Acoustic Emission Analysis for Structural Integrity
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Solution Overview
Problem
Current methods for detecting acoustic emissions from objects struggle to accurately identify multiple simultaneous structural changes, often masking signal features and failing to determine specific modes of change, which complicates the assessment of structural integrity.
Innovation Solution
An acoustic sensing system and analyzer module that generate frequency distribution functions and time evolution images from acoustic waveform data, using learning algorithms to classify structural changes and improve the identification of previously unidentifiable changes in objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If currently available acoustic detection methods are used, then the detection process is simple, but the ability to identify multiple simultaneous structural changes is insufficient and signal features are masked
Solution Approach 1:
The patent segments the acoustic waveform data by dividing it into multiple time windows or segments, allowing different structural changes occurring at different times to be analyzed separately. This segmentation enables the detection system to identify multiple simultaneous structural changes that would otherwise be masked in the combined signal, directly improving measurement precision without requiring overly complex processing.
Solution Approach 2:
The patent transforms the acoustic signal analysis from a single-time-point measurement to a multi-dimensional analysis by introducing time evolution images that display frequency distribution across multiple time windows. This dimensional transformation allows operators to visualize and identify multiple structural changes that occur at different times, improving identification accuracy while maintaining manageable system complexity through graphical representation.
2Loss of information
If currently available acoustic detection methods are used, then the testing process is quick, but the ability to determine specific modes of structural change is insufficient
Solution Approach 1:
The patent performs preliminary segmentation of the acoustic waveform data into multiple time windows before detailed analysis. By pre-organizing the data into manageable segments and generating time evolution images that show frequency distribution across these segments, the system prepares the information in advance, reducing the time needed for detailed mode identification while preserving complete information about different structural change modes.
Solution Approach 2:
The patent uses temporary data structures and intermediate representations (such as time evolution images and segmented waveform data) that are generated, analyzed, and then discarded. These intermediate objects allow detailed analysis of structural change modes without requiring permanent storage or complex long-term data management, reducing information loss while minimizing time investment.
3Measurement precision
If currently available acoustic detection methods are used, then the analysis is straightforward, but multiple simultaneous structural changes are masked and cannot be identified
Solution Approach 1:
The patent introduces time evolution images that add a time dimension to the frequency spectrum analysis. These images display frequency distribution across multiple time windows, allowing operators to easily visualize and identify multiple simultaneous structural changes that would be masked in traditional single-point spectral analysis. The graphical representation maintains ease of operation while dramatically improving detection precision.
Solution Approach 2:
The patent uses time evolution images as an intermediary between the raw acoustic waveform data and the final structural change identification. This intermediate representation transforms complex multi-component signals into visual patterns that are easier to interpret, allowing operators to identify multiple simultaneous structural changes without directly analyzing the complicated raw data, thus maintaining ease of operation while improving measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient and accurate assessment of structural integrity by uncovering hidden patterns in acoustic data, reducing testing costs and cycle time, and providing clearer insights into the behavioral characteristics of materials.
Implementation Method 1
Acoustic emission is the radiation of acoustic waves in an object or material when the material undergoes a structural change. For example, without limitation, acoustic emissions may occur when a composite object undergoes a structural change.
Data Source
AI summary
An apparatus comprises an acoustic sensing system and an analyzer module. The acoustic sensing system is positioned relative to an object, wherein the acoustic sensing system detects acoustic emissions and generates acoustic waveform data for the acoustic emissions detected. The analyzer module is implemented in a computer system that receives load data and the acoustic waveform data for the object, generates a plurality of frequency distribution functions using the acoustic waveform data, and generates a frequency distribution function time evolution image containing a plurality of points of each of the plurality of frequency distribution functions.