A distributed strain micro-crack detection system and method based on stacked autoencoders

A stacked autoencoder and distributed strain technology, applied in the field of pattern recognition, can solve problems such as unmeasured and easy to miss cracks, and achieve improved detection results, high robustness, and remarkable results

Active Publication Date: 2022-03-25
CHANGAN UNIV
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Problems solved by technology

These sensors are point-to-point sensors, which cannot measure the overall data of the structure and are easy to miss cracks

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  • A distributed strain micro-crack detection system and method based on stacked autoencoders
  • A distributed strain micro-crack detection system and method based on stacked autoencoders
  • A distributed strain micro-crack detection system and method based on stacked autoencoders

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Embodiment Construction

[0051] Below in conjunction with accompanying drawing, the present invention is described in further detail:

[0052] see Figure 1 to Figure 5 , a distributed strain crack detection system based on stacked autoencoders, including a strain sequence acquisition module; a strain sequence preprocessing module; a feature self-learning and characterization module based on stacked autoencoders; a Softmax classification recognition module (the specific process is as follows figure 1 shown).

[0053] The strain sequence collection module is used to collect the distributed strain of the structure, and the collected distributed strain of the structure is a one-dimensional sequence;

[0054] The strain sequence preprocessing module includes: a z-score normalization module and a sliding window module, and the z-score normalization module normalizes the strain sequence to data with 0 mean and 1 standard deviation. The sliding window module intercepts a set of strain subsequences of lengt...

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Abstract

The invention discloses a distributed strain crack detection system and method based on a stacked autoencoder. Using the good feature representation ability of a deep neural network, the crack detection is regarded as a binary classification problem, and a deep strain crack detection system based on a stacked autoencoder is constructed. A neural network for crack and non-crack classification of strain subsequences in structures. The method of the invention can accurately and completely detect tiny cracks with an opening width of 32 μm on a laboratory steel beam, and provides a solution with good noise robustness for detecting distributed strain cracks on the surface of a structure.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and in particular relates to a distributed strain micro-crack detection system and method based on a stacked autoencoder. Background technique [0002] Crack detection has always been an important topic in the field of structural health monitoring. Crack detection methods include manual observation methods and non-destructive testing methods. The method of manual observation requires specialized maintenance personnel to use professional tools to conduct regular inspections, which is inefficient and highly subjective. The non-destructive testing method mainly uses the data obtained by ultrasonic, X-ray, ground penetrating radar and camera to detect the cracks of the structure. These sensors are point-to-point sensors, which cannot measure the overall data of the structure and are easy to miss cracks. Contents of the invention [0003] The purpose of the present invention is to provide a dis...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N21/88G01B11/16
CPCG01N21/88G01B11/16
Inventor 宋青松武金睿陈禹
Owner CHANGAN UNIV
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