Federated fault diagnosis method for collaborative self-diagnosis and cross-robot peer diagnosis
Yan Qin, Ouyang Wang
TL;DR
This paper introduces a new method for diagnosing faults in multi-robot systems using federated learning, allowing robots to both self-diagnose and check each other for issues.
Contribution
The novel contribution is a federated learning-based fault diagnosis framework for multi-robot systems that enables both self-diagnosis and cross-robot peer diagnosis.
Findings
The self-diagnosis model accurately identifies faults in individual robot components.
The mutual diagnosis model effectively detects system-wide faults by analyzing group behavior consistency.
Abstract
In multi-robot collaboration, individual failures can propagate to other robots due to the topological coupling between them. Existing fault diagnosis models are designed for single robots and fail to meet the practical requirements of multi-robot scenarios. To address this, this study develops a federated learning-based fault self-diagnosis model for individual robots and a multi-robot mutual diagnosis model that accounts for group behavior consistency. This approach effectively isolates faulty robots in multi-robot systems. Initially, each robot’s local data is encoded using the Gramian Angular Field (GAF) to generate two-dimensional time-frequency plots, creating local fault datasets. Next, a federated learning framework is established, where fault models for different robots are pre-trained using the local fault datasets. The local model parameters from multiple robots are then…
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Taxonomy
TopicsFault Detection and Control Systems · Anomaly Detection Techniques and Applications · Elevator Systems and Control