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针对矿用自卸车举升液压系统故障诊断困难的问题,采用一种基于粒子群优化支持向量机的方法对其进行研究。该方法利用粒子群优化算法,对支持向量机参数寻优,从而得到具有最佳分类结果的支持向量机模型。利用AMESim软件建立举升液压系统的仿真模型,并通过模拟溢流阀故障、举升液压缸内泄漏、泵内泄漏3种故障工况,提取故障数据,对该方法进行验证。仿真结果表明,该方法能有效对矿用自卸车举升液压系统这3种故障进行诊断。
Aimed at the difficulty in fault diagnosis of lifting hydraulic system of mining dump trucks, a method based on Particle Swarm Optimization (SVM) is studied. The method uses particle swarm optimization algorithm to optimize the parameters of SVM to get the SVM model with the best classification results. The simulation model of lifting hydraulic system was established by using AMESim software. The fault data were extracted by simulating the relief valve failure, lifting the cylinder internal leakage and pump internal leakage, and the method was validated. The simulation results show that the method can effectively diagnose the three kinds of faults of hydraulic system of jacking hydraulic system for mining dump truck.