Feature Extraction of Acoustic Emission Energy Spectrum and Singularity Index in Tensile Process of Q235 Steel
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摘要: 为明确试样拉伸损伤过程声发射信号的变化规律,选取Q235钢试样开展轴向拉伸实验,并进行声发射信号的稳定连续采集。通过小波包分解与重构,提取声发射信号的小波包能量谱及信号幅值的奇异性指数特征。结果表明:信号能量在低频段较为集中,低频段中各频段能量占比随频段增加而降低,而高频段中则相反。随着损伤程度增加,高频能量占总能量比例不断减小,低频能量的总能量占比不断增加,奇异性指数不断下降。当拉伸速率增大时,高频能量在各拉伸损伤阶段的总能量占比不断下降,而低频能量占比和奇异性指数均升高。最后结合拉伸断口进行了宏、微观形貌特征分析。Abstract: In order to clarify the variation rules of acoustic emission signals in the tensile damage process, steel Q235 samples were selected to conduct axial tension and acoustic emission signals were collected stably and continuously. Through the wavelet packet decomposition and reconstruction, the wavelet packet energy spectrum of the acoustic emission signal and the singularity index characteristics of signal amplitude were extracted. The results show that the signal energy is concentrated in the low frequency band, and the energy ratio of each frequency band in the low frequency band decreases with the increasing of frequency band, while the opposite situation will occur in the high frequency band. As the damage degree increases, the ratio of high-frequency energy to total energy decreases, the ratio of low-frequency energy to total energy increases, and the singularity index decreases. When the stretching rate increases, the ratio of high-frequency energy to total energy decreases, while the ratio of low-frequency energy to total energy and the singularity index increase. Finally, combining with the tensile fracture, macro and microscopic features were analyzed.
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Key words:
- acoustic emission /
- feature extraction /
- energy spectrum /
- singularity index
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表 1 Q235钢试样元素质量分数
元素 质量分数/% Fe 98.69 Mn 0.65 Cr 0.044 Co 0.081 P 0.040 S 0.045 C 0.22 Si 0.23 表 2 各频带对应频率范围
序列 频率范围 1 (0, f/16) 2 (f/16, 2f/16) 3 (2f/16, 3f/16) 4 (3f/16, 4f/16) 5 (4f/16, 5f/16) 6 (5f/16, 6f/16) 7 (6f/16, 7f/16) 8 (7f/16, 8f/16) -
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