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Combined two-level damage identification strategy using ultrasonic guided waves and physical knowledge assisted

Mahindra Rautela¹, J Senthilnath², Jochen Moll³

¹Department of Aerospace Engineering, Indian Institute of Science, Bangalore, Karnataka 560012, India.

|May 8, 2021

Summary

This study introduces a novel physical knowledge-assisted machine learning method for structural health monitoring of composite panels. The technique accurately detects and localizes damage using ultrasonic guided waves, enhancing aerospace safety.

Keywords:

Damage identification Deep learning Physical knowledge assisted machine learning Structural health monitoring Ultrasonic guided waves

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Area of Science:

Aerospace Engineering
Materials Science
Artificial Intelligence

Background:

Structural Health Monitoring (SHM) of composite structures presents significant challenges in the aerospace industry.
Accurate damage detection and localization are crucial for ensuring the safety and integrity of aerospace components.

Purpose of the Study:

To develop and evaluate a novel physical knowledge-assisted machine learning technique for combined damage detection and localization in composite panels.
To improve the accuracy and efficiency of SHM for aerospace applications.

Main Methods:

Utilized ultrasonic guided waves for damage identification in a composite panel.
Developed a two-level damage identification strategy: damage detection (binary classification) and localization (multi-class classification) using convolutional neural networks (CNNs).
Integrated physical domain knowledge and expert supervision to assist the machine learning process.

Main Results:

Achieved high accuracy (above 99%) for both damage detection and localization.
Demonstrated superior performance compared to direct deep learning approaches by incorporating physical knowledge.
Real-time application feasibility shown with prediction times in milliseconds per signal.

Conclusions:

The proposed physical knowledge-assisted machine learning technique offers a robust and accurate solution for SHM of composite structures.
The combined damage identification strategy is effective for real-time aerospace applications.
The method provides advantages in accuracy, computational efficiency, sensor optimization, and noise robustness.