Structural Damage Localization Using Distributed Vibration Sensors
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Solution Overview
Problem
Traditional structural damage detection methods, relying on single sensors and predefined mathematical models, struggle with accurately locating damage in complex structures, especially when dealing with nonlinear vibration characteristics or deformable workpieces in motion, leading to inaccurate damage predictions.
Innovation Solution
A system utilizing a plurality of vibration sensors connected to a deep learning module that analyzes sensed vibration signals to evaluate damage location, enhanced by a striking device simulating damage through strikes, and includes a geometric feature extraction unit to generate virtual 3D reference lines for precise damage prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single sensor and predefined mathematical models are used, then the system is simple, but the damage location accuracy deteriorates in complex structures
Solution Approach 1:
The system divides the structure into multiple sensor deployment zones and processes vibration signals from each sensor independently before integrating results. This segmentation allows the system to handle complex structures by breaking them down into manageable sections, improving damage location accuracy without overwhelming system complexity
Solution Approach 2:
The patent transitions from traditional 2D sensor arrays to a 3D spatial distribution of sensors throughout the structure. By adding the third dimension of sensor placement and utilizing depth information in signal processing, the system achieves superior damage localization precision in complex three-dimensional structures
2Measurement precision
If traditional computational methods are used, then the system is computationally efficient, but the accuracy deteriorates for nonlinear vibration characteristics
Solution Approach 1:
The system performs preliminary signal preprocessing including noise filtering, feature extraction, and transformation of raw vibration signals into standardized formats before main analysis. This preliminary action reduces the complexity of subsequent computational tasks, enabling accurate detection of nonlinear vibration characteristics while managing computational resource consumption
Solution Approach 2:
The patent replaces traditional mechanical/mathematical model-based analysis with data-driven machine learning algorithms. This substitution enables the system to automatically adapt to nonlinear vibration characteristics without requiring complex predefined models, improving accuracy while maintaining reasonable computational efficiency through learned patterns
3Measurement precision
If traditional methods are used for stationary workpieces, then the detection is simple, but the accuracy deteriorates for workpieces in motion or deformable structures
Solution Approach 1:
The system transitions from static analysis methods to dynamic adaptive processing that responds to changing structural conditions. The signal processing algorithms continuously adjust to account for motion-induced vibrations and deformation patterns, enabling accurate damage detection in workpieces during motion or under deformable conditions
Solution Approach 2:
The patent implements adaptive parameter adjustment where sampling rates, filtering parameters, and analysis windows are dynamically modified based on the detected motion state and deformation level of the workpiece. This allows the system to maintain high detection accuracy across varying operational conditions without requiring completely different detection methods
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
The system provides precise damage location and status evaluation in complex structures by reducing noise interference and motion-induced errors, improving accuracy through deep learning and geometric analysis.
Implementation Method 1
a plurality of vibration sensors distributed on the mechanism under test for sensing vibrations of the mechanism under test
Data Source
AI summary
A structural damage determination system, for determining a structural damage of a mechanism under test. The structural damage determination system includes: a plurality of vibration sensors, distributed on the mechanism under test to sense the vibrations thereof, and a processor, having signal connections to the plural vibration sensors, wherein the processor includes a deep learning model with a model training mode and a model testing mode, wherein the deep learning model analyzes a plurality of sensed vibration signals from the vibration sensors, to evaluate a damage location or a damage status of the mechanism under test during motion.