Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization
This work leverages ultrasonic guided waves (UGWs) to detect and localize damage in structures using lightweight Artificial Intelligence (AI) models. It investigates the use of machine learning (ML) to train the effects of the damage on UGWs to the model. To reduce the number of trainable parameters...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/578 |
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| author | Jannik Henkmann Vittorio Memmolo Jochen Moll |
| author_facet | Jannik Henkmann Vittorio Memmolo Jochen Moll |
| author_sort | Jannik Henkmann |
| collection | DOAJ |
| description | This work leverages ultrasonic guided waves (UGWs) to detect and localize damage in structures using lightweight Artificial Intelligence (AI) models. It investigates the use of machine learning (ML) to train the effects of the damage on UGWs to the model. To reduce the number of trainable parameters, a physical signal processing approach is applied to the raw data before passing the data to the model. Starting from current state of the art in algorithms used for damage detection and localization, an AI-based technique is developed and validated on an experimental benchmark dataset before tiny ML implementation on a low-cost development board. A discussion of the need for a balance between the reduction in computational resources and increasing the precision of the models is also reported. It is shown that by extracting simple features of the signal, the models required to predict the damage locations can be significantly reduced in size while still having high accuracies of over 90%. In addition, it is possible to use these predictions to construct a fairly accurate heat map indicating the likely damage locations. Finally, a convenient edge/cloud visualization of the results can be achieved by simplifying the heat map. |
| format | Article |
| id | doaj-art-80f6951fd799461d91ca307b97c87a6d |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-80f6951fd799461d91ca307b97c87a6d2025-01-24T13:49:25ZengMDPI AGSensors1424-82202025-01-0125257810.3390/s25020578Tiny Machine Learning Implementation for Guided Wave-Based Damage LocalizationJannik Henkmann0Vittorio Memmolo1Jochen Moll2AG Terahertz-Photonik Physikalisches Institut, Johann Wolfgang Goethe-Universität, Max-von-Laue-Strasse 1, 60438 Frankfurt am Main, GermanyDepartment of Industrial Engineering, Universitá degli Studi di Napoli ‘Federico II’, Via Claudio 21, 80125 Naples, ItalyDepartment of Mechanical Engineering, University of Siegen, Paul-Bonatz-Straße 9-11, 57076 Siegen, GermanyThis work leverages ultrasonic guided waves (UGWs) to detect and localize damage in structures using lightweight Artificial Intelligence (AI) models. It investigates the use of machine learning (ML) to train the effects of the damage on UGWs to the model. To reduce the number of trainable parameters, a physical signal processing approach is applied to the raw data before passing the data to the model. Starting from current state of the art in algorithms used for damage detection and localization, an AI-based technique is developed and validated on an experimental benchmark dataset before tiny ML implementation on a low-cost development board. A discussion of the need for a balance between the reduction in computational resources and increasing the precision of the models is also reported. It is shown that by extracting simple features of the signal, the models required to predict the damage locations can be significantly reduced in size while still having high accuracies of over 90%. In addition, it is possible to use these predictions to construct a fairly accurate heat map indicating the likely damage locations. Finally, a convenient edge/cloud visualization of the results can be achieved by simplifying the heat map.https://www.mdpi.com/1424-8220/25/2/578edge AImachine learningultrasonic guided wavesstructural health monitoringtiny device |
| spellingShingle | Jannik Henkmann Vittorio Memmolo Jochen Moll Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization Sensors edge AI machine learning ultrasonic guided waves structural health monitoring tiny device |
| title | Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization |
| title_full | Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization |
| title_fullStr | Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization |
| title_full_unstemmed | Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization |
| title_short | Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization |
| title_sort | tiny machine learning implementation for guided wave based damage localization |
| topic | edge AI machine learning ultrasonic guided waves structural health monitoring tiny device |
| url | https://www.mdpi.com/1424-8220/25/2/578 |
| work_keys_str_mv | AT jannikhenkmann tinymachinelearningimplementationforguidedwavebaseddamagelocalization AT vittoriomemmolo tinymachinelearningimplementationforguidedwavebaseddamagelocalization AT jochenmoll tinymachinelearningimplementationforguidedwavebaseddamagelocalization |