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

TL;DR

This paper introduces an integrated fault detection and identification framework for multi-spacecraft inspection missions, linking guidance, control, and fault diagnosis through an information-driven cost functional, and demonstrating its effectiveness via simulations.

Contribution

It proposes a novel global-to-local FDI framework that unifies guidance, control, and fault diagnosis using a shared information-driven cost functional for multi-spacecraft systems.

Findings

Reliable fault localization and classification demonstrated in simulations.

Effective adaptive thresholding captures dynamic mission conditions.

Unified information-driven approach enhances autonomous inspection resilience.

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.