The question of energy efficiency in the manufacturing industry has driven the shift to a new paradigm through new efficient, smart systems. Due to increasing concerns about climate change and the transformation to sustainable systems, research trends have arisen from different perspectives to try to resolve these concerns.
Various approaches have been addressed, considered from the perspective of energy sources and their distribution to industries, and energy use in manufacturing processes by industries. Some research has focused on finding a more efficient way to supply energy to industries reducing distribution losses and maximise energy production from renewable energy sources.
The aim of TOFMAN is to propose methodologies and strategies for plants to implement in real time these improvements in energy consumption, the management of manufacturing and the planning of production. This smart manufacturing approach will be integrated within the frameworks of the internet of things and industry 4.0.
In this context, TOFMAN has mainly focused on online monitoring based on advanced machine learning techniques developed by members to improve maintenance efficiency (cost-availability) and consumption. The main areas of application have been monitoring of the state of industrial devices through the use of energy measures.
This information reveals the state of handling devices and their degradation, which enables maintenance to be programmed, avoids unexpected shut-downs and facilitates greater traceability and improvement in maintenance processes. In addition, the analysis of these energy signals in multi-bands at the same time reduces the number of sensors (that is, the costs and faults), provides an overview of the state of a line of luggage transport, and warns of anomalies in groups of bands.
Currently, the main examples of application of TOFMAN include general manufacture (analysis and assembly of the main devices) and infrastructures such as bridges and inspection systems for luggage at airports.
This project, which was carried out in collaboration between the Institute of Robotics and Industrial Informatics (IRI), CSIC-UPC and Aingura IIoT, started in September 2020 and ends in December 2022. It has a total budget of €123,762.