Monitoring of Discrete Electrical Signals from Welding Processes using Data Mining and IIoT Approaches

Abstract

Processes such as welding involve consumption of huge amounts of energy leading to generation of significant electrical data consisting of current and voltage signals. The added task is to inspect the quality of welding using such data as early as possible to identify defects in producing welded parts or equipment. From the perspective of machine learning, this paper presents a data mining approach to analyse small sampled amounts of electrical signals to identify welding inconsistencies using conventional methods such as clustering algorithms, time-series and multi-label classifiers. Using unlabelled and discrete signals, an attempt is made to build a process profile on the welding robots with the use of comparison measures such as Jaccard's metric. To monitor such a mechanism, a simulation application based on IIoT standard Open Platform Communication (OPC UA) is developed to present the analysis over secure network servers to clients. The application setup presents a basic monitoring system for welding processes using available technologies like machine learning algorithms and OPC UA.