王继新, 季景方, 胡际勇, 王乃祥, 张英爽. 基于贝叶斯理论的工程车辆载荷样本长度计算方法[J]. 农业工程学报, 2011, 27(6): 148-151.
    引用本文: 王继新, 季景方, 胡际勇, 王乃祥, 张英爽. 基于贝叶斯理论的工程车辆载荷样本长度计算方法[J]. 农业工程学报, 2011, 27(6): 148-151.
    Wang Jixin, Ji Jingfang, Hu Jiyong, Wang Naixiang, Zhang Yingshuang. Bayesian method for determination of load sample size in engineering vehicles[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2011, 27(6): 148-151.
    Citation: Wang Jixin, Ji Jingfang, Hu Jiyong, Wang Naixiang, Zhang Yingshuang. Bayesian method for determination of load sample size in engineering vehicles[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2011, 27(6): 148-151.

    基于贝叶斯理论的工程车辆载荷样本长度计算方法

    Bayesian method for determination of load sample size in engineering vehicles

    • 摘要: 确定载荷样本长度是载荷谱编制的一个关键步骤,直接影响着零部件疲劳寿命预测的准确性。为了找到一种有效地确定载荷样本长度的方法,该文以轮式装载机为例,结合其零部件的受载特点,假定载荷均值服从正态分布,均值的先验分布也服从正态分布,提出了用贝叶斯理论和加权平方损失函数来计算载荷样本长度的方法。该方法以加权平方损失函数的后验损失期望值取最小值为条件,得到了包含先验信息、样本信息以及总体信息的后验参数估计值;然后结合t分布得到了在满足一定置信度和误差要求时的载荷样本长度。

       

      Abstract: The determination of the load sample size, as a critical step of compiling load spectrum, directly affects the accuracy of fatigue life estimation. In order to provide an effective method to determine the load sample size for engineering vehicles, the wheel loader was taken as an example and the load characteristics under the loading cycle condition were analyzed. Under the assumption that both the mean of time-history load and the prior distribution of the mean following the normal distribution, Bayesian method and quadratic loss function with weighting were adopted to calculate the sample size. When the posterior loss expectation reached the minimum value, the estimated values of posterior parameters were acquired, which included the prior information, the sample information and the general information. Finally, based on t distribution theory, the load sample size was obtained, which could meet a given confidence level and the error limit.

       

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