I&T Solution

Reference No.

S-0568

Solution Name

AI-based Cross-material Structural Health Monitoring Using Ultrasonic Waves

Solution Description

The development of modern industries has put forward new requirements for structural health monitoring, including high efficiency, high accuracy, and high reliability. Existing physical-model-based evaluation methods, such as using ultrasonic waves, developed on one specific material cannot be directly applied to other materials due to the different mechanical properties of the materials. Therefore, the application of the physical-model-based damage evaluation in different materials requires a large amount of modelling work, which is time-consuming and inefficient. Our proposed AI-based method can automatically determine the health state of the monitored structures (intact or damaged) by making use of the wave energy distribution features in the frequency domain. In conjunction with a proper imaging algorithm, the location of the damage can be identified. Independent of the mechanical properties of the material and wave propagation characteristics in the structures, this method can be applied to different materials, including metallic and composite materials. The AI-based cross-material structural health monitoring using ultrasonic waves focuses on solving the key requirements of potential partnerships including automotive, aviation, and ship manufacturers, civil infrastructure systems, energy industries, etc., developing effective communication and technical solution.

Application Areas

Infrastructure
Automotive Production, Aircraft Maintenance, Material Testing

Technologies Used

Artificial Intelligence (AI)
Data Analytics
Deep Learning
Machine Learning
Ultrasonic Testing

Use Case

Different from the conventional ultrasonic wave-based structural monitoring and inspection method which requires a vast amount of modelling work for multiple materials, this newly proposed method can be directly applied to different materials, which is much more efficient by circumventing complicated physical modelling of wave propagation in structures. According to previous test results, the damage detection accuracy for aluminum plate and glass fibre-reinforced plastic laminate is 92.86% and 95.45%, respectively. The method can be applied in various industry fields, such as automotive, aviation, and ship manufacturers, civil infrastructure systems, energy industries, etc.

Company / Organisation Name

Hong Kong Productivity Council

Email Address

mingge@hkpc.org

Telephone Number

27886007

Website

www.hkpc.org

Address

HKPC Building, 78 Tat Chee Avenue, Kowloon, Hong Kong.

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