刀具声发射信号混沌特征提取及趋势分析

Chaotic characteristics extracting and trend analysis on acoustic emission signal of tool condition

旨在建立一种基于多混沌特征演化分析的刀具磨损状态监测方法,通过计算刀具声发射信号的混沌特性参数分析其磨损趋势。建立首先基于混沌理论分析和描述刀具不同磨损阶段声发射信号的混沌特性,包括:定性描述,即重构相空间的奇异吸引子轨迹和庞加莱截面;定量描述,计算不同时间段声发射信号的关联维数、最大Lyapunov指数等。其次,采用最小二乘回归方法对所计算的混沌参数进行趋势分析。结果表明,声发射信号具有混沌特性,而且关联维数和最大Lyapunov指数演化趋势与刀具的磨损状态具有一定的关联,从而为刀具磨损状态的在线监测和预测提供了新思路。

This paper is aim to build a monitoring method based on several chaotic characteristics,through chaotic characteristics analyzing of the acoustic emission signal from cutting tool and analyzing the trends of tool wear.Chaotic characteristics analyzed and described based on chaotic theory in different periods of acoustic emission signal from cutting tool,are including：（1） qualitative description,reconstructing the strange attractor track and Poincare map of the acoustic emission series;（2） quantitative description,computing correlation dimension and the max lyapunov exponent of the acoustic emission signal at different periods.Then chaotic characteristics computing result were analyzed by the least square method.The results show that chaotic phenomena exist in acoustic emission signal,and the correlation dimension and the max Lyapunov exponent have relationship with tool wear state.At last the paper provides a new idea for online monitoring and prediction of tool wear state.