期刊论文

Feature analysis for acoustic emission signals during metal fatigue damage based on Kolmogorov entropy and correlation dimension

Mao, Hanying(maohl79@gxu.edu.cn)

中文

振动与冲击

1000-3835

2017

36

15

210-214

10.13465/j.cnki.jvs.2017.15.031

51365006:国家自然科学基金 51445013:国家自然科学基金 51105045:国家自然科学基金 14-045-15S05:广西制造系统与先进制造技术重点实验室课题

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深化对金属疲劳损伤过程中声发射信号的特征认识,是运用声发射信号对金属结构损伤过程进行监测预测的重要基础热点问题。针对金属疲劳损伤经历裂纹萌生阶段、裂纹缓慢扩展阶段、裂纹快速扩展阶段、临近破坏等四个阶段产生大量的声发射信号,采用K熵和关联维数等混沌特征量来分析海量的声发射信号;通过对45号钢试件进行三点弯曲疲劳试验、测试得到试件疲劳损伤过程的声发射信号,分别估算不同时间段所产生声发射信号的K熵和关联维数,分析结果表明金属疲劳损伤过程的声发射信号具有混沌特征,其K熵和关联维数的变化趋势与金属疲劳损伤过程的四个阶段具有较清晰的对应关联,表明K熵和关联维数可以较好的...

Deeply knowing features of acoustic emission (AE) signals during metal fatigue damage is a basic and hot key for adopting AE signals to detect metal structures' damages. Aiming at a great amount of AE signals produced in four stages of metal fatigue damage including crack initiation, slow crack propagation, rapid crack propagation and approaching failure, they were analyzed with Kolmogorov entropy and correlation dimension. AE signals were measured in three-point bending tests of 45# steel specimens. Kolmogorov entropy and correlation dimension...