基于功率谱特征分析的立铣刀磨损实时监测方法
2019-03-14
作者：王海宁1，谢峰1，李楠1，韩凤华2
单位：1安徽大学；2国网阜阳供电公司
摘要：为了从拾取的振动信号中获取刀具的磨损状态，比较了ChebyshevⅠ型滤波器和Butterworth滤波器的振幅特性，对所获信号进行降噪滤波，并对滤波后的信号进行频谱分析。经分析发现，刀具在不同磨损阶段其基频信息和倍频特征有较大变化，利用自相关函数对其进行功率谱分析后发现，刀具急剧磨损时功率谱幅值特征较初期磨损和中期磨损阶段变化显著，可以作为刀具磨损程度的特征值指标，该特征值可用于在线监测刀具的磨损故障。
关键词：刀具磨损；降噪滤波；自相关函数；功率谱；在线监测
中图分类号：TG115.5+8；TH117.1文献标志码：ADOI：10.3969/j.issn.1000-7008.2019.03.030
Research on Realtime Monitoring Method of Milling Cutter′s Wear Based on
Power Spectrum Feature Analysis
Wang Haining，Xie Feng，Li Nan，Han Fenghua
Abstract:In order to obtain the wear state of the tool from the picked vibration signals，the amplitude characteristics of the Chebyshev type I filter and the Butterworth filter are compared in this paper. Noise reduction filtering is performed on the acquired signals and spectrum analysis is performed on the filtered signals.The analysis shows that the tool′s fundamental frequency information and frequency multiplication features have great changes in different wear stages.After using the autocorrelation function to analyze the power spectrum，it is found that the characteristics of the power spectrum amplitude change significantly compared with the initial wear and medium wear stages when the tool wears rapidly，and can be used as a characteristic value index of the cuttingtool wear.This characteristic value can be applied to the online monitoring of wear failures of the tool.
Keywords:cutting tool wear；noise reduction filtering；auto correlation function；power spectrum；online monitoring