摘要
对钻头的磨损程度进行实时检测有助于对钻削加工过程实施预防性维护,提醒及时换刀。针对自动化生产中的刀具监测问题,给出一个基于主轴电流检测的钻头磨损状态分析和剩余寿命预测的应用策略。通过主轴电流传感器采样加工过程的电流信号,使用一个滑动窗口从连续采样数据中得到真实加工段数据,采用小波包分解的方法进行特征提取。基于Fisher标准筛选出最能表达磨损过程的若干特征。最后利用逻辑回归法和自回归滑动平均模型相结合的方法评估当前钻削加工的可靠性,预测钻头的剩余寿命。试验证明此方法的有效性,可为换刀决策提供依据。
The real-time detection of drill wear is helpful to the preventive maintenance which will give caution of tool change in drilling production process.For the problem of tool monitoring during automatic production,a strategy of drill bit wear monitoring and remaining life time prediction system is presented for drilling machine with the use of spindle current signal.The spindle current information is acquired by closed loop current sensor,and a moving window technique is used to extract the real parts of data for drilling from the sampled data sequence.The wavelet packet decomposition is used to extract features.Critical features are selected according to their ability of discriminating the wear under Fisher criterion.Logistic regression（LR） combined with auto-regressive moving average（ARMA） model are used to evaluate the failure possibility and remaining life of the drill bit.Experimental results show good performance of the proposed algorithm and it can provide the basis for tool change.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2011年第1期177-181,共5页
Journal of Mechanical Engineering
基金
教育部留学回国人员科研基金资助项目(20091590)
关键词
刀具磨损
小波包分解
特征选择
剩余寿命预测
Tool wear Wavelet packet decomposition Feature selection Prediction of remaining life
