Apply AI on any data without expert knowledge

  • Drag-and-Drop import of excel files
  • Browse and select data sets within your DBs
  • Organize your data sets in separated work spaces

Knowledge-based data preparation

  • Evaluate the validity either for the whole data set or for individual signals based on automatically imported min/max ranges and quality codes
  • Apply your expert knowledge and adapt min/max ranges or reaction times in order to improve the automatized data cleaning
  • Visualize dependencies between signals in order to learn more about your data sets

Guided training phase for predictive modeling via AutoML

  • Train a model without advanced knowledge or specialized skills.
  • Let AIXPERT choose the best algorithms, models or combinations thereof, depending on the nature of problems or use cases
  • Achieve reliable predictions using the latest advances in RNN, LSTM and more

Learning from failures by using pattern recognition

  • Manually mark abnormalities in sensor signals
  • Test and evaluate the pattern detection directly on historical data and find the same phenomenon in the past
  • Export the pattern as a black box and integrate it into RULESXPERT
  • Combine different patterns to detect emerging equipment failures

Export models as „black-box“ for their integration in RULESXPERT

  • Import and evaluate data-driven models in RULESXPERT
  • Integrate AIXpert models as well as advanced models as R and Phython code
  • Run your ML model directly in operation (on the edge) or securely hosted in the cloud

« Existing process models are the result of many years of experience of our process engineers. They are not to be replaced. In order to integrate new data-driven approaches, our process engineers can now independently and intuitively train their own AI models. Machine Learning is successfully accepted not as a job killer but as active support for their work. »