Smart Monitoring for Energy Efficiency and Predictive Maintenance – Application to Electric Motors Retrofitting
2019-2020 | H2020

The SUPREEMO experiment developed advanced CPS / IOT technologies and edge/cloud data-analytics, to develop a data-driven approach for equipment fault detection, and test its application for the improvement of Energy Efficiency and to support Predictive Maintenance. The proposed solution is inexpensive and easy to deploy, and is particularly addressed to SMEs seeking cost effective industry 4.0-retrofitting-based solutions, to assist the transition to the Smart Factory era. The focus will be on Electric Motor-Driven Systems (EMDS), used in pumps, fans, compressors, and material handling and processing. They consume around 2/3 of the electrical energy used in industry, their environmental footprint is, therefore, significant; and they have energy efficiency potential estimated as 10% of the global electricity demand.


The experiment included the following:

  • Collection of large volumes of electricity consumption data using custom high frequency sampling sensors, data compression (fog PC) and data transmission to the cloud.
  • Development of advanced deep learning models for data fusion and data analysis at the cloud.
  • Development of a user interface for device monitoring, and decision support to increase equipment availability, reduce production losses and improve energy efficiency.


SUPREEMO was supported financially by H2020 through the 2nd Open Call of MIDIH: “Manufacturing Industry Digital Innovation Hubs” (H2020-FOF-12-2017, GA 767498).

Experiment websites:  &

MIDIH Project website:


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