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

Liu, Xiaoxu, Lu, Xin and Gao, Zhiwei (2022) A Deep Learning-Based Fault Diagnosis of Leader-Following Systems. IEEE Access, 10. pp. 18695-18706. ISSN 2169-3536

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Official URL: https://doi.org/10.1109/ACCESS.2022.3151155


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%.

Item Type: Article
Additional Information: Funding information: This work was supported in part by the National Natural Science Foundation of China under Grant 62003218, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515110234, in part by the Shenzhen Science and Technology Program under Grant RCBS20200714114921371, and in part by the Natural Science Foundation of Top Talent of Shenzhen Technology University (SZTU) under Grant 2020106.
Uncontrolled Keywords: batch normalization, convolution neural network, data-driven, Deep learning, distributed, fault diagnosis, image fusion, leader-following system, multisensor data fusion, sliding window data sampling
Subjects: G400 Computer Science
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 01 Mar 2022 15:01
Last Modified: 01 Mar 2022 15:15
URI: http://nrl.northumbria.ac.uk/id/eprint/48579

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