Abstract
This paper develops a multisensor data fusion-based deep learning algorithm to locate and classify faults in a leader-following multiagent system. First, sequences of one-dimensional data collected from multiple sensors of followers are fused into a two-dimensional image. Then, the image is employed to train a convolution neural network with a batch normalisation layer. The trained network can locate and classify three typical fault types: the actuator limitation fault, the sensor failure and the communication failure. Moreover, faults can exist in both leaders and followers, and the faults in leaders can be identified through data from followers, indicating that the developed deep learning fault diagnosis is distributed. The effectiveness of the deep learning-based fault diagnosis algorithm is demonstrated via Quanser Servo 2 rotating inverted pendulums with a leader-follower protocol. From the experimental results, the fault classification accuracy can reach 98.9%.
Highlights
Multiagent systems have been widely used in the fields of multiunmanned aerial vehicles [1], smart grids [2], multirobot cooperative formation [3] and sensor networks [4]
The developed deep learning fault diagnosis is applied to Quanser Servo 2 rotating inverted pendulums to demonstrate the effectiveness
99%, which is a considerable improvement compared with the BP and the traditional convolutional neural networks (CNNs)
Summary
Multiagent systems have been widely used in the fields of multiunmanned aerial vehicles [1], smart grids [2], multirobot cooperative formation [3] and sensor networks [4]. The reliability of multiagent systems depends on the performance of each agent and their communications. A fault in one agent can degrade the performance of its neighbours via communication (e.g., [5]), which threatens the whole multiagent system. There is a stringent demand to locate and identify the faults in multiagent systems at an early stage. Fault diagnosis of multiagent systems has received extensive attention and undergone rapid development [11,12,13,14,15]
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