Impact localization method based on phase-sensitive optical reflection and deep learning of convolutional neural network

Abstract

The invention provides an impact localization method based on phase-sensitive optical reflection and deep learning of a convolutional neural network. The impact localization method includes the following steps: 1) two optical fiber monitoring network topological structures for impact load localization, based on a phase-sensitive optical time domain reflection principle; 2) structure design of a phase-sensitive optical time domain reflection sensing probe for impact load monitoring; 3) construction of a distributed phase-sensitive optical time domain reflection sensing impact monitoring systemand meshing of a thin plate monitoring area; 4) phase-difference-based phase-sensitive optical time domain reflection technique for impact load localization; 5) generation of an impact response samplelibrary based on a Phi-OTDR sensor; 6) data pre-processing and deep learning convolutional neural network design; and 7) using the trained deep learning convolutional neural network to identify the impact response data of the Phi-OTDR sensor.

Description

technical field [0001] The invention belongs to the field of impact monitoring of structural health monitoring, and specifically proposes a deep learning optical fiber impact load positioning method based on phase-sensitive optical time domain reflection (Φ-OTDR) and CNN convolutional neural network. Background technique [0002] Phase Sensitive Optical Time Domain Reflectometry (Φ-OTDR) technology is an optical fiber sensing technology based on Optical Time Domain Reflectometry (OTDR) technology; OTDR is based on the backward Rayleigh scattered light and Fresnel reflection generated by light in the optical fiber The light intensity of the light is changed to locate the fault, and Φ-OTDR is a kind of strong coherent, high-frequency stable pulse light injected into the optical fiber, and the Rayleigh scattered light that is backscattered in the pulse range is detected by the photodetector. As a result, the change can be obtained by subtracting the two adjacent detection resul...

AI Technical Summary

Problems solved by technology

Piezoelectric sensors have the advantages of high sensitivity, convenient use, and high frequency of measurement signals, but they are susceptible to electromagnetic environment interference; for the conventional low-speed sampling fiber grating sensing mode, in practical applications, due to the low sampling frequency of the demodulator, It will lead to a large loss of effective information representing the characteristics of the shock response, which cannot satisfy the principle of time difference positioning, resulting in a significant reduction in positioning accuracy

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