Many machine builders and manufacturing companies have not yet been able to use the available machine learning tools independently, as their operation has been optimised for the data-driven activities of analytics experts. Companies can either train their existing employees for a huge amount of money, or hire a data scientist themselves. This results in an inhibition threshold that is currently slowing down the spread of artificial intelligence in industry.
An alternative is to develop user-friendly software solutions that even users without any statistical training are able to understand and generate analytics models. Weidmüller‘s Industrial Analytics business unit has put this idea into practice with its automated machine learning software. The name of the application itself implies that the models are largely developed automatically.
“Similar applications are currently being used in the areas of fintech, banking and marketing. However, the existing solutions are not suitable for machine and plant engineering, because they do not support the relevant data types from the automation industry. They always require an ideal database,” explains Dr Carlos Paiz Gatica, Product Manager at the BU Industrial Analytics. “In addition, they don‘t provide the ability to integrate the user‘s domain knowledge, which is essential for industrial applications.”
For the automated machine learning software, Weidmüller’s analytics experts combine the domain expert’s data and information with algorithms to automatically generate suitable models. The following working steps describe the model generation process using anomaly detection as example: