An approach towards Zero Defect Manufacturing by combining IIoT data with Industrial Social Networking
Kosmas Alexopoulos, Thodoris Tsoukaladelis, Chrysa Dimitrakopoulou, Nikolaos Nikolakis, Amit Eytan
Abstract
Zero Defect Manufacturing (ZDM) is intrinsically a multi-disciplinary approach. Recently a lot of attention has been given towards implementing data analytics using machine learning approaches upon timeseries data collected by Industrial Internet of Things (IIoT) systems during production. However, other types of data, such as text, that capture the human knowledge and experience from people working in manufacturing can be useful for ZDM. In this context, Industrial Social Network (ISN) solutions can be very efficient for serving communication needs in a manufacturing setting and can be used for capturing such informal knowledge, also facilitating sharing of know-how among employees. This work presents a method implemented in software prototype combining IIoT with textual data available in ISN into a knowledge base towards enabling ZDM. A dashboard for informal discussions of employees on the shopfloor status as depicted through IIoT data has been conceived and implemented within ISN. An ontology has been designed and implemented in semantic repository for linking IIoT information with textual information shared through the dashboard. The proposed system has been validated with a set of simulated IIoT data and textual information stored in ISN.