Abstract: A system and a method provide a global view of an automation system for any industrial controller in a network. The method comprises providing a distributed version control runtime system for managing industrial controller process images in that an automation engineering process provides non-linear workflows. The method further comprises providing an engineering system having a first industrial controller program database of an industrial controller program. The method further comprises providing a first industrial controller having a first process image including a second industrial controller program database of an industrial controller program and a first historian database. The method further comprises providing a second industrial controller having a second process image including a third industrial controller program database of an industrial controller program and a second historian database.

Abstract: Methods and systems for dynamic network link prediction include generating a dynamic graph embedding model for capturing temporal patterns of dynamic graphs, each of the graphs being an evolved representation of the dynamic network over time. The dynamic graph embedding model is configured as a neural network including nonlinear layers that learn structural patterns in the dynamic network. A dynamic graph embedding learning by the embedding model is achieved by optimizing a loss function that includes a weighting matrix for weighting reconstruction of observed edges higher than unobserved links. Graph edges representing network links at a future time step are predicted based on parameters of the neural network tuned by optimizing the loss function.

Abstract: A method for representing knowledge in a cognitive engineering system (CES) includes receiving information relating to an automation engineering project from an engineering tool, storing the received information in a cognitive engineering graph (CEG) storing a plurality of previously generated CEGs for previous automation engineering projects, and establishing a communication path between the CEG storing the received information and the plurality of previously generated CEGs. The method may further include applying machine learning to the stored CEG based on the received information and the stored plurality of previously generated CEGs. The machine learning may analyze the CEG to identify at least one pattern that is representative of a given object from the automation engineering project. The CES may automatically add an element to the CEG based on the received information and a query from a user. Further, the user may request a change made by the CES be reversed.

Abstract: Computerized engineering tool and methodology to develop neural skills for computerized autonomous systems, such as a robotics system (50), are provided. A disclosed computerized engineering tool (10) may involve an integrated arrangement of respective modular functionalities arranged in a closed loop, such as may include a physics engine (14), a neural data editor (16), an experiment editor (18), a neural skills editor (20), and a machine learning environment (22). Disclosed embodiments are conducive to cost-effectively simplifying development efforts involving neural skills, such as by reducing the time involved to develop the neural skills involved in any given robotics system and by reducing the level of expertise involved to develop neural skills.

Abstract: A system and method for modeling human behavior includes receiving, by a classifier module, sensor data from one or more sensors monitoring human behavior associated with a work task and to identify the type of human behavior based on a trained neural network. A prediction module receives the identified type of human behavior from the classifier and generates prediction data representing predicted next one or more human actions based on a time series of position vectors learned by the trained neural network. A rendering module translates the prediction data into a visual rendering for a virtual human simulation model.

Abstract: A system and a method provide an Artificial Intelligence (AI) companion for each Function Block in a Programmable Logic Controller (PLC) program to integrate AI in automation systems. Multiple function blocks and system function blocks are grouped into a logic group. A control problem is broken down from a top level into logical partitions as several functions that are programmed as Function Blocks in a PLC program. Each Function Block and the entire PLC program are integrated with an associated AI Companion. A runtime system for the AI Companion provides new runtime capabilities. An approach to implementing the AT Companions is provided. A method of controlling an automation process is also provided.

Abstract: A system for checking security vulnerabilities for automation system design includes a security database, an Internet crawler application, and security service application. The security database stores descriptions of known software vulnerabilities related to an automation system. The Internet crawler application is configured to systematically browse the Internet to find new software vulnerabilities related to the automation system and index the new software vulnerability into the security database. The security service application retrieves, from the security database, potential software vulnerabilities related to a hardware/software configuration of the automation system. The security service application also identifies policies related to the potential vulnerabilities. Each policy describes a potential vulnerability and action to be performed in response to detection of the potential vulnerabilities.

Abstract: A system for exchanging data in an automation environment is provided. The system includes at least one programmable logic controller (PLC A) containing program instructions executable by the at least one programmable logic controller and a queue block (50) configured to dynamically exchange data between the program instructions of the PLC and a data consumer (PLC C, PLC D).

Abstract: Applications of artificial intelligence (AI) in industrial automation have focused mainly on the runtime phase due to the availability of large volumes of data from sensors. Methods, systems, and apparatus that can use machine learning or artificial intelligence (AI) to complete automation engineering tasks are described herein.

Abstract: A system for autonomous generative design in a system having a digital twin graph a requirements distillation tool for receiving requirements documents of a system in human-readable format and importing useful information contained in the requirements documents into the digital twin graph, and a synthesis and analysis tool in communication with the digital twin graph, wherein the synthesis and analysis tool generates a set of design alternatives based on the captured interactions of the user with the design tool and the imported useful information from the requirements documents. The system may include includes a design tool with an observer for capturing interactions of a user with the design tool, In addition to the observer, an insighter in communication with the design tool and with the digital twin graph receives design alternatives from the digital twin graph and present the receive design alternatives to a user via design tool.

Abstract: A system and method to apply deep learning techniques to an automation engineering environment are provided. Big code files and automation coding files are retrieved by the system from public repositories and private sources, respectively. The big code files include examples general software structure examples to be utilized by the method and system to train advanced automation engineering software. The system represents the coding files in a common space as embedded graphs which a neural network of the system uses to learn patterns. Based on the learning, the system can predict patterns in the automation coding files. From the predicted patterns executable automation code may be created to augment the existing automation coding files.

Abstract: A computer-implemented method for quantifying assurance of a software system includes collecting artifacts of the software system generated during phases of the software system's engineering lifecycle. A graph of graphs (GoG) is constructed encoding the artifacts. Each subgraph in the GoG is a semantic network corresponding to a distinct assurance requirement. The GoG is used to calculate a component assurance value for each software component for each distinct assurance requirement. A system assurance value is calculated based on the component assurance values. An architectural view of the software system is presented showing at least one of the component assurance values and the system assurance values.

Abstract: A computer-implemented method for detecting cyber-attacks affecting a computing device includes retrieving a plurality of sensor datasets from a plurality of sensors, each sensor dataset corresponding to involuntary emissions from the computing device in a particular modality and extracting a plurality of features from the plurality of sensor datasets. One or more statistical models are applied to the plurality of features to identify one or more events related to the computing device. Additionally, a domain-specific ontology is applied to designate each of the one or more events as benign, failure, or a cyber-attack.

Abstract: A system and method is provided that facilitates threat impact characterization. The system may include a replica programmable logic controller (PLC) that corresponds to a production PLC in a production system and that may be configured to operate at an accelerated processing speed that is at least two times faster than a processing speed of the production PLC. The system may also include a data processing system configured to communicate with the replica PLC when executing malware infected PLC firmware and generate a simulation of the production system based on a virtual model of the production system operating at an accelerated processing speed that is at least two times faster than a processing speed of the physical production system. The simulation may include accelerated simulation of the production PLC based on communication with the replica PLC using the malware infected PLC firmware.

Abstract: Graph databases directly relate data items in the data store with edges that represent relationships between the data items. The relationships link the data items together and often permit complex sets of related data items to be retrieved with a single operation. New query systems and techniques for graph databases provide prediction of non-explicit connections between data items that further enhances the efficiency and utility of graph databases, as well as extend their industrial applications.

Abstract: A method of performing failover for programmable logic controllers (PLCs) in an automation environment and controlling a physical system includes an input/output module receiving sensor inputs from field devices and creating a copy of the sensor inputs for a first group of PLC in a first PLC bank. The input/output module transfers the copy the sensor inputs to each PLC in the first group of PLCs and receives processing results from each PLC in the first group of PLCs in response to transferring the copy of the sensor inputs. The input/output module determines whether there are any inconsistencies between the processing results received from each PLC in the first group of PLCs. If there are any inconsistencies between the processing results received from each PLC in the first group of PLCs, a failover control process is initiated by sending a failover control message to a second input/output module.

Abstract: A method for executing a machine learning model with a controller includes a processor within the controller writing input values to a process image within the controller. The term process image refers to a predefined address space within volatile memory of the controller. A co-processor connected to the controller reads the input values from the process image and applies a machine learning model to the input values to generate output values. The co-processor writes output values to the process image and the processor reads those output values from the process image. The process can then execute an application program that utilizes the one or more output values.

Abstract: A system for autonomous generative design in a system having a digital twin graph a requirements distillation tool for receiving requirements documents of a system in human-readable format and importing useful information contained in the requirements documents into the digital twin graph, and a synthesis and analysis tool in communication with the digital twin graph, wherein the synthesis and analysis tool generates a set of design alternatives based on the captured interactions of the user with the design tool and the imported useful information from the requirements documents. The system may include includes a design tool with an observer for capturing interactions of a user with the design tool, In addition to the observer, an insighter in communication with the design tool and with the digital twin graph receives design alternatives from the digital twin graph and present the receive design alternatives to a user via design tool.

Abstract: A model-based human machine interface (HMI) system is provided. The HMI system includes a plurality of output devices each having output capabilities that include at least one unique output capability level and a semantics library that is configured to receive SUC component models each having an output format and data. The semantics library is configured to analyze the data relative to the SUC component models and to transmit each of the SUC component models to one or more of the plurality of output devices. The one or more of the plurality of output devices are selected based on a correlation between the output capabilities of the plurality of output devices and the output format of the SUC component models.

Abstract: Systems (500) and methods (400) for an interactive system for automatic generation, analysis and exploration of composable system of systems based on knowledge graphs. A method (400) includes receiving (405) a scenario (110) and a domain ontology (111); determining (410) structures (132), attributes (133), and capabilities (131) from the domain ontology; generating (415) design alternatives (146) based on the scenario using the structures, attributes, and capabilities; performing (430) an evaluation (159) of the design alternatives based on the scenario; generating (445) an SoS design (300) based on the evaluation; and displaying the SoS design to a user.