Abstract: A system of communicating data over a high availability industrial control system is disclosed. The industrial control system includes a first data producer, a second data producer in communicative connection with the first data producer, a first data consumer, and a second data consumer in communicative connection with the first data consumer. The system further includes the first producer communicating the data over multiple connection paths from the first producer to the first consumer and the second consumer through intermediate modules, and the second producer communicating the data over multiple connection paths from the second producer to the first consumer and the second consumer through intermediate modules. Also disclosed is a method of communicating data over the high availability industrial control system.

Abstract: The present disclosure is directed to systems, methods and devices for facilitating object-based cross-domain industrial automation control. An object library comprising a plurality of objects may be maintained. One or more of the objects may represent physical counterparts for use in an industrial automation process. Each object of the plurality of objects in the object library may have at least one property that an automated control device operation can be programmed to act on. Each object of the plurality of objects may also have at least one property that a human machine interface component can utilize in generating display elements corresponding to the objects for display on the human machine interface. When modifications to objects in the object library are received, those modifications may be automatically deployed and incorporated in controller logic and HMI graphics and control.

Abstract: Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing programs. In an embodiment, a system comprises: a control component configured to run a closed-loop industrial process comprises a first machine learning model; a measurement component configured to measure a gap between outcome data predicted by the first machine learning model and actual outcome data; a determination component configured to determine, based on the gap, that the first machine learning model has degraded; and a management component configured to replace the first machine learning model with a second machine learning model, wherein the second machine learning model is trained based at least in part on the actual outcome data.

Abstract: A system may include a relay device. The relay device may include an armature that moves between a first position that electrically couples a first contact to a second contact and a second position that electrically uncouples the first contact from the second contact. The relay device may also include a relay coil that receives a voltage configured to magnetize a relay coil, thereby causing the armature to move from the first position to the second position. The system also includes a control system that receives an indication that the armature is in the second position and sends a signal to an actuator in response to receiving the indication. The signal causes an arm associated with the actuator to move the armature to achieve a gap distance between the first contact and the second contact.

Abstract: Various embodiments of the present technology generally relate to integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises a screen capture component configured to capture images of a human-machine interface in an industrial automation environment, wherein the one or more images include at least one visual depiction of data collected from an industrial device. The system further comprises an input component configured to provide the images to a machine learning model configured to analyze an operating condition of the industrial device.

Abstract: The invention provides in some aspects a transport system comprising a guideway having a plurality of regions in which one or more vehicles are propelled, where each such vehicle includes a magnet. Disposed along each region are a plurality of propulsion coils, each comprising one or more turns that are disposed about a common axis, such that the respective common axes of the plurality of coils in that region are (i) substantially aligned with one another, and (ii) orthogonal to a direction in which the vehicles are to be propelled in that region. The plurality of coils of at least one such region are disposed on opposing sides of the magnets of vehicles being propelled along that region so as to exert a propulsive force of substance on those magnets.

Abstract: A computer-readable medium may include instructions that may cause a processor to perform operations that may include receiving audio data representative of sound waves generated by industrial devices and extracting features from the audio data. The features may be representative of a portion of the audio data. The operations may also include identifying a subset of the features based on distances between each of the plurality of features in an information space. The information space may include known clusters. The operations may then include determining that the subset of the features corresponds to an unknown cluster in the information space, performing a constrained classification operation based on each feature of the subset of the features to identify a new known cluster for the information space, and modifying operations of the industrial devices based on the new known cluster.

Abstract: A system for selecting one of multiple paths in a linear drive system includes track segments having a first drive member and at least one mover having a second drive member for the linear drive system. Each mover includes a drive surface oriented toward the track segment when the at least one mover is driven along the track. Each mover also includes a first channel and a second channel extending along the drive surface. A switch track segment includes a first path and a second path along which the at least one mover may travel, at least one first pin positioned along the first path, and at least one second pin positioned along the second path. The first and second pins are extendable to engage the first and second channels of the at least one mover to direct the mover along either the first path or the second path, respectively.

Abstract: A system may include drives that are communicatively coupled to and/or control I/O devices via communication option cards. The drives and/or the communication option cards may include a logic engine to control internal I/O devices and external I/O devices via single pair Ethernet (SPE) conductors accessible via an Ethernet network.

Abstract: Embodiments of this present disclosure may include systems that perform operations including receiving a first symbol instance from an industrial automation device. The first template object instance may characterize a dataset accessible to the industrial automation device. Symbol object instances may inherit a categorization with respect to categories associated with the industrial automation device including an identity, a state, a runtime status, a maintenance status, sustainability information, or any combination thereof. The operations may include identifying a type of the industrial automation device based on the first symbol instance and generating template data based on the type of the industrial automation device and the first symbol instance. The template data may include values associated with the dataset and the categories associated with the first symbol instance. The operations may also include sending a control signal to the industrial automation device based on the template data.

Abstract: Systems and methods described herein may relate to a system that includes one or more industrial devices that perform one or more operations within an industrial automation system. One or more industrial devices may include a compute surface able to perform one or more software tasks. The system may include a processor that determines a trigger event has occurred. The processor may determine additional data and a target device based on the trigger event, where the processor may be located on a different hierarchical level as compared to the target device. The processor may determine a container to be deployed to the target device based on the container generating the additional data when deployed on the target device. The processor may deploy the container to the target device.

Abstract: A method may include receiving, via a secure deployment management (SDM) system, data associated with one or more operations of an industrial device from a secure deployment management (SDM) node associated with the industrial device. The data is received via a secure communication channel established by the SDM system with the SDM node and security protocols. The method also includes sending the data to a computerized maintenance management system (CMMS) container component may perform tasks in conjunction with a computerized maintenance management system (CMMS) process, such that the CMMS container component may communicate with the CMMS process via a first firewall through which the SDM system is incapable of communicating. The SDM system may enable the data associated with the operations to communicate with the SDM node through a second firewall between the SDM system and the SDM node, the second firewall being different from the first firewall.

Abstract: Systems and methods for detecting anomalies in network communication in an industrial automation system. An anomaly detection system, a decentralized system, may identify IoT devices within the network communication and corresponding communication metrics. Using the communication metrics between the identified IoT devices, the anomaly detection system may generate a social network model that is indicative of expected network communication properties. By analyzing social network metrics and overall entropy of the network communication in real time, the anomaly detection system may identify anomalies that may be associated with potential network vulnerabilities.

Abstract: A (GUI) for designing an industrial automation system includes a design window and a first accessory window. The GUI presents a library visualization representative of a plurality of objects within the first accessory window, each object is represented by an icon and corresponds to a respective industrial automation device. The GUI receives inputs indicative of a selection of one or more objects of the plurality of objects from the library, presents the one or more objects in the design window, determines that the one or more inputs do not comply with a set of industrial automation system rules comprising one or more relationships between a plurality of industrial automation devices, and displays a warning message that the one or more inputs do not comply with the set of industrial automation system rules.

Abstract: A system includes a circuit breaker and a delay circuit. The circuit breaker has a coil to enable control of a contact to selectively couple a load to a power source responsive to a coil voltage signal applied to the coil. The delay circuit has a depletion mode MOSFET and an energy storage circuit. The depletion mode MOSFET is controlled as an active current sink to provide the coil voltage signal based on a DC voltage signal. The energy storage circuit has an input coupled to a drain of the depletion mode MOSFET, a capacitor, and an output coupled to the drain of the depletion mode MOSFET, the energy storage circuit charges the capacitor from the DC voltage signal and delivers current to the depletion mode MOSFET to delay discontinuation of the coil voltage signal responsive to a drop or discontinuation of the DC voltage signal.

Abstract: A control system may include a processor that may receive a first dataset associated with a current received at a load device coupled to a relay device. The processor may also determine harmonics data associated with the current and determine a switching profile to control moving a first armature of three armatures in the relay device based on the harmonics data. The switching profile is configured to control movement of the first armature between a first position and a second position, and wherein the switching profile comprises a firing angle for moving the first armature with respect to an electrical waveform, a second armature, and a third armature. The processor may then control a current provided to a relay coil of the relay device based on the switching profile, such that the relay coil causes the first armature to move.

Abstract: Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments of the present technology include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based recommendation engine configured to, an industrial programming environment, generate a recommendation to add a component to control logic based on an existing portion of the control logic. A notification component is configured to surface the recommendation in the programming environment. A programming component is configured to, in the programming environment, add the component to the control logic.

Abstract: A method may include receiving, via a secure deployment management (SDM) system, data associated with operations of an industrial device from a SDM node associated with the industrial device. The data is received via a secure communication channel established by the SDM system with the SDM node and security protocols. The SDM node is communicatively coupled with a machine learning system for sending and receiving data. The machine learning system may generate an updated machine learning model based on the data and a machine learning model representative of expected outputs associated with the operations of the industrial device and generate updated configuration data based on the updated machine learning model. The method may then include receiving the updated configuration data from the SDM node via the secure communication channel and sending the updated configuration data to the industrial device without performing security operations on the updated configuration data.

Abstract: A non-transitory computer-readable medium stores instructions that, when executed by a processor, cause the processor to receive, via the processor, a characteristic of data to be collected from an operational technology (OT) device disposed within an OT network associated with an industrial automation system configured to perform an industrial automation process, determine, via the processor, that the characteristic exceeds a threshold value, and deploy, via the processor, in response to determining that the characteristic exceeds the threshold value, a container to a compute surface within the OT network that is disposed within a threshold distance of the OT device. The container is configured to receive the data from the OT device and process the received data.

Abstract: A method may include receiving, via a secure deployment management (SDM) system, a notification indicative of a change in configuration data associated with an industrial device from a secure deployment management (SDM) node associated with the industrial device. The notification is received via a secure communication channel established by the SDM system with the SDM node and one or more security protocols. The method also includes retrieving, via the SDM system, the configuration data associated with the industrial device from a data source in response to receiving the notification and sending, via the SDM system, the configuration data to the SDM node via the secure communication channel. The industrial device may receive the configuration data from the SDM node without performing one or more security operations on the configuration data.