Non-contact quantitative diagnosis method for beam structural defects based on convolutional neural network

Abstract

The invention discloses a method for non-contact quantitative diagnosis of beam structural defects based on convolutional neural network. The diagnosis method and process are as follows: using laser Doppler non-contact method to measure weak vibration time-domain signals of several points on the beam under random excitation , add the random vibration signals of any non-collinear three points, and then perform fast Fourier transform to generate a frequency spectrum, and transform the frequency spectrum by continuous wavelet transform (CWT) to generate a frequency-scale distribution map. The quantitative evaluation results of beam structure damage can be input into the trained convolutional neural network (CNN) by inputting the frequency-scale distribution diagram of any non-collinear three-point vibration data on the surface of the beam structure, so that the neural network can give the degree of beam defect the judgment result.

Description

technical field [0001] The invention relates to a non-contact quantitative diagnosis method for beam structural defects based on a convolutional neural network, belonging to the fields of laser detection and measurement and image processing. Background technique [0002] Beam structures are widely used in mechanical engineering, civil engineering, aerospace and other fields. During service, due to the influence of ambient temperature, long-term mechanical loads and various corrosion conditions, it will inevitably produce defect damage, resulting in beam structure Damaged or even fractured, resulting in huge property losses and immeasurable catastrophic accidents. Rapid quantitative diagnosis of beam structures is increasingly becoming an important research topic. The traditional beam defect identification method needs to add sensors to the beam structure for measurement. The additional mass causes large errors in the measurement results of thin-walled small beams, and the i...

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Problems solved by technology

The traditional beam defect identification method needs to add sensors to the beam structure for measurement. The additional mass causes a large error in the measurement results of thin-walled small structure beams, and the information of the measurement points cannot be fully utilized.

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