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Symbolic Analysis of Machine Behaviour and the Emergence of the Machine Language

  • Roland Ritt
  • Paul O’Leary
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)

Abstract

This paper takes a fundamental new approach to symbolic time series analysis of real time data acquired from human driven mining equipment, which can be seen as stochastic physical systems with non analytic human interaction (hybrid systems). The developed framework uses linear differential operators (LDO) to include the system dynamics within the analysis, whereas the metaphor of language is used to mimic the human interaction. After applying LDO, the multidimensional data stream is converted into a single symbolic time series yielding a more abstract but highly condense representation of the original data. Inspired by natural language, the presented algorithm combines iteratively symbol pairs (word pairs) which occur frequently to new symbols/words; a machine-specific language emerges in a hierarchical manner, which automatically structures the dataset into segments and sub-segments. As a demonstration, the automatic recognition of operation modes of a bucket-wheel excavator is presented, proving the metaphor of language to be valuable in such hybrid systems.

Keywords

Knowledge discovery Symbolic time series Emergence of language Compounding Segmentation Cyber physical system Hybrid systems 

Notes

Acknowledgments

This work was partially funded under the auspices of the EIT - KIC Raw materials program within the project “Maintained Mining Machine” (MaMMa) with the grant agreement number: [EIT/RAW MATERIALS/SGA2018/1]

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Chair of Automation – Department Product EngineeringUniversity of LeobenLeobenAustria

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