Two-Stage Compressive Sensing for Structural Health Monitoring

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Solution Overview

Problem

Traditional structural health monitoring (SHM) systems face challenges in efficiently inspecting large complex structures due to difficulties with large sensor arrays, timely analysis of data sets, and overall weight, which hinders accurate damage detection and characterization.

Innovation Solution

The implementation of compressive sensing algorithms for automated reduction of NDE/SHM data from pitch-catch ultrasonic guided waves, utilizing two-stage data acquisition and analysis processes to handle temporally and spatially undersampled sensor signals, reducing data acquisition and processing burdens while maintaining damage detection ability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional structural health monitoring systems use large sensor arrays to inspect large complex structures, then damage detection accuracy is improved, but system weight and data processing burden increase significantly

Engineering Contradiction:

Improvedamage detection accuracyVSAvoidsystem weight

Core Design Contradiction:

Measurement precisionVSWeight of stationary object

Solution Approach 1:

The patent applies asymmetric sampling strategies where sensors are strategically positioned and activated based on specific inspection needs rather than uniform full-array activation. This allows selective engagement of sensor subsets, reducing overall system weight while maintaining detection accuracy for critical damage zones.

Inventive Principle:

Principle #4Asymmetry

Solution Approach 2:

The system employs partial action by activating only necessary sensor subsets for each inspection task rather than using the complete sensor array. Compressive sensing algorithms enable accurate damage detection from these partial measurements, significantly reducing the effective number of sensors needed and thus system weight.

Inventive Principle:

Principle #16Partial or excessive action

2Loss of information

If traditional SHM systems use large sensor arrays with full data acquisition, then complete structural information is captured, but data processing time and computational burden increase

Engineering Contradiction:

Improvestructural information completenessVSAvoiddata processing time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent extracts only the essential structural health information needed for damage detection rather than processing complete sensor datasets. Compressive sensing algorithms identify and extract critical features from undersampled data, eliminating redundant information processing while maintaining detection accuracy.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system performs preliminary action by pre-processing sensor signals to identify relevant features before full analysis. Compressive sensing reconstruction algorithms prepare data in advance by filling in missing information from undersampled measurements, reducing the computational burden of subsequent damage detection analysis.

Inventive Principle:

Principle #10Preliminary action

3Productivity

If compressive sensing is used to reduce sensor array size and data acquisition, then system weight and processing burden are reduced, but signal reconstruction accuracy must be maintained

Engineering Contradiction:

Improvedata acquisition speedVSAvoidsignal reconstruction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent implements feedback mechanisms where the compressive sensing reconstruction process iteratively refines signal estimates based on measured data and sparsity constraints. This feedback loop ensures that reconstruction accuracy is maintained even with reduced sampling rates, allowing faster data acquisition without sacrificing measurement precision.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes parameters by adjusting sampling rates, sensor activation patterns, and reconstruction algorithm parameters dynamically based on structural conditions and inspection requirements. This allows optimization of the balance between data acquisition speed and reconstruction accuracy for different operational scenarios.

Inventive Principle:

Principle #35Parameter changes

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 faster data acquisition and reduced data sets without compromising damage detection, facilitating the use of SHM systems in large complex structures by minimizing sensor numbers and data acquisition requirements.

Implementation Method 1

pitch-catch ultrasonic guided waves

Methodology Applied

Scientific EffectUltrasonic guided waves: Ultrasound

Implementation Method 2

transmits at least one signal to the structure and receives the at least one signal after encountering the structure

Methodology Applied

Scientific EffectWave propagation:

Data Source

PatentUS12105228B2Damage detection using two-stage compressive sensing

Publication Date: 2024.10.01 ADVENT INNOVATIONS LTD CO

AI summary

Described herein are Compressive Sensing algorithms developed for automated reduction of NDE/SHM data from pitch-catch ultrasonic guided waves as well as a methodology using Compressive Sensing at two stages in the data acquisition and analysis process to detect damage: (1) temporally undersampled sensor signals from (2) spatially undersampled sensor arrays, resulting in faster data acquisition and reduced data sets without any loss in damage detection ability.