A Deep Learning-Based Fault Diagnosis of Leader-Following Systems

This paper develops a multisensor data fusion-based deep learning algorithm to locate and classify faults in leader-following multiagent system. First, sequences of one-dimensional collected from multiple sensors followers are fused into two-dimensional image. Then, the image is employed train convolution neural network with batch normalisation layer. The trained can three typical fault types: actuator limitation fault, sensor failure communication failure. Moreover, exist both leaders followers, be identified through indicating that developed diagnosis distributed. effectiveness learning-based demonstrated via Quanser Servo 2 rotating inverted pendulums leader-follower protocol. From experimental results, classification accuracy reach 98.9%.