Predictive Maintenance 4.0

In-plant data acquisition

In factory production processes, there are certain parameters that can influence the deterioration of physical assets or even the quality of the product, such as: temperatures, pressures, vibration, lack or excess of lubrication, etc.

Monitoring these parameters throughout the process and relating them to consolidated events such as breakdowns, production stoppages or quality defects through systems such as SCADA’s or MES platforms is the first step to implement a good predictive strategy in the plant.

Data and predictive model interpretation

Once the correct data collection is guaranteed and there are indications of which parameters may be influencing plant events, the next step is to apply the necessary predictive models and algorithms for the automatic detection of malfunction patterns or anomalous situations to guarantee a timely intervention that can anticipate undesired events.


Predictive maintenance pilot and start-up

Given the complexity of these projects, Geprom’s proposal is to start with a pilot project that follows the steps indicated above to ensure that the proposed technology can anticipate possible failures, shutdowns or defects.

The basis of these projects is to take advantage of existing plant sensors as much as possible or to install the appropriate sensors where necessary to bring this data to the most appropriate monitoring platform, identify the correct predictive models and design a reporting screen that is useful for real-time decision making.

Predictive Maintenance 4.0: Benefits

Grupo 725

Improved general reliability


Reduces equipment intervention rate / year.

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Follow-up results in reduced accidents and improved safety.


Reduce spare parts expenditure.


Reduced industrial insurance cost through improved KPI’s.

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Reduction of general failures by monitoring data.