Title :
Large Sample Detection and Optimal Distribution Models of Vehicle Brake Performance
Author :
Qiao Xiangming ; Xu An ; Liu Shengtian ; Zhao Changli ; Zhang Henghai
Author_Institution :
Shandong Jiaotong Univ., Jinan, China
Abstract :
To improve and optimize vehicle brake safety, using large sample detection, multi-parameter data analysis and mathematical model technical means, actual detection and corresponding sample data statistical analysis are carry out on 6 brake performance of a a given type commercial vehicles, then the optimal fitting distribution and their respective distribution parameters are determined. On this basis, 6 distributive models which correspond to different brake performance are established. Comparing the model calculation results and large sample data actual detection results, the calculation error average can be know only 1.775%. The research is of great significance on evaluating vehicle brake performance accurately, indicating further improvement and optimization directions and improving vehicle brake safety.
Keywords :
automotive engineering; brakes; road accidents; road safety; road traffic; road vehicles; statistical analysis; commercial vehicle; distribution parameter; mathematical model; multiparameter data analysis; optimal distribution models; optimal fitting distribution; road traffic accident; sample data statistical analysis; sample detection; vehicle brake performance; vehicle brake safety; vehicle engineering; Accidents; Axles; Certification; Fitting; Force; Roads; Vehicles; Brake safety; Distributive modeLing; Improvement; Performance detection; Vehicle engineering; optimization;
Conference_Titel :
Digital Manufacturing and Automation (ICDMA), 2011 Second International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-1-4577-0755-1
Electronic_ISBN :
978-0-7695-4455-7
DOI :
10.1109/ICDMA.2011.171
