The Self-aware factory made possible by the IoT

Discover how predictive maintenance made possible by Industrial IoT interoperability can improve production plant performance.

Predictive Maintenance

As part of the digital transformation process that is consolidating the role of Industrie 4.0 within production systems, the self-aware factory represents a natural evolution of current trends. The development of this new type of factory is based on a distributed intelligence model more closely aligned to the production line, on advanced sensors and on the most sophisticated data analysis and Machine Learning techniques available.

The latest developments facilitate the integration of latest generation sensors into existing equipment, using data supplied by means of production. This combination makes it possible to test the system in order to predict eventual failures and outages, reducing machine downtime, together with energy and materials consumption.

Reply supports its customers to exploit the potential of predictive maintenance providing end-to-end solutions, from the design of ad-hoc sensors to the integration with existing systems and specialised algorithms in function of the specific production sectors (Automotive, Machine Tools, Consumer goods).

Concept Reply, Reply's Research and Development Centre on the Internet of Things, is currently working on the ALMeS (Add-on, Low-cost, Multi-purpose Sensors) project, focused on overcoming cost-related barriers and other obstacles impeding the adoption of these innovative techniques.

The project, with the support of technology partners including the Politecnico di Milano, the Fondazione Bruno Kessler di Trento and ST Microelectronics, aims to demonstrate that predictive maintenance does not necessitate costly investments. The innovative ALMeS sensors solution relies on standard optical fibre, low cost microcontrollers and a machine learning software that can help reduce maintenance costs by 25-35%, eliminating 70% of outages and promoting a 25% increase in productivity.

Reply has been operating as a technology incubator for some of the most innovative start-ups in the IoT realm for a number of years. Several Group companies specialise in predictive maintenance. Senseye, for example, focuses on downtime and optimising machine OEE (Overall Equipment Effectiveness) within a facility. The Cloud Solution relies heavily on current and historical data so does not have high initial costs, facilitating a 30-50% reduction in downtime. We Predict, on the other hand, offers predictive analytics solutions in the automotive sector, concentrating on warranty cost savings with reductions of 8-15%.