摘要
为提升现有无人车车载电源故障检测专家系统的准确性和诊断效率,使用改进的深度优先搜索算法进行推理机的设计;为解决静态树模型难以应用于复杂车辆电源系统的问题,设计了动态树生成算法;运用面向对象的程序设计方法,设计了无人车电源故障检测专家系统,实现故障原因推理和故障定位,并对该专家系统进行了模拟和实车测试。测试结果表明,该专家系统可以准确、快速地推理故障原因并定位故障位置。
In order to improve accuracy and diagnosis efficiency of automotive power supply failure detection expert system of existing autonomous vehicle, a modified depth-first search algorithm is used to design inference machine. In order to address the difficulty of the static tree model in applying to complicated vehicle power supply system, dynamic tree generation algorithm is designed. Object-oriented programming approach is utilized to design power supply failure detection expert system of autonomous vehicle, for fault reasoning and positioning, the expert system is then simulated and tested on vehicle. Test results show that this expert system can accurately and rapidly infer fault cause and locate fault position.
作者
杨一鸣
汪贵平
Yang Yiming;Wang Guiping(Chang’an University, Xi’an 710064)
出处
《汽车技术》
CSCD
北大核心
2019年第6期30-35,共6页
Automobile Technology
基金
国家重点研发计划项目(2018YFB0105104)
陕西省重点研发计划重点项目(2017ZDL-G-2-6)
关键词
面向对象
专家系统
电源检测
无人车
Object oriented
Expert system
Power diagnosis
Autonomous vehicle
