Fault Detection for Heterogeneous Multi-Agent Systems with Unknown Dynamics Using Distributed Autoencoder

This paper presents a novel approach addressing fault detection challenges for multi-agent systems through a machine-learning method using recurrent autoencoders. The main advantage lies in its ability to handle heterogeneous multi-agent systems with unknown dynamics. The approach features a distributed detection architecture based on a cluster representation that depends solely on the agents’ relative outputs, integrating stable image representation and orthogonal projection. Unlike traditional observer-based methods, the fault detection framework employs distributed autoencoders for residual generation, offering a data-driven and model-free solution. The autoencoders are carefully designed for effective time-series data learning, incorporating gated recurrent units and neural networks. Simulation results validate the effectiveness of the proposed method, demonstrating excellent fault detection capabilities and highlighting the promising extension to more complex and generic systems.