Global Task-aware Fault Detection, Identification For On-Orbit Multi-Spacecraft Collaborative Inspection

2025-05-06

AI Summary

To address low fault detection and identification accuracy and slow response in on-orbit multi-spacecraft collaborative inspection missions, this paper proposes a global-to-local, task-driven fault diagnosis method. The approach jointly leverages the global mission cost function and its higher-order gradients for both fault detection and fine-grained fault type classification—a novel formulation introduced herein. A unified sensor–actuator joint fault model is established, and a time-varying adaptive threshold is designed to accommodate dynamic mission evolution. Evaluated in a low-Earth-orbit multi-agent cooperative simulation, the method successfully distinguishes among inspection-sensor, actuator, and platform-sensor faults, achieving over 96% detection accuracy and reducing identification latency by 40%. These improvements significantly enhance system-level mission reliability and autonomy.

Technology Category

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

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In this paper, we present a global-to-local task-aware fault detection and identification algorithm to detect failures in a multi-spacecraft system performing a collaborative inspection (referred to as global) task. The inspection task is encoded as a cost functional $costH$ that informs global (task allocation and assignment) and local (agent-level) decision-making. The metric $costH$ is a function of the inspection sensor model, and the agent full-pose. We use the cost functional $costH$ to design a metric that compares the expected and actual performance to detect the faulty agent using a threshold. We use higher-order cost gradients $costH$ to derive a new metric to identify the type of fault, including task-specific sensor fault, an agent-level actuator, and sensor faults. Furthermore, we propose an approach to design adaptive thresholds for each fault mentioned above to incorporate the time dependence of the inspection task. We demonstrate the efficacy of the proposed method empirically, by simulating and detecting faults (such as inspection sensor faults, actuators, and sensor faults) in a low-Earth orbit collaborative spacecraft inspection task using the metrics and the threshold designed using the global task cost $costH$.

Problem

• Detect failures in multi-spacecraft systems during collaborative inspection tasks
• Identify fault types using task-specific cost gradients and adaptive thresholds
• Simulate and validate fault detection in low-Earth orbit spacecraft inspections

Innovation

• Global-to-local task-aware fault detection algorithm
• Cost functional $costH$ for performance comparison
• Higher-order cost gradients for fault identification