Data science in mechanical engineering

More efficient processes, higher quality or new digital services: the possible applications of data science in mechanical and plant engineering are as diverse as the resulting advantages. With more than 10 years of experience, we are the contact for everything to do with data science for mechanical engineering.

We transform machine data into real assets and create competitive advantages for machine builders based on data and algorithms. To this end, we support the mechanical engineering industry both in the implementation of analysis projects and in building up knowledge and the appropriate technology landscape for the productive use of data science.

A selection of questions we have answered in our projects for machine and plant manufacturers:


How can predictive maintenance be achieved?


Analysis of sensor data to detect anomalies in advance of historical failures. Creating the possibility to detect problems before they occur.

How to create a new customer service based on data?


Development of a "health indicator" about the condition of the machines. Customer satisfaction increases through a better understanding of the current machine status and higher availability of the machines.

How can product quality be continuously improved?


Use of deep learning for continuous quality monitoring and early detection of defective welding seams based on image data.

How can production be made more efficient?


Set up a digital twin, to bundle all relevant machine information and analyse 3D models of the individual machine components.

How can the prediction of the hit rate of offers be improved?


Determination and analysis of all relevant influencing variables for a reliable prediction of the probability of closure. Instrument for controlling the effort involved in preparing offers and for improving the sales forecast.

How can data quality problems with operating hours be successfully solved?


Development of an algorithm that detects structural errors in the recording of machine operating hours and corrects them appropriately. The basis for further analysis steps.

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Selected reference projects


Predictive Maintenance –TRUMPF Lasertechnik

eoda has helped TRUMPF Laser Technology to increase its "Industry 4.0 Maturity Level" based on data and algorithms.

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Predictive Maintenance Trumpf Control Room

New customer services with predictive maintenance

Development of a "health indicator" for Schenck Process Europe GmbH

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Firmensitz von Schenck Process

Predictive maintenance with deep learning

We have developed a deep learning model for a machine builder to reliably predict machine failures.

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Our data science solutions


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Oliver Bracht

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