Information-Driven Fault Detection and Identification for Multi-Agent Spacecraft Systems: Collaborative On-Orbit Inspection Mission

2025-11-11

AI Summary

To address degraded reliability and autonomy in low-Earth-orbit multi-spacecraft cooperative inspection missions caused by sensor, actuator, and state estimator faults, this paper proposes a task-aware, information-driven hierarchical fault detection and identification (FDI) framework. The method unifies task-level information gain objectives with spacecraft pose dynamics and sensor models to formulate a differentiable information cost functional; online identification of multiple fault types is achieved via high-order gradient analysis of this functional, while a geometrically and task-adaptive thresholding mechanism ensures robustness under dynamic environmental conditions. Simulation results demonstrate accurate localization and classification of representative faults under uncertainty, significantly enhancing system robustness and autonomy. The proposed framework provides a unified, scalable FDI architecture for multi-spacecraft on-orbit inspection.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

This work presents a global-to-local, task-aware fault detection and identification (FDI) framework for multi-spacecraft systems conducting collaborative inspection missions in low Earth orbit. The inspection task is represented by a global information-driven cost functional that integrates the sensor model, spacecraft poses, and mission-level information-gain objectives. This formulation links guidance, control, and FDI by using the same cost function to drive both global task allocation and local sensing or motion decisions. Fault detection is achieved through comparisons between expected and observed task metrics, while higher-order cost-gradient measures enable the identification of faults among sensors, actuators, and state estimators. An adaptive thresholding mechanism captures the time-varying inspection geometry and dynamic mission conditions. Simulation results for representative multi-spacecraft inspection scenarios demonstrate the reliability of fault localization and classification under uncertainty, providing a unified, information-driven foundation for resilient autonomous inspection architectures.

Problem

• Detecting faults in multi-spacecraft systems during inspection missions
• Identifying sensor, actuator, and estimator faults using task metrics
• Adapting fault detection to dynamic orbital inspection conditions

Innovation

• Global-to-local task-aware FDI framework for spacecraft
• Information-driven cost functional integrates sensors and objectives
• Adaptive thresholding handles dynamic mission conditions