Enhancing Spacecraft Resilience: NASA GRAMS Phase I Success Drives Autonomous Innovation

Executive Summary

NASA’s Graceful Architecture for Mitigation of System Failures (GRAMS) project, developed under SBIR Phase I by VISIMO, addresses the challenges of autonomous failure detection and response in long-duration space missions. GRAMS leverages machine learning and digital twin technology to create a modular cognitive architecture capable of identifying and mitigating spacecraft failures in real time. The Phase I project validated the system’s feasibility through rigorous simulated testing and deployment on the Spaceborne Computer-2 (SBC-2) Test and Development System. This success led to a Phase II award, positioning GRAMS for real-world flight testing aboard the International Space Station (ISS).

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Client

NASA

Challenge

Communication Delays and Earth-Based Limitations

In deep-space missions, communication delays can range from several seconds to over 22 minutes, creating a significant barrier for real-time problem-solving. These delays leave spacecraft and their crews vulnerable to escalating failures that require immediate intervention, making reliance on Earth-based operations untenable for missions to Mars or other distant targets.

Deep-Space Failure Risks

Spacecraft systems face a variety of failure risks due to the unpredictable and hostile nature of space. Hardware malfunctions, exposure to extreme radiation, micrometeoroid impacts, and cascading system failures present significant challenges. Traditional systems that rely on predefined failure scenarios often fall short when addressing novel or “unknown unknown” issues, leaving missions at risk.

Need for Real-Time Autonomous Systems

Traditional failure mitigation strategies depend heavily on redundant hardware or human intervention. Redundant systems add significant weight and cost, while human reliance is impractical for deep-space missions due to resource constraints and communication lags. An effective solution requires autonomous systems that can detect, analyze, and address failures without external input, ensuring mission continuity.

Problem Overview

Limitations of Current Spacecraft Systems

Modern spacecraft typically rely on pre-programmed fault detection methods, such as failure trees, which only address known scenarios. While this approach is effective for anticipated issues, it cannot account for novel or unexpected anomalies. Redundant hardware is often used as a safeguard, but this increases the overall weight and cost of missions, reducing payload capacity for scientific instruments or other mission-critical assets. These limitations create a growing need for systems capable of operating autonomously and dynamically in the unpredictable environment of deep space.

Consequences of Failure in Deep-Space Missions

Failures in spacecraft systems can have catastrophic consequences, ranging from mission aborts to the loss of crewed vehicles. In long-duration missions, a minor malfunction can quickly escalate into a critical issue if not addressed promptly. For example, a pressure leak might start as a small anomaly but, without immediate action, could lead to life-threatening conditions. Additionally, delays in resolving failures can lead to the loss of millions of dollars in equipment and years of scientific research.

Key Requirements for Autonomous Solutions

For missions beyond Earth orbit, spacecraft must incorporate systems that detect anomalies in real time, adapt to emerging conditions, and provide actionable solutions without requiring Earth-based assistance. These systems must also be scalable and modular to address a wide variety of missions, from robotic probes to crewed exploration vehicles. By integrating intelligent failure detection, simulation capabilities, and real-time decision-making, autonomous solutions can ensure mission success even in the face of unpredictable failures.

Solution

Overview of GRAMS Cognitive Architecture

VISIMO’s GRAMS system is a modular cognitive architecture designed to detect, analyze, and mitigate spacecraft anomalies in real time. By leveraging machine learning and digital twin technology, GRAMS is capable of identifying both known and novel failures, simulating their impacts, and providing actionable solutions to mitigate risks.

Key Components of GRAMS

• Risk Identification Algorithm (RIA): Detects system anomalies and classifies their severity.
• Alert Generator (AG): Prioritizes and routes alerts to reduce the crew’s cognitive load.
• Failure Simulator (FS): Generates synthetic failures for model training and adaptation.
• Digital Twin (DT): Simulates spacecraft systems to predict behavior and identify risks.
• Action Recommender (AR): Suggests optimal corrective actions for failure mitigation.

GRAMS is built with modularity in mind, allowing it to adapt to diverse mission profiles while maintaining a high level of reliability.

Implementation

Simulated Testing on ISS-Relevant Environments

The GRAMS system was rigorously tested in simulated environments designed to replicate conditions aboard the International Space Station (ISS). The Digital Twin modeled a range of failure scenarios, including pressure leaks, sensor malfunctions, and pump failures, demonstrating the system’s ability to detect and resolve anomalies efficiently.

Deployment on Spaceborne Computer-2 (SBC-2)

GRAMS was deployed on Hewlett Packard Enterprise’s SBC-2 Test and Development System, an Earth-based replica of the ISS computing environment. This successful deployment validated GRAMS’ compatibility with space-grade hardware and its readiness for Phase II flight testing.

Modular Design for Scalability

GRAMS’ plug-and-play architecture enables seamless integration with existing spacecraft systems, providing flexibility for both NASA missions and commercial applications.

Results

Exceeded Precision and Recall Benchmarks

The Risk Identification Algorithm (RIA) achieved precision of 0.895 and recall of 0.870, surpassing industry benchmarks for anomaly detection in spacecraft systems.

Validated Digital Twin in Simulating Complex Failures

The Digital Twin accurately modeled failure scenarios, enabling robust training of machine learning models to handle both anticipated and unanticipated anomalies.

Secured Transition to Phase II Testing on ISS

Based on Phase I’s success, GRAMS has been approved for Phase II testing aboard the ISS, marking a significant milestone in its development journey.

Applications

NASA Missions

• Deep-Space Crewed Missions: GRAMS provides autonomous anomaly detection and mitigation for Mars and lunar exploration missions.
• Robotic Interplanetary Missions: Enhances the resilience of autonomous probes by enabling real-time failure management.

Commercial Space Applications

• Low Earth Orbit Platforms: Supports commercial space station operations by optimizing system performance and safety.
• Satellite Operations: Identifies and mitigates hardware failures in orbit, reducing downtime and mission risks.

Terrestrial Applications

• Unmanned Aerial Systems: Provides real-time diagnostics and adaptive failure management for defense and logistics operations.
• High-Risk Facility Monitoring: Detects and mitigates failures in nuclear reactors, chemical plants, and other critical infrastructure.

Conclusion

GRAMS’ Potential for Revolutionizing Spacecraft Operations

The GRAMS Phase I project successfully validated the system’s ability to enhance spacecraft resilience through intelligent failure detection and mitigation. Phase II will focus on real-world validation aboard the ISS, paving the way for widespread adoption in NASA missions, commercial aerospace, and terrestrial applications.

Positioning for Success in Phase II and Beyond

GRAMS is set to become a cornerstone of autonomous systems for deep-space exploration and high-risk operational environments, driving innovation across sectors.

Call to Action

Discover how GRAMS is transforming autonomous spacecraft systems. Contact VISIMO today to learn more or explore collaboration opportunities.