基于AdaBoost集成回归模型的液压锻锤磨损状态识别
Wear state identification of hydraulic forging hammer based on AdaBoost integrated regression model
冀永曼 1许锋1
作者信息
- 1. 新乡职业技术学院,智能制造学院,河南新乡453000
- 折叠
摘要
选择液压锻锤作为测试对象,再以多项式拟合和集成算法结合的过程,开发出了锻锤磨损阶段建立回归模型的方法.再把AdaBoost集成算法也加入统一回归模型内,从而降低磨损过程中的回归模型预测误差.研究结果表明:给出了基于AdaBoost集成回归模型的液压锻锤磨损状态识别表达式.平稳磨损阶段所需时间最短,最长的为急剧磨损阶段.该研究为进一步识别锻锤磨损状态提供了一定的理论支撑作用,该研究可以拓宽到其它的磨损领域,具有很好的实际应用价值.
Abstract
The hydraulic hammer is selected as the test object, and the regression model of hammer wear stage is developed by polynomial fitting and integrated algorithm. Then AdaBoost integrated algorithm is added into the unified regression model to reduce the prediction error of the regression model in the wear process. The research results show that the expression of hydraulic forging hammer wear state recognition based on AdaBoost integrated regression model is given. The period of smooth wear is the shortest, and the period of sharp wear is the longest. The research provides some theoretical support for further identifying the wear state of forging hammer. The research can be extended to other wear fields and has good practical application val-ue.
关键词
锻锤磨损/回归模型/集成算法/状态识别Key words
Tool wear/Regression model/Integration algorithm/State recognition引用本文复制引用
出版年
2024
NETL
NSTL
万方数据