Databackoff – Intelligent Industrial Control With Big

DatabackOFF integrates cutting-edge technologies like BigData, IoT, and blockchain to enhance efficiency, predict failuresm and ensure ribust cybersecurity. Intelligent Industrial control with Big Data is transforming induatrial processes by leveraging advanced analytics and predictive maintenance.

DatabackOFF brings the following vision to current interrelated events such as the use of data warehouses, predictive maintenance, industrial cybersecurity, blockchain, IoT network management using LPWA technology.

Big Data | Predictive Maintenance

What is DatabackOFF?

Improve the overall efficiency level of the industrial process using analytical tools and ensure data security for the manufacturing process or management of manufactured and distributed capital goods, using Industry 4.0 enabling technologies for performing predictive maintenance, with the generation of alerts and anomaly prediction models from the existing dataset.

DatabackOFF problem to be solved: Enhancingindustrial process efficiency

This justification report aims to capture the actions carried out during the years 2017-2020 by Instituto Tecnológico de Castilla y León in the Data Scientists project in the Industrial Back Office within the call in non-competitive concurrence aimed at applied R&D projects carried out by the Technological Centers of Castilla y león, co-financed by the European Regional Development Fund (ERDF) (resolution of the President of ICE of August 28, 2017, BOCYL No. 180, of September 19, 2017).

DatabackOFF Objectives

The technical objectives were:

From the point of view of Big Data & Analytics technology.

With the design of intelligent algorithms for the management of the Industrial Back Office, the functions of data storage have been optimized to ensure an efficient management of information systems. By interlinking back office functionalities with industrial maintenance and machine safety events, we can reliably predict when breakdowns are likely to occur and evaluate the most important machine variables and determine correlations.
Integrating status information and parameters into a standardized management system allows us to track corrective and preventive processes, thanks to the integration of data sources and PLCs of each remote installation.

From the point of view of the ICS broker technology for industrial cybersecurity

By implementing algorithms of use for industrial plants and identifying the types of ICS cyber-risks at the industrial machine level, machine learning algorithms adapted to cybersecurity needs have been developed.

From the LPWA technology point of view in IoT communications.

After researching LPWA technologies, hardware platforms with this technology have been integrated into Big Data architectures.

From the Blockchain technology point of view

A prototype platform for certification, assurance and evidence of information in industrial network has been developed using Blockchain technology for the assurance of information in decentralized IoT platforms.

So the main results have been:

Effective tools for early detection of faults and/or failures within industrial predictive maintenance.
Functional prototypes of devices with LPWA technology capable of being integrated into industrial networks.
Development of a Big Data architecture for predictive maintenance.
Design of a security Broker with new algorithms for the securization of ICS.
Development of a Bolckchain network capable of managing users and their different roles, with a fully customized, robust and secure data management.
Design and implementation of a think tank that integrates all the demonstrators developed in the project.