Neural Network-based Optimization of Rescue Performance of Nitrogen Explosion Type Hydraulic Breaker

Abstract

The original intention of hydraulic breaker design is to be used as a demolition equipment in roads, mines and ruins. Because of its excellent demolition performance, it played a certain role in rescue. And compared with other professional rescue equipment, the design cost is lower. However, because its safety cannot be guaranteed, there is a problem of insufficient rescue performance. Therefore, the comprehensive performance evaluation system of large multi-functional rescue attachments is used to evaluate a nitrogen explosion type hydraulic breaker, and the impact energy and frequency are determined as the rescue performance indicators. Establish a simulation model of hydraulic breaker using power bond graph method and verify its accuracy. The key factors are determined by analyzing the impact of various parameters of the hydraulic breaker on the rescue performance; Comprehensive utilization of uniform design-neural network-genetic algorithm to optimize the rescue performance. The results show that the rescue performance evaluation of the optimized hydraulic breaker is improved. It has reference significance for the conversion of other existing earth-moving machinery into rescue equipment.