Acoustic emission signal feature extraction and crack damage identification method based on information entropy theory

Abstract

The invention discloses an acoustic emission signal feature extraction and crack damage identification method based on an information entropy theory, which applies the information entropy theory to an extraction process of acoustic emission feature parameters, and provides a new acoustic emission parameter, namely acoustic emission entropy, capable of accurately and effectively identifying material crack damage. And a material crack damage identification method based on the acoustic emission entropy is established. The acoustic emission entropy is not only unrelated to a manually set acoustic emission system threshold value, but also can reflect the internal chaotic characteristic or uncertainty of an acoustic emission waveform, can accurately and effectively identify and evaluate different crack damage states of a material, also has excellent anti-noise capability, and has huge development and application potential in acoustic emission health monitoring.

Description

technical field [0001] The invention belongs to the field of acoustic emission monitoring and damage identification, in particular to a method for feature extraction of acoustic emission signals and crack damage identification based on information entropy theory. More specifically, it involves a new acoustic emission parameter—acoustic emission entropy—that can accurately and effectively identify material damage. Background technique [0002] Acoustic Emission Technology (AET) is an important monitoring technology in Structural Health Monitoring (SHM). Compared with traditional non-destructive testing techniques such as ultrasound, ray, and eddy current, acoustic emission technology has been gradually applied to various pressure vessels due to its unique advantages of being able to detect dynamic defects, realize long-term online monitoring, and evaluate structural integrity. and pipeline health monitoring and damage assessment. However, the key problem and challenge faced...

AI Technical Summary

Problems solved by technology

If the threshold is not set properly, or the acoustic emission monitoring environment is not easy to control (such as a high-noise environment), it will inevitably affect the size of these parameters, and then directly affect the damage assessment results

On the other hand, although the amplitude and RMS voltage have nothing to do with the threshold value of the acoustic emission system, they can only reflect the absolute intensity of the acoustic emission waveform, and cannot reflect the chaotic characteristics of the acoustic emission waveform caused by material damage

The above factors greatly limit the development of damage assessment based on AE parameters, as well as the industrial application of AE monitoring

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