Ansaldo

The Challenge

The Lighthouse Plant project: Digitization of production sites & Predictive Maintenance

Alleantia is among the partners of Ansaldo Energia’s Lighthouse Plant project, the only one in Italy relating to an Italian company that will invest 14 million euros in a three-year industrial research and development plan based on the main digital technologies of the Industry 4.0 Plan. A project that will affect the entire manufacturing process of the two Ansaldo Energia production sites in Genoa and in which Alleantia played a fundamental role as regards the interconnection methods of the machines.

The data collection of the machines made by Alleantia allows to pass the information to the SAP Leonardo platform and to provide the information that allows the Polytechnic of Milan to study the model that defines the digital Twin of the physical machine allowing precisely to identify a series of critical parts of the machine and on these a series of clinical variables worthy of a monitoring that can allow some machine and learning algorithms to provide the system with feedback, i.e. information of malfunctions or drift of the product quality of the machine itself that with a period adequate allow to give vital information to the operator and to prevent machine downtime and malfunction problems.

The role of Alleantia

Alleantia is located in the edge part. that is, it connects physically or to physical machines or to PLCs and allows you to recover variables for different construction types (hydraulic unit, cooler, spindle) allowing you to retrieve the telemetry information that provides machine learning algorithms with this ability to predict malfunction or of bad production not congruent with the prerequisites

Predictive maintenance or advanced maintenance capable of diagnosing and prognosticating the operation of one or more machines on the basis of data machines arriving from the machines themselves. This is because predictive maintenance requires data from the machines to know their proper functioning, possibly their anomalies and finally failures.

These data become important from the application point of view because then processed with appropriate machine learning algorithms called analytics, they allow you to better understand the functioning of the machine but everything starts from connectivity. Connectivity with the machine requires the ability to extract data from a pre-existing automation and therefore this is a key element to be able to then bring the development of predictive maintenance hooked to the machines in an application key.