Ultrasonic Fouling Detector Powered by Machine Learning
| Autor: | Edward Haggstrom, Joni Makinen, Tom Sillanpaa, Chang Rajani, Krista Longi, Ari Salmi, Timo Rauhala, Arto Klami |
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| Přispěvatelé: | Division of Pharmaceutical Chemistry and Technology, Materials Physics, Department of Computer Science, Multi-source probabilistic inference research group / Arto Klami, Helsinki Institute for Information Technology, Department of Physics |
| Rok vydání: | 2019 |
| Předmět: |
Materials science
Fouling Wave propagation business.industry Detector 010501 environmental sciences 113 Computer and information sciences Machine learning computer.software_genre 114 Physical sciences 01 natural sciences Finite element method Pipeline transport Transducer 0103 physical sciences Ultrasonic sensor Artificial intelligence 221 Nano-technology Dispersion (water waves) business 010301 acoustics computer 0105 earth and related environmental sciences |
| Zdroj: | 2019 IEEE International Ultrasonics Symposium (IUS). |
| DOI: | 10.1109/ultsym.2019.8925773 |
| Popis: | Guided waves can be used to monitor structural health in industrial pipelines, and e.g. allow detection of accumulated precipitation on the surface of pipe. Propagation of guided waves in a tubular structure carrying possible fouling can be separated from a clean structure due to variation in wave propagation properties at the fouled area. In addition, multiple propagation paths around the tubular structure allow locating the fouled areas. In this study, we obtained dispersion curves of a tubular structure loaded with a local fouling layer of different thickness by using numerical simulations. We combined the dispersion curve information with simulated and measured times-of-arrival of guided wave propagation to second order helicoidal paths and used a Gaussian Process machine learning approach to estimate location of fouling on a steel pipe. |
| Databáze: | OpenAIRE |
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