Business Lounge

Alexander von Birgelen M.Sc.

Software Engineer

No manual programming of complex rules and algorithms

In automation, the use of machine learning is becoming increasingly widespread, with the application often focusing on already familiar subject areas: Condition Monitoring (CM) and Predictive Maintenance (PM). In a use case, data from normal plant operation is used to learn a model that, compared with live data, indicates anomalies and indicates wear.

Anne Breuer, M.A.

PLCnext Technology  / Technical Documentation

A virus shakes the world and the world as we know it is now a different one. The changes affect our everyday life, our work and private life, our plans for the future and sometimes our view of the past. We are facing new challenges. Challenges for which we do not yet have solutions. What really matters now is above all: technology and kindness.

Christian Vilsbeck

A&D

Applications with AI

Artificial intelligence can be used very quickly and easily for the quality control of products. There are ready-to-use AI-based software solutions that evaluate image material from industrial cameras in real time. Based on the images, the AI system learns what the product looks like in ideal condition and which tolerances and irregularities are still permissible. In this way, even the smallest scratches or deviations are reliably detected. Compared to manual inspection by employees, which requires high concentration and is tiring, the error rate can be significantly reduced and the inspection throughput increased.

Christian Vilsbeck

A&D

Modern machines and production facilities supply vast amounts of data. The art lies in generating added value from this information. With artificial intelligence, correlations can be formed and processes can be optimized by self-learning. To do this, however, mechanical engineers and plant operators need a future-proof technological basis on which they can introduce their AI competence step-by-step and in a scalable manner. A powerful and open control platform is the basis for the implementation of artificial intelligence.

Arno Martin Fast, B.Eng.,

Product Manager Digital Services

Cloud-native vs. cloud-agnostic: What's the mystery behind the hype?

Speed seems to be the order of the day in digitization. Service providers and customers seem to compete with each other in this digital space. We see huge, successful solution providers switching from rigid monolithic structures to loosely coupled, service-oriented architectures. Cloud-based solutions along with API-based communications and container-centric applications seem to have helped with this transition. For some time now, cloud vendors, especially the best-known ones like Amazon, Microsoft Azure and Google Cloud, have been driving business processes worldwide. As the technology matures, the company's business processes are now asking which cloud platform is more efficient, what could be easily maintained, which option is potentially more advantageous in the long run. At this point, the question arises: cloud native or cloud agnostic? This is where companies decide whether to stick to one vendor or be present in all clouds.

Arno Martin Fast, B.Eng.,

Product Manager Digital Services

The trend towards the digitization of industrial applications involves risks as well as opportunities. For example, large computing capacities and considerable storage capacities are required. Cloud computing enables companies to process and evaluate these growing volumes of data in order to optimize the entire value chain on the basis of the results. But what service models are there and when does it make sense to deal with cloud computing? What does it mean to be "cloud agnostic" and what can multi-cloud do?