Model-based fault prediction of ABS system with vehicle stationary.

Shi, John Z., Legate, I., Gu, Fengshou, Fieldhouse, John D. and Ball, Andrew (2008) Model-based fault prediction of ABS system with vehicle stationary. In: Proceedings of the third world congress on engineering asset management and intelligent maintenance systems conference. Springer.

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URI:

https://eprints.hud.ac.uk/id/eprint/7867

Title:

Model-based fault prediction of ABS system with vehicle stationary.

Creators:

Shi, John Z., Legate, I., Gu, Fengshou, Fieldhouse, John D. and Ball, Andrew

Editors:

Jinji, Gao, Lee, Jay, Ni, Jun, Ma, Lin and Mathew, Joseph

Date:

2008

Subjects:

T Technology > T Technology (General)
T Technology > TL Motor vehicles. Aeronautics. Astronautics

Schools:

School of Computing and Engineering
School of Computing and Engineering > Automotive Engineering Research Group
School of Computing and Engineering > Pedagogical Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > High-Performance Intelligent Computing > High Performance Computing Research Group
School of Computing and Engineering > High-Performance Intelligent Computing
School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group

Additional Information:

Paper originally presented at The Third World Congress on Engineering Asset Management and Intelligent Maintenance Systems Conference, held in October 2008, Beijin, China

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Item Type:

Book Chapter

Depositing User:

Sharon Beastall

Date Deposited:

17 Jun 2010 09:26

Revision:

Last Modified:

28 Aug 2021 10:57

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