基于经验模分解和最小二乘支持矢量机的装载机载质量动态测量混合建模方法

Integrative Modeling Method Based on Empirical Mode Decomposition and Least Square Support Vector Machines about Dynamic Weighing of Loaders

针对同时实现装载机载质量动态测量的高精度和快速性这一复杂问题,在基于动力学分析所给出的测量方法的基础上,给出一种实现高精度快速性装载机载质量动态测量的混合建模方法,该方法采用经验模分解(Empirical mode decomposition,EMD)作为实测信号的前端处理应用,用于从动态暂态测量信号中提取有效信号;采用最小二乘支持矢量机(Least square support vector machines,LS-SVM)对动态和不确定性因素进行自学习,实现载质量动态测量的速度补偿;同时为使最小二乘支持矢量机发挥最优性能,采用贝叶斯证据框架对其参数进行推断优化;最后,通过比例线性计算方法获知待测载质量值。仿真分析和试验结果表明,按照所建立的混合建模方法进行载质量动态测量其测量精度可稳定在1%以内,验证了该方法的有效性。

For dynamic weighing about loaders, to obtain a quick and exact measure result synchronously is a complex problem. Based on an existent dynamic weighing method about loaders, an original integrative modeling method for dynamic weighing is given. In the method, empirical mode decomposition is used to be the signal processing method for local pressure signal contaminated; and the least square support vector machines is used to be learning machine for dynamic pressure compensation varying with different lift crane velocity; also the Bayesian evidence framework for selecting and tuning parameters of least square support vector machines; Finally, after doing some simple linear proportional calculation, the weight of load is obtained. In the end, emulation analysis and test results all indicate that by using the above modeling method, measure precision within 1% can be obtained.