Details

Autor(en) / Beteiligte

• Zhang, Changxin
• Xu, Xin
• Zhang, Xinglong
• Zhou, Xing
• Lu, Yang
• Zhang, Yichuan

Titel

A data-driven fault detection and diagnosis method via just-in-time learning for unmanned ground vehicles

Ist Teil von

• Automatika, 2023-04, Vol.64 (2), p.277-290

Ort / Verlag

Ljubljana: Taylor & Francis

Erscheinungsjahr

2023

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Fault detection and diagnosis technologies for unmanned ground vehicles are important for ensuring safety and reliability. Due to the complexity and uncertainty of unmanned ground vehicles, it is challenging to realize accurate and fast fault detection and diagnosis. For the purpose of solving the data-driven fault detection and diagnosis problems of unmanned ground vehicles, improving the diagnostic accuracy and shortening the training time, a novel fault detection and diagnosis method is proposed, which is called JITGP-ELM. In the proposed method, a model estimator based on the just-in-time Gaussian process is designed for the online residual generation to cope with the dynamics and nonlinearity of systems. A fault classifier using Extreme Learning Machine is designed for fault identification with residuals extracted by the just-in-time Gaussian process modelling. The proposed method has online adaptability, noise-resistant ability, and high generalization. A field test on a real unmanned ground vehicle's steering-by-wire system demonstrates the effectiveness of the proposed method.