系统仿真学报 ›› 2016, Vol. 28 ›› Issue (8): 1725-1731.

• 仿真建模理论与方法 • 上一篇    下一篇

基于折息递推辨识算法的数控机床能效预测

蔡磊, 王艳, 纪志成   

  1. 江南大学 电气自动化研究所,无锡 214122
  • 收稿日期:2015-09-06 修回日期:2016-01-21 出版日期:2016-08-08 发布日期:2020-08-17
  • 作者简介:蔡磊(1988-),男,安徽亳州,硕士生,研究方向为离散制造系统能效预测和优化。
  • 基金资助:
    国家863计划(2014AA041505), 国家自然科学基金(61572238), 江苏省杰出青年基金(BK20160001)

Prediction of Energy Efficiency of NC Machine Tools Based on Recursive Method with Discounted Measurements

Cai Lei, Wang Yan, Ji Zhicheng   

  1. Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China
  • Received:2015-09-06 Revised:2016-01-21 Online:2016-08-08 Published:2020-08-17

摘要: 针对数控机床能量效率难于直接获取的问题,结合折息递推辨识算法给出一种新的机床能效预测方法。基于机床主传动系统功率平衡方程及附加载荷损耗函数,得出切削功率的估计模型;进一步考虑模型中的附加载荷损耗系数无法直接测量,采用折息递推辨识算法对附加载荷损耗系数进行辨识,从而估算出切削功率,根据机床的能效定义计算出能效值。实验与仿真结果表明,采用折息递推辨识方法估计附加载荷损耗系数,能够比采用传统最小二乘估计获得更高的辨识精度,求取的机床效率与其它方法相比更接近真实值。

关键词: 参数辨识, 数控机床, 能耗, 能效预测

Abstract: Aiming at the problem that the energy efficiency of numerical control machine tool is difficult to obtain directly, a new method combined with the recursive method with discounted measurements was presented to predict the energy efficiency of machine tool. The estimation model of the cutting power was given in view of the power balance equation of the machine tool main drive system and the additional load loss function, further taking into account the additional load loss coefficients in model could not be directly measured, the recursive method with discounted measurements was adopted to identify the additional load loss coefficients as well as estimating the cutting power. Afterwards, the energy efficiency of machine tool was calculated according to its definition. The experiment and simulation results show that the recursive method with discounted measurements has greater advantage on obtaining higher identification precision of the additional load loss coefficients than the traditional least-squares method and the efficiency of the machine tool which was obtained by using this method comes closer to the real value than other methods.

Key words: parameter identification, NC machine tools, energy consume, energy efficiency prediction

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