System Architecture for Diagnostics and Supervision of Industrial Equipment and Processes in an IoE Device Environment

Abstract

IoE components are becoming an integral part of our lives and support the operation of systems such as smart homes, smart cities, or Industry 4.0. The large number and variety of IoE components force the creation of flexible systems for data acquisition, processing, and analysis. The work presents a proposal for a new flexible architecture model and technology stack designed for the diagnostics and monitoring of industrial components and processes in an IoE device environment. The proposed solutions allow creating custom flexible systems for managing a distributed IoT environment, including the implementation of innovative mechanisms like, for example: predictive maintenance, anomaly detection, business intelligence, optimization of energy consumption, or supervision of the manufacturing process. In the present study, two detailed system architectures are proposed, and one of them was implemented. The developed system was tested in near-production conditions using a real IoT device infrastructure including industrial systems, drones, and sensor networks. The results showed that the proposed model of a central data-acquisition and -processing system allows the flexible integration of various IoE solutions and has a very high implementation potential wherever there is a need to integrate data from different sources and systems.