Automatic grouping of production data in Industry 4.0: The use case of internal logistics systems based on Automated Guided Vehicles

Abstract

Automated Guided Vehicles (AGVs) have become an indispensable component of Flexible Manufacturing Systems. AGVs are also a huge source of information that can be utilised by the data mining algorithms that support the new generation of manufacturing. This paper focuses on data preprocessing, aggregation, and clustering in the new generation of manufacturing systems that use the agile manufacturing paradigm and utilise AGVs. This paper presents a method by which it is possible to automatically detect the types of work performed by an AGV based on streaming information. The paper presents a comparison of results obtained with two different classical clustering algorithms: K-Means and DBScan. As the results show, using at least one of the proposed algorithms, it is possible to automatically detect the types of work performed by AGVs and to detect new, previously unknown types. Additionally, to a limited extent, it is possible to identify anomalies using this method. The proposed methodology can be used as the initial step for production optimisation, predictive maintenance activities, production technology verification, or as a source of models for the simulation tools that are used in virtual factories. This is an extended version of the ‘Data preprocessing, aggregation and clustering for agile manufacturing based on Automated Guided Vehicles’ paper [43].