Interpretation of Schematics


A schematic is a graphical representation of an electrical circuit. Schematics are created, for example, for the circuits of control cabinets. Schematics show the function of the individual components and their electrical interconnections in abstract form. The arrangement or shape of the components is not shown. Clearly defined and standardized symbols for the individual components and interconnections are used to create schematics. There are different types of schematics, such as schematics in resolved representation. In practice, the analysis and interpretation of existing schematics can sometimes be a time-consuming task.


A possible solution approach is the use of machine learning, specifically the use of neural networks. First, the corresponding schematic document is read. The machine learning algorithm labels the features based on predefined classes. Then, the algorithm extracts features, such as the class name and connections between schematics, to learn the classes of the schematics. Then, the machine learning algorithm predicts the classes of the schematics. Finally, the interfaces including all information are returned to the user.


The automatic interpretation of schematic documents leads to significant time savings for users. At the same time, the machine learning algorithm is further trained and optimized with the help of manual corrections by the user. In this way, the quality of the algorithm increases over time. However, to be able to make the predictions with a high success rate right from the start, a large amount of training data is initially required.


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