Quantitative diagnosis method of beam defects based on laser Doppler non-contact random vibration measurement. (February 2020)

• Record Type: Journal Article
• Authors: Zhao, Shuanfeng; Li, Shijun; Guo, Wei; Zhang, Chuanwei; Cong, Bowen
• Is Part Of: Measurement. Volume 152(2020)
• Publication Date: 2020-02
• Subjects: Quantitative diagnosis -- Beam defects -- Non-contact random excitation -- Laser Doppler -- Continuous wavelet transform -- Convolutional neural network; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.107271
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The vibration signal of the beam was obtained by random vibration excitation. The vibration signal of the beam was transformed into frequency-scale map. CNN is used to quantitatively evaluate the defects of the beam. Potential for online and real-time detection of defects in beam structures. Abstract: The beam structure is prone to defect damage during its use, and the rapid quantitative diagnosis of the beam structure can detect the defects of the beam in real time and quantitatively. In this article, the method of obtaining the vibration time-domain signal under random excitation of beam structure is proposed by using random vibration excitation and Laser Doppler principle. Based on this, the defect quantitative identification algorithm of beam structure is proposed based on fast Fourier, continuous wavelet transform and convolutional neural network. The random vibration of different parts of steel beams with artificial defects is measured by Laser Doppler method. The experimental results show that the defect size of the beam structure can be effectively identified only by the random vibration signal of the finite point. The method is expected to help to develop an online real-time assessment instrument for beam structure defects in service state.