دورية أكاديمية

Effective Defect Features Extraction for Laser Ultrasonic Signal Processing by Using Time–Frequency Analysis

التفاصيل البيبلوغرافية
العنوان: Effective Defect Features Extraction for Laser Ultrasonic Signal Processing by Using Time–Frequency Analysis
المؤلفون: Zhenyu Zhu, Hao Sui, Lei Yu, Hongna Zhu, Jinli Zhang, Jianping Peng
المصدر: IEEE Access, Vol 7, Pp 128706-128713 (2019)
بيانات النشر: IEEE, 2019.
سنة النشر: 2019
المجموعة: LCC:Electrical engineering. Electronics. Nuclear engineering
مصطلحات موضوعية: Time-frequency analysis, laser ultrasonic signal, feature extraction, wavelet transform, signal-to-noise rate, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
الوصف: The time-frequency analysis (TFA) by wavelet transform is adopted for the laser ultrasonic signal processing, and the effective features extraction of the material defect is obtained. The TFA is adopted here to analyze the laser-generated surface acoustic wave (SAW) signal which contains the defect features, the echo wave features are extracted significantly, especially under the condition of low signal-to-noise rate (SNR). The simulation model by using finite element method (FEM) is set up in an aluminum plate with different surface defect depths in detail, and the defect depths prediction with TFA is also considered. It shows that, without extra denoising process, the echo SAW is extracted significantly in case of defect depths ranging from 0.1mm to 0.9mm at SNR of -3dB by TFA. The TFA for processing the laser ultrasonic signal provides a promising way to get the defect information, with the accuracy increased by 7.9dB in this work, which is extremely meaningful for the ultrasonic signal processing and material evaluation.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2169-3536
Relation: https://ieeexplore.ieee.org/document/8822956/; https://doaj.org/toc/2169-3536
DOI: 10.1109/ACCESS.2019.2939262
URL الوصول: https://doaj.org/article/f7bdf3cf5b014a23b8ea399bacde41e2
رقم الأكسشن: edsdoj.f7bdf3cf5b014a23b8ea399bacde41e2
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:21693536
DOI:10.1109/ACCESS.2019.2939262