Mel倒谱
声发射
波形
声学
材料科学
复合材料层合板
计算机科学
语音识别
复合数
特征提取
人工智能
复合材料
物理
电信
雷达
作者
D. Xu,Pengfei Liu,Zhiping Chen,Tao Wu
标识
DOI:10.1177/14759217211056566
摘要
The correlation between acoustic characteristics and mechanical behaviors shows great significance for health monitoring and characterization. This paper develops a new quantitative model based on the modified Mel-frequency cepstral analysis and statistical methods so as to link acoustic emission (AE) features with mechanical behaviors of end-notched flexure (ENF) composite laminates. First, the Mel-frequency cepstral analysis in automatic speech recognition is modified to adapt to AE sensors and signals. Second, the modified Mel-frequency cepstral coefficients (MFCCs) are extracted from original waveforms of AE hits for damage characterization of composites. MFCC 0 is taken as an effective feature to qualitatively discriminate damage stages and to identify the pre-failure critical point. The decreasing patterns of MFCC 1 and MFCC 2 for ENF specimens can be clearly observed with the loading time by using the simple moving average method. Third, pencil lead breaks are repeatedly conducted on the healthy specimen to verify the pattern in the degraded specimen. Finally, a further investigation based on the cumulative moving average method demonstrates that MFCC 1 and MFCC 2 are quadratic and linear functions of the load ratio or the deflection ratio, respectively. In addition, the latter is more suitable to be an indicator of damage accumulation of composite laminates.
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