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

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.

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