Abstract—Electro Mechanical Brake (EMB) is a high
efficiency pure electric vehicle braking system which is based on
the technology of Electronic, machinery, communication
network. Because of the expensive cost and immature key
technology in safety and reliability, the products cannot be
mass-produced on the market at present. Electronic control of
the EMB system needs a variety of sensors information
feedback, therefore, how to correctly detect and diagnose the
faults of the sensors is one of the important problems for the
development of pure electric vehicles. Based on three-loop
control architecture model of EMB system, the sensor fault
detection model is established on the basis of Support Vector
Regression (SVR), and the sensor fault diagnosis model is
established on the basis of Support Vector Classification (SVC).
In order to further improve the Support Vector Machine (SVM)
for fault classification accuracy and fault detection reliability,
the parameters of SVM can be optimized by using Clonal
Selection Algorithm (CSA) and the modified CSA-SVM model
is established. Simulated result of experiment indicates that the
proposed CSA - SVM fault detection rate is increased than the
traditional SVM by 62.5% and the fault classification accuracy
is increased by 10% which laids a solid foundation of
fault-tolerant control technology for the EMB system.
Index Terms—Fault detection, fault diagnosis, support vector
machine, clonal selection, electro mechanical brake.
The authors are with the division of electronic and communication
engineering of Yanbian University, Yanji, China (e-mail:
2013050243@ybu.edu.cn, ynxu*@ ybu.edu.cn).
[PDF]
Cite: Z. J. Yu and Y. N. Xu, "Research of Sensor Fault Detection and Diagnosis for
EMB System Based on CSA-SVM Model," International Journal of Engineering and Technology vol. 7, no. 4, pp. 349-356, 2015.
