为改进轮式装载机可靠性及耐久性设计中数据清洗和分析的方法,以装载机半轴为对象,优化载荷信号去噪和作业段识别的方法。利用小波自适应阈值法去除实测载荷的噪声尖峰和突变信号,并通过迭代平滑滤波法搜寻单个作业周期内同步载荷信号的极值点,识别、划分作业段为＂行走段-铲掘段＂。结合雨流计数和频次外推法编制载荷谱,有限元静力分析,以及Miner准则对装载机半轴进行疲劳寿命分析。结果表明：雨流计数的均幅值结果符合正态分布和三参数威布尔分布的假设性检验,疲劳分析损伤最大节点出现在万向节叉连接处,其寿命为7.22×10^8次循环。结果证实了作业段智能识别法的便利性和准确性,为传动系统疲劳分析的数据处理提供了借鉴。In order to improve the method of data cleaning and data analysis in fatigue durability and modify reliability design of wheel loader,research on load signal denoising and operation section recognition of wheel loader axle shaft was conducted. In view of the shortcoming of residual noise and waveform distortion caused by traditional wavelet threshold analysis,an adaptive threshold denoising method was used. Operation sections were divided as＂driving section-shoveling section＂through searching the extreme points of synchronous load signal in one single work cycle after iterative smoothing filter. Based on one-dimensional program loading spectrum and the finite element analysis,the fatigue life of axle shaft was predicted using the Miner criterion. The result shows that rain flow counting results were consistent with Normal distribution and Weibull distribution hypothesis testing. And the maximum damage node appeared in the universal joint folk which fatigue life is 7. 22 × 10^8 times cycle. The result confirmed that the intelligent recognition method is convenient and accurate,which provides a reference method for data processing of fatigue analysis of wheel loader transmission system.福建省财政厅企业技术创新项目资
