作者：郭庆, 吴广军, 徐翠锋

作者单位：桂林电子科技大学电子工程与自动化学院, 广西 桂林 541004

关键词：钻头磨损;声音信号;功率谱;BIF;Fisher准则;逻辑回归

摘要：

钻头工况的实时自动监测有助于提高钻削加工过程的可靠性。针对钻头磨损在线监测,提出基于钻头工作声音信号累积功率谱的失效监测法。采用驻极体声电转换器采集声音信号,根据钻头磨损的慢变性,提出基于累积功率谱提取能量特征集的方案,并使用BIF特征选择结合Fisher准则筛选最优特征集,解决特征数量较多的问题。最后,利用二分类逻辑回归实现特征集与磨损量之间的数学建模,以h函数值作为失效判断的依据。结果表明:系统在钻头磨损严重并接近失效时,计算失效概率值0.7,近似等于真实值,能为钻头更换决策提供可靠依据。

Monitoring of failure drill based on cumulative power spectrum of acoustic information

GUO Qing, WU Guangjun, XU Cuifeng

School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China

Abstract: Real-time and automatic detection of drill's working conditions contribute to improve the reliability of drilling process. An approach for online drill wear monitoring was proposed according to the cumulative power spectrum of drill acoustic information. The acoustic information was acquired by an electret microphone. The optimal feature set was screened by BIF feature selection and Fisher criterion to minimize the number of features. Furthermore, a mathematical model for feature set and wear amount was created by binary logistic regression and the function h was used as the criterion for failure determination. The study has indicated that the failure probability is higher than 0.7 and approximately equals to the true value when the drill is worn heavily and almost out of service. This approach mentioned above can provide a reliable basis for drill replacement.

Keywords: drill wear;acoustic signal;power spectrum;BIF;Fisher criterion;logic regression

2016, 42(2): 111-114  收稿日期: 2015-3-10;收到修改稿日期: 2015-5-7

基金项目: 桂林市科技攻关项目(LD14042E);广西重点学科重点实验室项目(LD12047B)

作者简介: 郭 庆(1962-),男,陕西杨凌示范区人,教授,研究方向为信号处理、微弱信号检测及测控技术。

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