Low-cost prototype for bearing failure detection using Tiny ML through vibration analysis
The document presents a low-cost, open-source device designed to facilitate the learning of technologies like artificial intelligence in embedded systems through vibration analysis. It also aims to enhance students’ skills by introducing industrial challenges into the classroom via a scaled-down pro...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | HardwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468067225000367 |
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| author | Andres Felipe Cotrino Herrera Jesús Alfonso López Sotelo Juan Carlos Blandón Andrade Alonso Toro Lazo |
| author_facet | Andres Felipe Cotrino Herrera Jesús Alfonso López Sotelo Juan Carlos Blandón Andrade Alonso Toro Lazo |
| author_sort | Andres Felipe Cotrino Herrera |
| collection | DOAJ |
| description | The document presents a low-cost, open-source device designed to facilitate the learning of technologies like artificial intelligence in embedded systems through vibration analysis. It also aims to enhance students’ skills by introducing industrial challenges into the classroom via a scaled-down prototype. This study analyzes the vibrations generated by bearings to classify, using Artificial Intelligence (AI), whether they are defective. The device integrates electronic, mechanical, and software components, leveraging online technologies and platforms like Arduino to support hands-on learning. The document provides detailed instructions on the components used, circuit connections, step-by-step construction, and implementation, allowing replication of the prototype. This device fosters the development of STEM skills, promotes the application of AI and TinyML in real-world contexts, and enriches educational programs by encouraging interdisciplinary learning. |
| format | Article |
| id | doaj-art-e79f84ac2f234e2096d3afa43d379d08 |
| institution | DOAJ |
| issn | 2468-0672 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | HardwareX |
| spelling | doaj-art-e79f84ac2f234e2096d3afa43d379d082025-08-20T03:11:26ZengElsevierHardwareX2468-06722025-06-0122e0065810.1016/j.ohx.2025.e00658Low-cost prototype for bearing failure detection using Tiny ML through vibration analysisAndres Felipe Cotrino Herrera0Jesús Alfonso López Sotelo1Juan Carlos Blandón Andrade2Alonso Toro Lazo3School of Engineering and Basic Sciences, Universidad Autónoma de Occidente, Cali, Colombia; Corresponding author.School of Engineering and Basic Sciences, Universidad Autónoma de Occidente, Cali, ColombiaSystems and Telecommunications Engineering Program, Universidad Católica de Pereira, Pereira, ColombiaSystems and Telecommunications Engineering Program, Universidad Católica de Pereira, Pereira, ColombiaThe document presents a low-cost, open-source device designed to facilitate the learning of technologies like artificial intelligence in embedded systems through vibration analysis. It also aims to enhance students’ skills by introducing industrial challenges into the classroom via a scaled-down prototype. This study analyzes the vibrations generated by bearings to classify, using Artificial Intelligence (AI), whether they are defective. The device integrates electronic, mechanical, and software components, leveraging online technologies and platforms like Arduino to support hands-on learning. The document provides detailed instructions on the components used, circuit connections, step-by-step construction, and implementation, allowing replication of the prototype. This device fosters the development of STEM skills, promotes the application of AI and TinyML in real-world contexts, and enriches educational programs by encouraging interdisciplinary learning.http://www.sciencedirect.com/science/article/pii/S2468067225000367Artificial intelligenceMachine learningTeaching strategyVibration analysis |
| spellingShingle | Andres Felipe Cotrino Herrera Jesús Alfonso López Sotelo Juan Carlos Blandón Andrade Alonso Toro Lazo Low-cost prototype for bearing failure detection using Tiny ML through vibration analysis HardwareX Artificial intelligence Machine learning Teaching strategy Vibration analysis |
| title | Low-cost prototype for bearing failure detection using Tiny ML through vibration analysis |
| title_full | Low-cost prototype for bearing failure detection using Tiny ML through vibration analysis |
| title_fullStr | Low-cost prototype for bearing failure detection using Tiny ML through vibration analysis |
| title_full_unstemmed | Low-cost prototype for bearing failure detection using Tiny ML through vibration analysis |
| title_short | Low-cost prototype for bearing failure detection using Tiny ML through vibration analysis |
| title_sort | low cost prototype for bearing failure detection using tiny ml through vibration analysis |
| topic | Artificial intelligence Machine learning Teaching strategy Vibration analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2468067225000367 |
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