Open AccessResearch on trouble diagnosis method of braking anti-lock system based on vehicle data flow
Author(s) -
Tianhao Zhang,
Sheng Xu,
Fujia Liu
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/688/3/033003
Subject(s) - brake , acceleration , artificial neural network , test data , smoothing , computer science , lock (firearm) , simulation , automotive engineering , anti lock braking system , steering wheel , engineering , control theory (sociology) , artificial intelligence , computer vision , physics , control (management) , classical mechanics , programming language , mechanical engineering
Trouble diagnosis method of anti-lock braking system based on vehicle data flow was proposed. The selection principles of trouble diagnosis parameters and trouble modes were analyzed firstly, then 3 trouble modes and 9 parameters including four wheels’ speed, vehicle speed, brake pedal position, steering wheel angle, lateral acceleration and yaw rate were selected based on SAE J1939, 48 sets of test data were obtained by vehicle trouble simulation test. The test data were pretreated to form 32 sets of training samples and 16 sets of test samples, and then neural network models were trained and validated. The accuracy of the test results reach 100% by adjusting and optimizing smoothing factor values. The results show that the neural network model based on vehicle data flow can achieve accurate trouble diagnosis.