Machine activity recognition is important for accurately estimating 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 demonstrate 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 representations 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.

Authors and Affiliations

1. Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden

   Kunru Chen, Sepideh Pashami, Sławomir Nowaczyk, Emilia Johansson, Gustav Sternelöv & Thorsteinn Rögnvaldsson

2. Toyota Material Handling Europe, Mjölby, Sweden

   Kunru Chen, Sepideh Pashami, Sławomir Nowaczyk, Emilia Johansson, Gustav Sternelöv & Thorsteinn Rögnvaldsson

Corresponding author

Correspondence toKunru Chen.

Editors and Affiliations

1. University of Porto, Porto, Portugal

   Joao Gama

2. Halmstad University, Halmstad, Sweden

   Sepideh Pashami

3. Waikato University, Hamilton, New Zealand

   Albert Bifet

4. University of Lille, Lille, France

   Moamar Sayed-Mouchawe

5. Heidelberg University, Heidelberg, Germany

   Holger Fröning

6. Graz University of Technology, Graz, Austria

   Franz Pernkopf

7. University of Duisburg-Essen, Essen, Germany

   Gregor Schiele

8. XILINX Research, Dublin, Ireland

   Michaela Blott

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