Abstract: A method includes obtaining an engineering schematic containing multiple symbols and connections involving the symbols, where different ones of the symbols in the engineering schematic represent different types of equipment. The method also includes identifying visual features of the engineering schematic. The method further includes processing the visual features using at least one trained machine learning model to (i) identify boundaries around the symbols in the engineering schematic and (ii) classify the symbols in the engineering schematic into multiple classifications, where different ones of the classifications are associated with different types of symbols.

Abstract: 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.

Abstract: A method includes obtaining an engineering schematic containing multiple symbols and connections involving the symbols, where different ones of the symbols in the engineering schematic represent different types of equipment. The method also includes identifying visual features of the engineering schematic. The method further includes processing the visual features using at least one trained machine learning model to (i) identify boundaries around the symbols in the engineering schematic and (ii) classify the symbols in the engineering schematic into multiple classifications, where different ones of the classifications are associated with different types of symbols.

Abstract: 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.

Abstract: A method includes analyzing information to be processed, where analyzing the information includes classifying invalid data contained in the information and substituting replacement data in place of at least some of the invalid data in the information. The method also includes training at least one machine learning model based on some of the analyzed information. The method further includes providing other of the analyzed information to the at least one trained machine learning model, where the at least one trained machine learning model is used to generate one or more recommendations based on the analyzed information. In addition, the method includes translating each of the one or more recommendations into one or more actions and generating one or more control instructions based on the one or more actions for at least one of the one or more recommendations.

Abstract: A method includes obtaining an engineering schematic containing multiple symbols and connections involving the symbols, where different ones of the symbols in the engineering schematic represent different types of equipment. The method also includes identifying visual features of the engineering schematic. The method further includes processing the visual features using at least one trained machine learning model to (i) identify boundaries around the symbols in the engineering schematic and (ii) classify the symbols in the engineering schematic into multiple classifications, where different ones of the classifications are associated with different types of symbols.

Abstract: A method includes obtaining training data having at least one training engineering schematic containing multiple known symbols, where different ones of the known symbols represent different types of equipment. The method also includes augmenting the training data with at least one additional training engineering schematic having at least one synthetic schematic, where the at least one synthetic schematic contains additional known symbols. The method further includes training at least one machine learning model using the training data including the at least one training engineering schematic and the at least one additional training engineering schematic.

Abstract: 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.