Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device Article Swipe
Pankhi Kashyap
,
Kajal Shivgan
,
Sheetal Patil
,
B. Ramana Raja
,
S. B. Mahajan
,
Sauvik Banerjee
,
Siddharth Tallur
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-54418-w
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-54418-w
Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units, various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e.g., temperature) and types of damages. Such models typically comprise of millions of trainable parameters, and therefore add to cost of deployment due to requirements of cloud connectivity and processing, thus limiting the scale of deployment of GW-SHM. In this work, we propose an alternative solution that leverages TinyML framework for development of light-weight ML models that could be directly deployed on embedded edge devices. The utility of our solution is illustrated by presenting an unsupervised learning framework for damage detection in honeycomb composite sandwich structure with disbond and delamination type of damages, validated using data generated by finite element simulations and experiments performed at various temperatures in the range 0–90 °C. We demonstrate a fully-integrated solution using a Xilinx Artix-7 FPGA for data acquisition and control, and edge-inference of damage. Despite the limited number of features, the lightweight model shows reasonably high accuracy, thereby enabling detection of small size defects with improved sensitivity on an edge device for online GW-SHM.
Related Topics
Concepts
Computer science
Structural health monitoring
Deep learning
Enhanced Data Rates for GSM Evolution
Delamination (geology)
Edge computing
Cloud computing
Ultrasonic sensor
Inference
Software deployment
Artificial intelligence
Machine learning
Materials science
Acoustics
Paleontology
Subduction
Operating system
Physics
Biology
Composite material
Tectonics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-024-54418-w
- https://www.nature.com/articles/s41598-024-54418-w.pdf
- OA Status
- gold
- Cited By
- 14
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391811851
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391811851Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-024-54418-wDigital Object Identifier
- Title
-
Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge deviceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-14Full publication date if available
- Authors
-
Pankhi Kashyap, Kajal Shivgan, Sheetal Patil, B. Ramana Raja, S. B. Mahajan, Sauvik Banerjee, Siddharth TallurList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-024-54418-wPublisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-024-54418-w.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.nature.com/articles/s41598-024-54418-w.pdfDirect OA link when available
- Concepts
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Computer science, Structural health monitoring, Deep learning, Enhanced Data Rates for GSM Evolution, Delamination (geology), Edge computing, Cloud computing, Ultrasonic sensor, Inference, Software deployment, Artificial intelligence, Machine learning, Materials science, Acoustics, Paleontology, Subduction, Operating system, Physics, Biology, Composite material, TectonicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 5Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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