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A Novel Data Reduction Approach for Structural Health Monitoring Systems
Hamed Bolandi1, Nizar Lajnef1, Pengcheng Jiao2
1Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA.
Sensors (Basel, Switzerland)
|November 9, 2019
Summary
This study introduces a novel probability-based method for data reduction in structural health monitoring (SHM) systems. The technique efficiently detects damage progression by analyzing strain event durations, significantly reducing data storage needs.
Area of Science:
- Structural Health Monitoring
- Materials Science
- Probability Theory
Background:
- Structural Health Monitoring (SHM) systems generate massive datasets, challenging data transmission and analysis.
- Existing methods often require processing extensive strain data, limiting real-time applications.
- Efficient data reduction is crucial for the practical implementation of SHM.
Purpose of the Study:
- To propose a novel data reduction technique for SHM systems based on probability theory.
- To develop a method that alleviates the need for collecting and analyzing entire strain datasets.
- To enable efficient damage detection and progression monitoring in structural components.
Main Methods:
- A relative damage approach correlating strain distribution variation rates with damage rates.
- Experimental and numerical studies on a steel plate with varying damage states (circular holes).
- Measurement of cumulative durations of strain events at predefined levels, rather than full strain response.
Main Results:
- The proposed technique successfully detected damage progression in the steel plate.
- Damage detection accuracy improved with increased predefined strain levels.
- Achieved over 2500% reduction in data storage requirements.
Conclusions:
- The probability-based data reduction method is effective for SHM.
- The approach significantly reduces data storage, benefiting on-line SHM systems.
- This method offers a practical solution for handling large data volumes in structural monitoring.
