基于深孔钻削噪声信号的钻头磨损特征提取方法研究

摘要

采用深孔钻削过程中的噪声信号对钻头的磨损状态进行监测,建立了钻削过程噪声信号采集系统.运用改进的经验模态分解方法对经过广义形态滤波后的噪声信号进行了模态分解,获得了钻削噪声信号的本征模态函数.采用希尔伯特变换对本征模态函数进行处理,获得了与本征模态函数对应的边际谱.研究了谱频段能量和峰值随钻头磨损的变化规律.结果表明,边际谱频段能量和峰值与钻头磨损状态之间存在密切联系,根据噪声信号边际谱特征参数的变化规律可实现钻头磨损状态的监测.%The drilling tool wear condition monitoring is investigated in this paper by using the drilling noise signal. The noise signal acquisition system is established for the drilling process. The improved Empirical Mode Decomposition method is used to carry out the modal decomposition for noise signal filtered by the generalized morphological filtering, and the Intrinsic Mode Function (IMF) of signal is obtained. The IMF is processed by Hilbert transformation, and the marginal spectrum of IMF is obtained. The changing laws of the special frequency range energy and peak value of marginal spectrum along with the drilling tool wears are researched. The experimental results show that there exist the closer correlations between the tool wear condition and the two feature parameters. The drilling tool wear condition monitoring can be realized based on the variation laws of the marginal spectrum feature parameters of noise signal.