Quantitative Identification of Pipeline Crack Based on BP Neural Network
| Autor: | Ming Jiang, Shujun Liu, Sheng Lin Li, Dean He |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
business.industry Computer science Mechanical Engineering 02 engineering and technology computer.software_genre Pipeline (software) Identification (information) 020303 mechanical engineering & transports 020401 chemical engineering 0203 mechanical engineering Mechanics of Materials General Materials Science Artificial intelligence Data mining 0204 chemical engineering business computer |
| Zdroj: | Key Engineering Materials. 737:477-480 |
| ISSN: | 1662-9795 |
| DOI: | 10.4028/www.scientific.net/kem.737.477 |
| Popis: | In the paper, the Metal Magnetic Memory Testing signal of pipeline crack is extracted. The BP neural network is constructed and trained. The experiment shows that the BP neural network can effectively identify the crack parameters of oil and gas pipeline in quantitative. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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