Overlay digitalisation for intelligent monitoring of manufacturing systems

The system has acquired a dataset of more than 40 million observations over the course of 2 years, and has the potential for wide-spread impact due to the permissive licensing of its components. The novel F-QoS metric (a probabilistic measure of wireless network performance requiring only timestamps of transmissions) is developed and applied to assess LoRaWAN ISM-band sensor nodes, and shows that they are a viable solution for the difficult task of data acquisition in a harsh industrial environment.Two novel time series analysis algorithms, Binary Applied Threshold Segmentation (BATS) and Steady-State Extraction Segmentation (SES) are also formulated.

Both algorithms are shown to quickly and accurately extract operational regions from unevenly indexed time series, whilst preserving temporal crispness of state transitions. The Notional Alignment Index Lag Sampling (NAILS) method is developed to generate a pairwise comparison between segmented time series, and its ability to work directly with compressed representations extends the scale of analysis that can be undertaken with a fixed compute resource.A novel graph-based methodology is also proposed for analysing time series that arise from batch flow-line production systems, which includes the automatic discovery of system structure and parameters, and the programmatic instantiation of a Discrete Event Simulation.

This automated extraction of system knowledge from real-world unlabelled time series data represents a notable step towards automated semantic analysis of industrial data. The depth of knowledge embedded in simple counts of units travelling down a line is revealed by the development of an unbroken chain from raw observations to a self-structuring parametrised system simulation.<br>