Overlay digitalisation for intelligent monitoring of manufacturing systems

Abstract

The contemporary journey towards data-driven operations is underway in almost all modern organisations, and manufacturing is not an exception. This work addresses several key challenges by: developing hardware and software infrastructure to reliably and economically acquire, process and present industrial data; proposing novel algorithms for analysing Industrial Big Data time series; verifying the proposed analytical techniques through real-world applications in a case study of a commercial bakery.Industry 4.0 heralds a revolution in the field of manufacturing but many of its promises remain undelivered outside experimental contexts. Uptake of digitalised operations is impeded by: capital expenditure, operating expenses, lack of data, the unclear tangible value of undertaking the digitalisation journey, and the challenges of converting data into actionable insights. The Overlay Digitalisation approach is proposed, which uses a modular and open-source architecture to mitigate the characteristic high costs and vendor lock-in of mainstream Industrial IoT platforms.The proposed Industrial Cyber Elements (ICE) Overlay Digitalisation system (spanning from retrofit wireless sensor node to data-handling web services) is developed and deployed in a large commercial bakery. 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.


Thesis embargoed until 31 December 2024

Date of Award	Jul 2022
Original language	English
Awarding Institution	Queen's University Belfast
Sponsors	Northern Ireland Department for the Economy
Supervisor	Wasif Naeem (Supervisor), Seán McLoone (Supervisor) & Kang Li (Supervisor)