Abstract
This article presents an innovative visual method for measuring material mass online by quantified conveyor belt deformation with deep learning, which offers a noncontact and safe alternative to traditional pressure- and radioactivity-based weighing techniques. The correlation between the belt deformation and the carried material mass is further investigated through finite element simulations. Then, a visual weighing method by belt deformation is proposed, comprising a calibration algorithm to construct a measurement model using a gated recurrent unit-based network, and an online measurement algorithm to calculate material mass with the trained network. Finally, a case study is presented to analyze the effect of different dimension configurations and networks. The results validate that the proposed method attains a notable accuracy and is suitable for high-velocity conveyor environments. The demonstrated benefits signify an advancement in visual perception of materials, enabling a new approach for intelligent operation and monitoring in material handling field.
| Original language | English |
|---|---|
| Pages (from-to) | 960-969 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Industrial Informatics |
| Volume | 21 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2025 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Belt conveyor
- belt deformation
- deep learning
- gated recurrent unit (GRU)
- mass estimation
- material mass flow measurement