Success Story

reading time: 2 min

Manufacturing

Mechanical Engineering

Predictive Quality

Predictive Maintenance

Context

The EU had issued a call for a research project to tackle the problem of processing data and error codes from robots, with the aim to predict critical errors. This is how the ESMERA Suspicion project was born.

Challenge

Modern industrial manufacturing facilities are highly interconnected and automated. Sometimes errors can occur (e.g., cable of welding equipment is failing), that lead to downtimes which turn out to be cost intensive. Knowing when an error might occur helps industrial experts to prepare in advance and reduce the repair time for getting everything up and running again.

Assignment

To take a time window to process data and predict the probability of an error occurring, for each error type. The customer came with a rough technical idea and the initial database model to use.

Solution

We follow a SCRUM-like approach where we discuss new features with the customer, create a technical concept to be reviewed by the customer, and then the features are being implemented. Data and production knowledge has been offered by the challenge provider Magna.

In the ESMERA Suspicion research project, we created a software solution that can predict upcoming errors of a robotic cell with high certainty and is the basis for prescriptive maintenance.

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three male team members of craftworks at a meeting table looking at laptops and working

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