Model-based Sensor Fault Diagnosis of Vehicle Suspensions with a Support Vector Machine
In this paper, a means of generating residuals based on a quarter-car model and evaluating them using a support vector machine (SVM) is proposed. The proposed model-based residual generator shows very robust performance regardless of unknown road surface conditions. In addition, an SVM classifier without empirically set thresholds is used to evaluate the residuals. The proposed method is expected to reduce the effort required to design fault diagnosis algorithms. While an unknown input observer is used to generate the residual, the relative velocity of the vehicle suspension is obtained additionally. The proposed algorithm is verified using commercial vehicle simulator Carsim with Matlab & Simulink. As a result, the fault diagnosis algorithm proposed in this paper can detect sensor faults that cannot be detected by a limit checking method and can reduce the effort required when designing algorithms.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/12299138
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Supplemental Notes:
- Copyright © 2019, The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg.
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Authors:
- Jeong, Kicheol
- Choi, Seibum
- Publication Date: 2019-10
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: pp 961-970
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Serial:
- International Journal of Automotive Technology
- Volume: 20
- Issue Number: 5
- Publisher: Korean Society of Automotive Engineers
- ISSN: 1229-9138
- EISSN: 1976-3832
- Serial URL: http://link.springer.com/journal/12239
Subject/Index Terms
- TRT Terms: Algorithms; Automobiles; Fault location; In vehicle sensors; Mathematical models; Simulation; Suspension systems
- Uncontrolled Terms: Support vector machines
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01719941
- Record Type: Publication
- Files: TRIS
- Created Date: Oct 22 2019 2:42PM