A Method of Selecting the Block Size of BMM for Estimating Extreme Loads in Engineering Vehicles

Abstract

Extreme loads have a significant effect on the fatigue damage of components. The block maximum method (BMM) is widely used to estimate extreme values in various fields. Selecting a reasonable block size for BMM is crucial to ensure that proper extreme values are extracted to get extreme sample to estimate extreme values. Aiming at this issue, this study proposed a comprehensive evaluation approach based on multiple-criteria decision making (MCDM) method to select a proper block size. A wheel loader with six sections in one operating cycle was illustrated as an example. First, spading sections of each operating cycle were extracted and connected as extreme loads often occur at that section. Then extreme sample was obtained by BMM for fitting the generalized extreme value (GEV) distribution. Kolmogorov-Smirnov (K-S) test, Pearson’s Chi-Square (χ2) test, and average deviation in Probability Distribution Function (PDF) are selected as the fitting test. The comprehensive weights are calculated by the maximum entropy principle. Finally, the optimal block size corresponding to the minimum comprehensive evaluation indicator is obtained and the result exhibited a good fitting effect. The proposed method can also be flexibly used in various situations to select a block size.