Data-driven fault diagnosis for an automobile suspension system by using a clustering based method

DOI10.1016/j.jfranklin.2014.03.004zbMath1290.93127OpenAlexW2009219152MaRDI QIDQ2017252

Guang Wang, Shen Yin

Publication date: 25 June 2014

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2014.03.004

zbMATH Keywords

benchmarkautomobile suspension systemfault diagnosis and monitoring

Mathematics Subject Classification ID

Application models in control theory (93C95) Fault detection; testing in circuits and networks (94C12)

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Cites Work

• Fault diagnosis for the vertical three-tank system via high-order sliding-mode observation
• A linear matrix inequality approach to robust fault detection filter design of linear systems with mixed time-varying delays and nonlinear perturbations
• Robust actuator fault diagnosis scheme for satellite attitude control systems
• Robust fault detection with missing measurements

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