Abstract
We study methods for observing physical defects on the surface of power cables. Quality control is essential for power cable manufacturing and surface defects are an important quality factor. Traditionally power cable manufacturing has relied on manual inspection as automated methods have not been sufficient to be used in industrial production. We have designed and implemented a novel defect detection system that applies machine learning methods to detect power cable surface defects. Our system uses laser scanning to map the surface of a cable during production. For the machine learning, we have evaluated different CNN (Convolutional Neural Net- work) architectures and studied their performance and accuracy. According to our results, CNNs are suitable for the detection of surface defects as they can be trained with large amounts of cable surface data.
| Original language | English |
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| Title of host publication | Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019 |
| Publisher | IEEE |
| Pages | 193-198 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-7281-2927-3 |
| DOIs | |
| Publication status | Published - 2019 |
| MoE publication type | A4 Conference publication |
| Event | IEEE International Conference on Industrial Informatics - Aalto University, Helsinki and Espoo, Finland Duration: 22 Jul 2019 → 25 Jul 2019 Conference number: 17 https://www.indin2019.org/ |
Publication series
| Name | IEEE International Conference on Industrial Informatics |
|---|---|
| ISSN (Electronic) | 2378-363X |
Conference
| Conference | IEEE International Conference on Industrial Informatics |
|---|---|
| Abbreviated title | INDIN |
| Country/Territory | Finland |
| City | Helsinki and Espoo |
| Period | 22/07/2019 → 25/07/2019 |
| Internet address |
Keywords
- deep learning
- convolutional neural networks, power cables, quality controlvolutional neural networks
- power cables
- quality control