摘要
传统的汽车零部件渐变可靠性演化分析方法在分析上准确度较低,为此设计一种基于遗传算法的汽车零部件渐变可靠性演化分析方法。建立可靠性指标以及故障类型分布函数,选取合适的疲劳理论,判断零件形变的等效应力,并采用累积损伤法预测某一汽车零部件所产生的疲劳损伤,计算零部件临界失效状态,采用遗传算法确定汽车零部件剩余寿命,以此完成基于遗传算法的汽车零部件渐变可靠性演化分析。实验对比结果表明,此次设计的基于遗传算法的汽车零部件渐变可靠性演化分析方法比传统分析方法分析准确度高,很好的反应车辆在磨损状态下可靠性变化情况,为车辆零件的磨损渐变可靠性提供理论参考。
The traditional reliability analysis method for gradual reliability evolution of automobile parts has low analysis accuracy.To this end,a genetic algorithm-based gradual reliability evolution analysis method for automobile parts is designed.Establish reliability index and fault type distribution function,select appropriate fatigue theory,determine equivalent stress of part deformation,and use cumulative damage method to predict fatigue damage generated by a certain automotive component,calculate critical failure state of component,and adopt genetic The algorithm determines the remaining life of automobile parts,and thus completes the evolution analysis of the reliability of automobile parts based on genetic algorithm.The experimental comparison results show that the evolutionary reliability analysis method of automotive parts based on genetic algorithms designed this time is more accurate than the traditional analysis method,and it reflects the reliability changes of the vehicle in the wear state,which is the wear of the vehicle parts.Gradient reliability provides a theoretical reference.
作者
汤志华
TANG Zhi-hua(Guangzhou City Polytechnic,Guangzhou 510405)
出处
《环境技术》
2020年第3期153-157,173,共6页
Environmental Technology
关键词
遗传算法
汽车零部件
渐变
可靠性
磨损
genetic algorithm
automotive parts
gradual change
reliability
wear
