Descripció del projecte
The digitization of the industry is undoubtedly a coming revolution that will redefine current production models, improving productivity and industrial efficiency. It will also have an impact on the quality and safety at the workplace, requiring at the same time more qualified labor, ultimately impacting on social welfare. Industrial digitization lays on different pillars: sensorics, communication systems, processing, as well as value generation via specialized data analytics, and finally, actuation and/or decision-making. In this research proposal we focus in those elements that support the development of advanced software services to enable smarter, more efficient and cleaner industrial machinery. The project focuses on industrial cooling systems, as a representative case of industrial equipment and it pursuits through digitization the optimization of their operation, ultimately making their operation totally predictable.
The goal of the research is to deep dive into novel technologies to support efficient digitalization of industrial assets, the transportation and transformation of the generated data into information and finally the knowledge extraction through advanced analytics and machine learning tools to support smarter and automatic decisions taking.
The key areas of research will be the following:
– Research of the state of the art for machinery automation and information digitization, understanding standards, formats and capacity requirements as well as their inter-connection to industrial existing equipment and/or assets.
– Research of the state of the art for advanced analytical techniques applied to industrial data sets, to support knowledge extraction and derive predictive maintenance applications. Ultimately, supporting automatic decision making.
– Performance and accuracy analysis of the developed techniques, applied to different industrial equipment, considering information from potentially a large dataset containing data from distributed machines.
– Development of advanced industrial asset management techniques and integration to operational workflows to better respond to the outcomes of the predictive services.
