Systems and methods for automated parsing of schematics
The present disclosure provides systems, methods, and computer program products for generating a digital representation of a system from engineering documents of the system comprising one or more schematics and a components table. An example method can comprise (a) classifying, using a deep learning algorithm, (i) each of a plurality of symbols in the one or more schematics as a component and (ii) each group of related symbols as an assembly, (b) determining connections between the components and the assemblies, (c) associating a subset of the components and the assemblies with entries in the components table; and (d) generating the digital representation of the system from the components, the assemblies, the connections, and the associations. The digital representation of the system can comprise at least a digital model of the system and a machine-readable bill of materials.
1. A method for generating a digital representation of a system from engineering documents of the system comprising one or more schematics and a components table, the method comprising:
(a) classifying, using a deep learning algorithm, (i) each of a plurality of symbols in the one or more schematics as a component and (ii) each group of related symbols as an assembly, wherein classifying each group of related symbols as an assembly comprises, for each group, detecting dashed lines in the one or more schematics that delineate the group;
(b) determining connections between the components and the assemblies;
(c) associating a subset of the components and the assemblies with entries in the components table; and
(d) generating the digital representation of the system from the components, the assemblies, the connections, and the associations, wherein the digital representation of the system comprises at least a digital model of the system and a machine-readable bill of materials.
2. The method of claim 1 , further comprising, prior to (a), preprocessing the one or schematics by removing white space or noise from the one or more schematics.
3. The method of claim 1 , further comprising, prior to (a), determining a library of components to detect in the one or more schematics.
4. The method of claim 3 , further comprising (i) determining that a respective symbol in the one or more schematics is not in the library of components and (ii) adding the respective symbol and its corresponding component to the library of components.
5. The method of claim 1 , wherein (a) comprises detecting the plurality of symbols in the one or more schematics using a binary classifier, wherein the deep learning algorithm comprises the binary classifier, and wherein the detecting comprises a sliding window approach.
6. The method of claim 5 , wherein (a) further comprises classifying each of the detected symbols as a component using a multi-class deep neural network, wherein the deep learning algorithm comprises the multi-class deep neural network.
7. The method of claim 5 , wherein (a) further comprises classifying each of the detected symbols as a component using an autoencoder, wherein the deep learning algorithm comprises the autoencoder.
8. The method of claim 6 , wherein the multi-class deep neural network is selected from the group consisting of: a feed-forward neural network and a convolutional neural network.
9. The method of claim 6 , wherein the multi-class deep neural network is trained using a semi-supervised learning process.
10. The method of claim 1 , wherein (a) comprises detecting and classifying the plurality of symbols in the one or more schematics using a single object detection algorithm.
11. The method of claim 1 , wherein (b) comprises detecting solid lines connecting the components and the assemblies.
12. The method of claim 11 , wherein the detecting comprises using a graph search algorithm.
13. The method of claim 1 , wherein (c) comprises:
detecting and identifying, in the one or more schematics, identification numbers associated with the components and the assemblies in the subset of components and assemblies using optical character recognition; and
matching the detected identification numbers with identification numbers in the components table.
14. The method of claim 13 , wherein detecting the identification numbers comprises using a Hough transform.
15. The method of claim 1 , wherein the digital model comprises computer code that defines the components, the connections, and a state of the system.
16. The method of claim 15 , wherein the digital model is a static model.
17. The method of claim 15 , wherein the digital model is a time-varying model.
18. One or more non-transitory computer storage media storing instructions that are operable, when executed by one or more computers, to cause the one or more computers to perform operations comprising:
(a) classifying, using a deep learning algorithm, (i) each of a plurality of symbols in one or more schematics as a component and (ii) each group of related symbols as an assembly, wherein classifying each group of related symbols as an assembly comprises, for each group, detecting dashed lines in the one or more schematics that delineate the group;
(b) determining connections between the components and the assemblies;
(c) associating a subset of the components and the assemblies with entries in a components table; and
(d) generating a digital representation of a system from the components, the assemblies, the connections, and the associations, wherein the digital representation of the system comprises at least a digital model of the system and a machine-readable bill of materials.