基于模糊综合评判的乘用车制动系统失效风险严重程度分析
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摘要
为了准确评估乘用车制动系统失效风险严重程度,提高车辆行车安全,对我国汽车制动系统缺陷涉及的部件和失效模式进行了分析,并针对真空助力器和制动管路失效,设计、开展了实车试验。试验数据分析表明,在制动系统不同的失效状态下,平均制动减速度、制动距离和最大踏板力存在明显差异。因此将以上3个指标与制动系统失效模式结合构建事故树,确立了影响制动系统失效风险严重程度的指标体系;利用G1法和模糊综合评判法,得出了制动系统失效风险的严重程度。结果表明,制动系统真空助力器失效的严重程度等级为较高,制动管路泄漏的严重程度等级为高,与实际案例的风险严重程度评估结果一致。
This paper has established an indicator system that can influence the risk severity of the passenger vehicle braking system failure through fuzzy comprehensive evaluation so as to heighten the safety probability of the safety of the vehicle transportation. For the said purpose,we have first of all analyzed all the constituent components concerning the failure modes associated with the defects of the automobile brake system of the country by using the data of China's defective automobile recall from 2004 to 2017. The results of our assessment show that the number of the recalls caused by the brake pedal should be responsible for the failure primarily; and the next two constituent factors concerening failure modes should be the number of recalls caused by the vacuum booster. And the third two constituent factor should be taken to account for the brake line. Thus,it can be summarized that the above illustrated 3 constituent factors or key prob-lems that should be responsible for the main defective factors.And,next,the key efforts we would like to make is to manage to design a real vehicle testing braking system to resolve the 3 failure problems. What can be found from our experimental inspection data is that the actual failure situation or status-in-situ of the brake system turn out to be various,for example,there can be found an apparent difference among the average brake deceleration,the brake distance and the maximum pedal pressure in the abovementioned situation. Thus,we have taken the above said 3 indicators and the failure modes into account to work out a new system of reference indicators. And,thirdly,we have managed to establish an indicator system which may influence the failure risks of the brake system through the function of the event tree.For,the failure risk severity of the brake system can be worked out and determined by using the G1 method and the fuzzy comprehensive evaluation system on the basis of the optimistically chosen facotrs. Thus,the results of practical application of the assessment system can all demonstrate that the severity of the vacuum assisting failure of the brake system and the severity of the brake pipeline leakage all prove rather high. Therefore,the results of our experimental testing turn out to be consistent with the actual risk severity and the given fuzzy comprehensive evaluation method can be taken to evaluate the severity of braking system failure successfully and purposely.
This paper has established an indicator system that can influence the risk severity of the passenger vehicle braking system failure through fuzzy comprehensive evaluation so as to heighten the safety probability of the safety of the vehicle transportation. For the said purpose,we have first of all analyzed all the constituent components concerning the failure modes associated with the defects of the automobile brake system of the country by using the data of China's defective automobile recall from 2004 to 2017. The results of our assessment show that the number of the recalls caused by the brake pedal should be responsible for the failure primarily; and the next two constituent factors concerening failure modes should be the number of recalls caused by the vacuum booster. And the third two constituent factor should be taken to account for the brake line. Thus,it can be summarized that the above illustrated 3 constituent factors or key prob-lems that should be responsible for the main defective factors.And,next,the key efforts we would like to make is to manage to design a real vehicle testing braking system to resolve the 3 failure problems. What can be found from our experimental inspection data is that the actual failure situation or status-in-situ of the brake system turn out to be various,for example,there can be found an apparent difference among the average brake deceleration,the brake distance and the maximum pedal pressure in the abovementioned situation. Thus,we have taken the above said 3 indicators and the failure modes into account to work out a new system of reference indicators. And,thirdly,we have managed to establish an indicator system which may influence the failure risks of the brake system through the function of the event tree.For,the failure risk severity of the brake system can be worked out and determined by using the G1 method and the fuzzy comprehensive evaluation system on the basis of the optimistically chosen facotrs. Thus,the results of practical application of the assessment system can all demonstrate that the severity of the vacuum assisting failure of the brake system and the severity of the brake pipeline leakage all prove rather high. Therefore,the results of our experimental testing turn out to be consistent with the actual risk severity and the given fuzzy comprehensive evaluation method can be taken to evaluate the severity of braking system failure successfully and purposely.
引文
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[3] WANG Yan(王琰),HUANG Guozhong(黄国忠),SONG Cunyi(宋存义),et al. Risk assessment model of automobile defects based on the gray theory[J]. Journal of University of Science and Technology Beijing(北京科技大学学报),2009,31(9):1178-1182.
[4] YE Wei(叶伟),HUANG Tianbin(黄天彬). Renovated multilevel fuzzy integrated hazards assessment system for the airport apron safety[J]. Journal of Safety and Environment(安全与环境学报),2011,11(3):231-235.
[5] ZHANG Weiliang(张卫亮),XIAO Lingyun(肖凌云),LIU Yahui(刘亚辉). Risk assessment guidelines of automobile steering system defects and automobile recall cases[J]. Journal of Automotive Safety and Energy(汽车安全与节能学报),2013,4(4):361-366.
[6] CHEN Yuzhong(陈玉忠),DONG Honglei(董红磊),LI Chenfeng(李晨风),et al. Risk assessment of vehicle ABS defects based on a modified fuzzy FMECA method[J]. Journal of Automotive Safety and Energy(汽车安全与节能学报),2016,7(3):265-271.
[7] DONG Honglei(董红磊),CHEN Yuzhong(陈玉忠),ZHANG Jinhuan(张金换). Defect identification of automotive product based on fuzzy risk matrix[J]. Journal of Automotive Safety and Energy(汽车安全与节能学报),2016,7(4):377-381.
[8] TAYLOR D. Drivers’galvanic skin response and the risk of accident[J]. Ergonomics,1964,7(4):439-451.
[9] LEFEVRE S,VASQUEZ D,LAUGIER C. A survey on motion prediction and risk assessment for intelligent vehicles[J]. ROBOMECH Journal,2014,1:1.
[10] DONG Honglei(董红磊),CHEN Yuzhong(陈玉忠),LUNing(吕宁),et al. Risk ranking of automobile brake system defects based on Borda ranking method[J]. Standard Science(标准科学),2016(6):58-61.
[11] XU Zhengjie(徐征捷),ZHANG Youpeng(张友鹏),SU Hongsheng(苏宏升). Application of risk assessment on fuzzy comprehensive evaluation method based on the cloud model[J]. Journal of Safety and Environment(安全与环境学报),2014,14(2):69-72.
[12] LI Lin(李霖),ZHU Xichan(朱西产),MA Zhixiong(马志雄). Driver brake reaction time under real traffic risk scenarios[J]. Automotive Engineering(汽车工程),2014,36(10):1225-1229.
[13] DUAN J,LI R,HOU L,et al. Driver braking behavior analysis to improve autonomous emergency braking systems in typical Chinese vehicle-bicycle conflicts[J]. Accident Analysis and Prevention,2017,108:74-82.
[14] LIU Jian(刘建),ZHENG Shuangzhong(郑双忠),DENG Yunfeng(邓云峰),et al. Weight determination of indexes in evaluation of emergency response ability based on G1[J]. China Safety Science Journal(中国安全科学学报),2006,16(1):30-33.
[2] ADSMS L. Review of the literature on obstacle:development of the braking versus steering[R]. Michigan:University of Michigan Transportation Research Institute,1994.
[3] WANG Yan(王琰),HUANG Guozhong(黄国忠),SONG Cunyi(宋存义),et al. Risk assessment model of automobile defects based on the gray theory[J]. Journal of University of Science and Technology Beijing(北京科技大学学报),2009,31(9):1178-1182.
[4] YE Wei(叶伟),HUANG Tianbin(黄天彬). Renovated multilevel fuzzy integrated hazards assessment system for the airport apron safety[J]. Journal of Safety and Environment(安全与环境学报),2011,11(3):231-235.
[5] ZHANG Weiliang(张卫亮),XIAO Lingyun(肖凌云),LIU Yahui(刘亚辉). Risk assessment guidelines of automobile steering system defects and automobile recall cases[J]. Journal of Automotive Safety and Energy(汽车安全与节能学报),2013,4(4):361-366.
[6] CHEN Yuzhong(陈玉忠),DONG Honglei(董红磊),LI Chenfeng(李晨风),et al. Risk assessment of vehicle ABS defects based on a modified fuzzy FMECA method[J]. Journal of Automotive Safety and Energy(汽车安全与节能学报),2016,7(3):265-271.
[7] DONG Honglei(董红磊),CHEN Yuzhong(陈玉忠),ZHANG Jinhuan(张金换). Defect identification of automotive product based on fuzzy risk matrix[J]. Journal of Automotive Safety and Energy(汽车安全与节能学报),2016,7(4):377-381.
[8] TAYLOR D. Drivers’galvanic skin response and the risk of accident[J]. Ergonomics,1964,7(4):439-451.
[9] LEFEVRE S,VASQUEZ D,LAUGIER C. A survey on motion prediction and risk assessment for intelligent vehicles[J]. ROBOMECH Journal,2014,1:1.
[10] DONG Honglei(董红磊),CHEN Yuzhong(陈玉忠),LUNing(吕宁),et al. Risk ranking of automobile brake system defects based on Borda ranking method[J]. Standard Science(标准科学),2016(6):58-61.
[11] XU Zhengjie(徐征捷),ZHANG Youpeng(张友鹏),SU Hongsheng(苏宏升). Application of risk assessment on fuzzy comprehensive evaluation method based on the cloud model[J]. Journal of Safety and Environment(安全与环境学报),2014,14(2):69-72.
[12] LI Lin(李霖),ZHU Xichan(朱西产),MA Zhixiong(马志雄). Driver brake reaction time under real traffic risk scenarios[J]. Automotive Engineering(汽车工程),2014,36(10):1225-1229.
[13] DUAN J,LI R,HOU L,et al. Driver braking behavior analysis to improve autonomous emergency braking systems in typical Chinese vehicle-bicycle conflicts[J]. Accident Analysis and Prevention,2017,108:74-82.
[14] LIU Jian(刘建),ZHENG Shuangzhong(郑双忠),DENG Yunfeng(邓云峰),et al. Weight determination of indexes in evaluation of emergency response ability based on G1[J]. China Safety Science Journal(中国安全科学学报),2006,16(1):30-33.