LoLiPoP IoT

One of the main challenges for companies is to improve their competitiveness, and preventive maintenance can be a good solution.

Foresight and anticipation can bring huge savings to industries. Through devices and algorithms that facilitate data collection and subsequent analysis, any company can anticipate its future needs and optimise its investments.

The LoLiPoP IoT project is part of the development of energy harvesting and micro-power management solutions for IoT devices, enabling tasks such as condition monitoring for predictive/predictive maintenance (ML-AI) and asset tracking in Industry 4.0.

Spanish Cluster

TST Systems

Polytechnic University of Madrid (UPM)

Fersa

Smart bearing monitoring

For the realisation of this initiative, TST’s work has been directed to:

– The design and construction of a device integrated in the FERSA bearing that, in turn, integrates sensors, microcontrollers, communications and batteries with the help of UPM energy management managers.

– The development of design and ML (machine learning) algorithms that enable the monitoring of the condition of bearings, so that failures can be prevented, loads monitored, system failures alerted and remaining bearing life predicted.