paper describes the results of a study of technical feasibility performed within a Mars human research analog, with participants conducting scientifically relevant planetary science sample analysis and return tasks in two distinct field locations.MethodsThe authors collected heart rate, respiration rate, and heart rate variability (HRV) data, using commercial off-the-shelf hardware and software from study participants as they performed field science tasks within a concept of operations for a Mars science return human expedition mission. These data were remotely monitored, shared in real time, and later analyzed to identify different responses to different tasks in order to determine if there were any predictable or consistent patterns among participants.ResultsIt was ultimately determined that, while differences exist between responses to tasks, they are highly subject to multiple sources of individual variability, dynamics of evolving field science tasks, and demands of a demanding physical environment. Further, distributional analyses of participants do not support parametric statistical analysis techniques.ConclusionThe authors conclude that the physiology of individual astronauts should be extensively studied and modeled to support individualized automated monitoring tools for each crew member that is sent to Mars. Application: Physiological monitoring for specialized populations will require significant individual-level analysis, baselining, and bootstrap statistical methods to enable appropriate human performance determinations.

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Section: Discussionmentioning

confidence: 94%

Assessment of Physiological Responses During Field Science Task Performance: Feasibility and Future Needs

Hill

Caldwell

2022

Front. Physiol.

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“…We have presented the algorithms we have developed for supporting this ISHM architecture. This approach has been successfully applied to managing components of an Advanced Life Support system (ALSS) for a simulated 90 day manned mission to a lunar habitat [5].…”

Section: Discussionmentioning

confidence: 99%

“…Decision making schemes use the predicted measures to compute when performance and resource levels may decrease below a value, where the system functionality can be maintained in a safe and reliable manner [5]. The safety measures with implied thresholds also indicate when to terminate system operation and go into maintenance mode.…”

Section: Prognosis Decision Making and Controlmentioning

confidence: 99%

“…In work we have done thus far [5], prognosis is looked upon as a combination of prediction with an assumed POF degradation model that is evaluated in the context of a realistic resource management strategy. Therefore, our measures compute the predicted use of resources (e.g., power consumption and water consumption).…”

Section: Prognosis Decision Making and Controlmentioning

confidence: 99%

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A Hierarchical Model-based approach to Systems Health Management

Biswas

Mahadevan

2007

2007 IEEE Aerospace Conference

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Integrated Systems Health Management (ISHM) provides the ability to maintain system health and performance over the life of a system. For safety-critical systems, ISHM must maintain safe operations while increasing availability by preserving functionality and minimizing downtime. This paper discusses a model-based approach to ISHM that combines fault detection, isolation and identification, fault-adaptive control, and prognosis into a common framework. At the core of this framework are a set of component oriented physical system models. By incorporating physics of failure models into component models the dynamic behavior of a failing or degrading system can be derived by simulation. Current state information predicts future behavior and performance of the system to guide decision making on system operation and maintenance. 1,2We demonstrate our approach on the fluid loop of a secondary sodium cooling loop of a nuclear reactor system. We model the fluid loop at the system level and a generic pump system at the component level. Monitoring and diagnosis at the system level may point to faults and degradation in components, e.g., the pump. A more detailed analysis of the pump using structural and material models may point to physics of failure models that explain degradation in the pump components. This information can form the basis for prognostic analysis that forms the core methodology for monitoring system performance and decision making for maintenance.

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