Adaptive Payload Estimation: A Universal Payload Estimation System for Wheel Loaders Using Recurrent Neural Networks and Transfer Learning
| dc.contributor.author | Carlsson, Marcus | |
| dc.contributor.author | Hiljemark, Elin | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Mechanics and Maritime Sciences | en |
| dc.contributor.examiner | Forsberg, Peter | |
| dc.contributor.supervisor | Liljeqvist, Simon | |
| dc.date.accessioned | 2024-06-24T14:12:10Z | |
| dc.date.available | 2024-06-24T14:12:10Z | |
| dc.date.issued | 2024 | |
| dc.date.submitted | ||
| dc.description.abstract | A wheel loader is a versatile construction machine designed for various tasks related to the loading and transport of materials. In many wheel loader applications, accurately knowing the mass of the load being lifted is crucial. For example, when loading materials onto trucks, it is essential to ensure that the load of the truck does not exceed legal weight limits. Therefore, various solutions have been developed to help operators determine the payload mass. However, many existing systems use mechanical equations to estimate the payload, consequently these models are heavily dependent on the geometry of the machine it is implemented for. Using technologies emerging from the field of deep learning, this thesis aimed to investigate the possibilities of a universal payload estimation model. By leveraging real operational data from wheel loaders and formatting it into time sequences, a neural network based on long short-term memory units was constructed. In addition, a classification network was developed based on both convolutional neural network and autoencoder architectures to assess the reliability of data sequences. To generalize the model across different wheel loader models, transfer learning was employed. The results showed that transfer learning enabled the model to accurately estimate payload mass for different wheel loader models. However, the classification network for sensor data reliability was found to be redundant, as it reduced the performance of the payload estimation model. Furthermore, the result also showed a trade-off between the amount of data used for fine-tuning the general model and the accuracy of the model. To further investigate the adaptability and usage of the adaptive payload estimation model, more operational scenarios needs to be tested as well as further examination of the adaptability to different sizes of wheel loaders. | |
| dc.identifier.coursecode | MMSX30 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/308021 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | Technology | |
| dc.subject | Transfer learning | |
| dc.subject | LSTM | |
| dc.subject | payload estimation | |
| dc.subject | wheel loader | |
| dc.subject | deep learning | |
| dc.subject | load weighing | |
| dc.subject | construction equipment | |
| dc.title | Adaptive Payload Estimation: A Universal Payload Estimation System for Wheel Loaders Using Recurrent Neural Networks and Transfer Learning | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Information and communication technology (MPICT), MSc | |
| local.programme | Complex adaptive systems (MPCAS), MSc |