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Forklift Truck Activity Recognition from CAN Data
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-6420-8316
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-3272-4145
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
Toyota Material Handling Europe, Mjölby, Sweden.
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2021 (English)In: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning: Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers / [ed] Joao Gama, Sepideh Pashami, Albert Bifet, Moamar Sayed-Mouchawe, Holger Fröning, Franz Pernkopf, Gregor Schiele, Michaela Blott, Heidelberg: Springer, 2021, p. 119-126Conference paper, Published paper (Refereed)
Abstract [en]

Machine activity recognition is important for accurately esti- mating machine productivity and machine maintenance needs. In this paper, we present ongoing work on how to recognize activities of forklift trucks from on-board data streaming on the controller area network. We show that such recognition works across different sites. We first demon- strate the baseline classification performance of a Random Forest that uses 14 signals over 20 time steps, for a 280-dimensional input. Next, we show how a deep neural network can learn low-dimensional representa- tions that, with fine-tuning, achieve comparable accuracy. The proposed representation achieves machine activity recognition. Also, it visualizes the forklift operation over time and illustrates the relationships across different activities. © Springer Nature Switzerland AG 2020

Place, publisher, year, edition, pages
Heidelberg: Springer, 2021. p. 119-126
Series
Communications in Computer and Information Science, ISSN 1865-0929
Keywords [en]
Machine Activity Recognition, Learning representation, Autoencoder, Forklift truck, CAN signals, Unsupervised learning
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-44103DOI: 10.1007/978-3-030-66770-2_9Scopus ID: 2-s2.0-85101578762ISBN: 978-3-030-66769-6 (print)ISBN: 978-3-030-66770-2 (electronic)OAI: oai:DiVA.org:hh-44103DiVA, id: diva2:1541981
Conference
ITEM 2020/IoT Streams 2020, IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, Ghent, Belgium, 14-18 September, 2020
Funder
Knowledge Foundation, 20200001Available from: 2021-04-06 Created: 2021-04-06 Last updated: 2023-11-17Bibliographically approved
In thesis
1. Learning Representations for Machine Activity Recognition
Open this publication in new window or tab >>Learning Representations for Machine Activity Recognition
2022 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Machine activity recognition (MAR) is an essential and effective approach for equipment productivity monitoring. Developing MAR methods for forklift trucks, a vital piece of the industry, can benefit productivity efficiency, maintenance service, product design, and potential savings. With the growth of the Internet of Things, a large amount of sensory data has become accessible. Conventional MAR methods that have been developed primarily focus on data collected from external sensors, such as inertial measurement units (IMUs) and cameras. However, they are not effective for forklift applications: the IMU data does not reflect kinematic patterns due to a lack of large articulated parts, while the vision-based data collection requires many cameras to create sufficient coverage of an indoor environment, which, in result, risks the privacy and is less economical. Moreover, typical objectives in the existing MAR works are heavy equipment in construction sites where the working environment and tasks differ from the logistics sector. Therefore, it is necessary to develop intelligent and innovative approaches that are more suitable for forklift trucks.

This thesis demonstrates developing and utilizing representation learning methods to solve forklift MAR problems, based on the assumption that forklift activities are formed by a series of basic movements that can be detected from the onboard communication, i.e., signals in a Controller Area Network (CAN). Most of the methods proposed in this thesis incorporate semi-supervised techniques to deal with the limited amount of labeled data and to capitalize on a large amount of unlabeled data in our experiments. Deep neural networks are implemented to overcome different challenges of recognizing forklift activities and learn various representations of the data: i) learning invariant features to reconstruct input CAN signals by applying autoencoders, ii) learning discriminative features to recognize forklift activities by fine-tuning pre-training networks, and iii) learning temporal coherence to capture activity transitions by implementing gated recurrent units. Apart from achieving promising classification performance for forklift MAR problems, the representations obtained also support visualization and interpretability of the data as they are three-dimensional. Our ongoing works are new experiments about learning domain-invariant features, where domain adaptation methods are implemented to recognize activities performed by forklift trucks from different sites.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2022. p. 67
Series
Halmstad University Dissertations ; 94
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-48549 (URN)978-91-89587-01-4 (ISBN)978-91-89587-00-7 (ISBN)
Presentation
2022-12-13, Wigforssalen in Hus J, Halmstad University, Kristian IV:s väg 3, Halmstad, 14:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation, 20200001
Available from: 2022-10-31 Created: 2022-10-31 Last updated: 2022-11-01Bibliographically approved

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Chen, KunruPashami, SepidehNowaczyk, SławomirRögnvaldsson, Thorsteinn

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