内容：
 随着测试技术的发展及跟踪试验在列车结构疲劳失效和诊断中的大规模应用,试验采样频率大幅提高,连续采样时间不断增长,从而生成了海量试验数据(单通道数据会达到TB级)。然而现有的商业软件对海量数据处理能力的不足,阻碍了海量测试数据发挥其应有的作用。在机车车辆疲劳强度线路试验或台架试验中,这一问题尤为突出。针对这一问题,本文利用C++和Pascal语言混合编程技术,给出了一种适用于海量时间历程数据雨流计数算法的具体编程实现过程,并与Matlab工具箱雨流技术算法进行了详细比较,结果表明:该算法在实现海量数据雨流循环计数的同时保证了较高的计算效率。在此算法基础上,总结梳理相关疲劳强度评定标准,开发出一套机车车辆疲劳强度评估系统(TPL.Fatigue)。在线路试验上的具体应用表明,该系统能满足海量应力时间历程数据雨流计数的要求,具有一定的实用价值。 With the development of test technology and the large-scale application of tracking test in the fatigue failure and diagnosis of train structure, the sampling frequency is greatly improved, and the continuous sampling time is increasing, resulting in mass test data (single channel data will reach TB level). However, the existing commercial software on the massive data processing capacity of the lack of hinder the massive test data to play its due role. This problem is particularly pronounced in locomotive fatigue line test or bench test. Aiming at this problem, this paper uses C ++ and Pascal language mixed programming technology to give a concrete programming realization process which is suitable for the data flow calculation algorithm of massive time course data, and compares it with Matlab toolbox rain flow technology algorithm, The results show that the algorithm can ensure the high computational efficiency while achieving massive rainwater cycle counting. On the basis of this algorithm, a set of evaluation criteria for fatigue strength assessment is summarized and a set of TPL.Fatigue is developed. The specific application on the line test shows that the system can meet the requirements of the rain flow counting of the massive stress time history data, and has certain practical value.

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