AI engineering
Deep learning has become an integral part of modern image processing and convolutional neural networks have established themselves as a standard method for automatically detecting objects in images with pixel accuracy. However, artificial intelligence (AI) methods are also opening up new possibilities in many other areas for uncovering and modeling complex relationships in data.
The potential of existing plant data can also be better exploited with machine learning in order to optimize processes in a data-driven manner. For example, sensor data that is continuously measured in industrial plants can be intelligently linked by AI models in order to calculate meaningful real-time parameters for the current process quality . Particularly where process control relies on sample-based laboratory evaluations of the produced product quality, continuous real-time estimation of the relevant quality characteristics offers considerable added value for process optimization and even new, innovative control options.
Our expertise covers the entire spectrum from supervised and unsupervised machine learning (ML) to deep learning (DL), so that the suitable method can be used depending on the issue and data basis. In the machine learning pipeline, we pay particular attention to the preparation of data from various heterogeneous (sensor) sources in order to make it usable - whether scalar (sensor) data, time series or image data.
Take a look at our data management services as well as examples of our AI-based approaches