USING SEQUENTIAL CLUSTERING TO IDENTIFICATION OF HYBRID TIMED AUTOMATA FROM ANALOG IIOT DATA

Abstract

In Industrial IoT (IIoT) systems, timed automata provide a highly useful abstraction for diagnosis and control tasks. Applying them requires automaton to be learned in passive online manner using positive samples only. Such kind of learning is supported by Hybrid timed Automata (HTA) and algorithm OTALA, but requireds a sequence of discrete events rather than continuous analog time series typically found in IIoT. Recent attempts to cover this gap, taken by A. von Birgelen, O. Niggemann, and others, involved pre-processing observations with a self-organized map (SOM) and watershed transform, yet resulting models have proven ineffective in some real-world systems. In this paper, incremental model-based clustering (IMCF) is employed to learn timed automaton from analog IIoT data. IMCF is a sequential algorithm that processes observed time-series online and splits them into a sequence of discrete states with either crisp or fuzzy transitions between them. Such transitions are then treated as events required for HTA identification with OTALA. Obtained models are evaluated in a case of IIoT system that has proved to be challenging for existing modelling techniques. Experimental results show 24,9–76,8% increase in model’s performance and suggest that discretizing obtained with IMCF has higher informativeness for HTA identification. Finally, wider perspectives of applying HTA in IIoT are discussed, and remaining principal limitations are identified as discrete nature of state transitions, and lack of long-term memory for transitions.