Integral Support Predictive Platform for Industry 4.0

• Main Author: Sergio Márquez Sánchez
• Format: Article
• Language: English
• Published: Ediciones Universidad de Salamanca 2020-12-01
• Series: Advances in Distributed Computing and Artificial Intelligence Journal
• Subjects: iiot; artificial intelligent; deep learning; neural networks; big data
• Online Access: https://revistas.usal.es/index.php/2255-2863/article/view/25210

Currently, companies in the industrial sector are focusing their efforts on incorporating the advances contained in the Industry 4.0 model, to continue competing in an increasingly high-tech market. These advances, in addition to productivity, have a remarkable impact on the working environment of workers and on the measures adopted to maintain a healthy workspace. Thus, for example, there are projects to develop augmented reality technologies for maintenance and industrial training, advanced modelling tools for additive manufacturing, or Big Data analysis platforms for industrial data. However, the solutions designed are too specific to a particular industry problem or the platforms proposed are too generalist and not easily adaptable to the industries. This work seeks to provide a reference software architecture at the service of the connected industry that allows the provision of new capacities for process optimisation, predictive maintenance and real-time visualisation, integrating all the relevant information generated by the existing systems, incorporating new sources of data resulting from the digital society, and ensuring future compatibility with the new sources of information, solutions and Industrial Internet of Things (IIoT) devices that may be implemented.