The Static Adjustment Method of Secondary Suspension Load for Six-Axle Locomotives via Modified Mayfly Optimization Algorithm

The static distribution of the static secondary suspension load has a significant impact on the adhesion, rim wear and passenger comfort of the locomotive. At present, the locomotive secondary suspension load adjustment is mainly realized by artificial padding to adjust the height of the spring. This process is not predictable, so it is difficult to complete the adjustment at one time, and it is limited by the operating space, technician proficiency, weight and volume of equipment, etc., so the efficiency of the whole industrialization process needs to be improved. Therefore, it is necessary to study the relationship between the shimming quantity, shimming position and load variance, and establish an expert system to guide the spring adjustment process, so as to avoid the waste of time, space and human resources caused by repeated operation. Based on the analysis of the supporting structure and force balance relationship, the general theoretical model of the load adjustment of the secondary suspension design with flexicoil suspension springs of six-axle locomotive is established in this study. The mayfly algorithm (MOA), which is characterized by high efficiency and precision, is introduced into the field of the locomotive spring load adjustment process for the first time in this study. However, the standard MOA still faces the problems of low efficiency of late iteration, sample degradation and algorithm prematurity. Therefore, this study optimized the standard MOA from the parameter control, offspring mutation strategy and weighted optimization strategy of the position updating process. The experimental analysis of an HXD1C locomotive proves that the proposed method is a great improvement in terms of efficiency and accuracy.