Currently, industrial machines have sensors connected to programmable logic controllers (PLC’s) that recollect information of parameters, such as temperature and pressure level. We created a “machine to customer” experience with the PLC’s, using a raspberry pi computer board to validate and transform the data collected by the PLC to send it to the cloud. That means that the hardware was in charge of sending the information from time to time, transforming and validating the data without human intervention. 4TUNA sends alerts to technicians and operators when it detects that the operating values are not in the expected range, allowing them to take proactive actions and avoid possible failures. The historical information of all these parameters are stored so that machine operators can view them and over time these could be used as a predictive tool for future fault detection. This information is sent to the cloud that we implement with AWS and an "infrastructure as code" approach.

Since the project ended in 2019, 4TUNA has collected and analyzed more than 100 million readings of variables from different machines and protocols using our IoT solution. 4TUNA has detected and sent more than 1000 alerts in real time of deviations, reducing by 60% the risk of failures in the machines.