Title :
Integrated model-based and data-driven fault detection and diagnosis approach for an automotive electric power steering system
Author :
Ghimire, Rajeev ; Sankavaram, Chaitanya ; Ghahari, Alireza ; Pattipati, Krishna ; Ghoneim, Youssef ; Howell, Mark ; Salman, Mutasim
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Connecticut, Storrs, CT, USA
Abstract :
Integrity of electric power steering system is vital to vehicle handling and driving performance. Advances in electric power steering (EPS) system have increased complexity in detecting and isolating faults. In this paper, we propose a hybrid model-based and data-driven approach to fault detection and diagnosis (FDD) in an EPS system. We develop a physics-based model of an EPS system, conduct fault injection experiments to derive fault-sensor measurement dependencies, and investigate various FDD schemes to detect and isolate the faults. Finally, we use an SVM regression technique to estimate the severity of faults.
Keywords :
automotive components; electric sensing devices; electrical engineering computing; fault diagnosis; regression analysis; steering systems; support vector machines; EPS system; FDD schemes; SVM regression technique; automotive electric power steering system; data-driven fault detection; fault diagnosis approach; fault injection; fault-sensor measurement dependency; integrated model-based detection; physics-based model; DC motors; Mathematical model; Power systems; Support vector machines; Torque; Vehicles; Wheels; Electric power steering (EPS); SVM regression; double lane change maneuver; multiway partial least squares (MPLS); support vector machines(SVM); torque sensor;
Conference_Titel :
AUTOTESTCON, 2011 IEEE
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4244-9362-3
DOI :
10.1109/AUTEST.2011.6058760
