Disclosed is a field device electronics for a field device of automation engineering, comprising: first and second terminals for connecting the field device electronics to a cable for an electrical input current to the field device electronics; a series regulator to set the input current; a shunt regulator following the series regulator; a first capacitance connected in parallel with the shunt regulator for energy storage; a supply circuit connected in parallel with the shunt regulator and the first capacitance for providing an operating voltage; and connected after the supply circuit and supplied by the operating voltage, a control unit adapted to register a buffer voltage lying across the first capacitance, based on the registered buffer voltage, to make a decision concerning at least one part of the field device electronics.

An engineering system for orchestration of an industrial plant includes: a modular plant to be orchestrated including at least one processor from a topology having: a process orchestration layer, and a plurality of modules. A portion of the plurality of modules are formed as at least one combined module. Each combined module of the at least one combined module has at least two different modules of the portion of the plurality of modules. The process orchestration layer controls the plurality of modules. The control by the process orchestration layer includes in-direct control of the portion of the plurality of modules via control of the at least one combined module.

A system includes a robotic system including a robot disposable at a mobile workstation, where the robot is configured to perform an automated operation on a workpiece. The system includes one or more transfer robots configured to transfer the robotic system to or from the mobile workstation. The system includes a control system configured to command the transfer robot to perform a transfer operation of the robotic system, where the transfer operation includes at least one of disposing the robotic system at the mobile workstation or retrieving the robotic system from the mobile workstation. The control system is configured to control the mobile workstation and the robotic system based on image data from the one or more infrastructure sensors, position data from the one or more on-board position sensors, the automated operation to be performed by the robot, or a combination thereof.

According to various embodiments, a compactor arrangement (202) may have the following: a function module arrangement (1002), which is installed on a support framework (204a) above an empty-container transport device (202t), wherein the function module arrangement (1002) has: a module receptacle (1004) for receiving multiple function modules (1006), the multiple function modules (1006), which are designed to match the module receptacle (1004) such that the multiple function modules (1006) can each be selectively received in multiple configurations (1100c, 1100d, 1100e, 1100f) in the module receptacle (1004), at least one processor (1008) which is configured to ascertain an actual configuration of the multiple configurations (1100c, 1100d, 1100e, 1100f) and to operate the function module arrangement (1002) and/or the compactor arrangement (202) on the basis of the ascertained actual configuration.

A manufacturing process adopting the reconfigurable robotic manufacturing cells that can work conjointly and yet have the capabilities to be reconfigured to disconnect from other cells and handle multiple tasks. The reconfigurable robotic cell is not dependent on any other robotic cells to complete work in progress.

A method for configuring an interface device connected to a control device and a field device, wherein the method includes receiving a first machine learning application having a plurality of logical components connected in a pipeline, where the first machine learning application serves to analyze a signal from the field device utilizing a first machine learning model, generating a plurality of code blocks utilizing a translator based on the plurality of logical components of the first machine learning application, connecting the plurality of code blocks in accordance with the pipeline of the first machine learning application to generate a first output from the signal from the field device, and deploying the connected code blocks on firmware of the interface device including creating a virtual port connectable to the control device, and where the virtual port serves to transmits the first output to the control device.

Systems for self-organizing data collection and storage in a refining environment are disclosed. An example system may include a swarm of mobile data collectors structured to interpret a plurality of sensor inputs from sensors in the refining environment, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of a plurality of refining system components disposed in the refining environment, and wherein the plurality of refining system components is structured to contribute, in part, to refining of a product. The self-organizing system organizes a swarm of mobile data collectors to collect data from the system components, and at least one of a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs.

Methods and systems for sensor fusion in a production line environment are disclosed. An example system for data collection in an industrial production environment may include an industrial production system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the components; a sensor communication circuit to interpret a plurality of sensor data values in response to a sensed parameter group; and a data analysis circuit to detect an operating condition of the industrial production system based at least in part on a portion of the sensor data values; and a response circuit to modify a production related operating parameter of the industrial production system in response to the detected operating condition.

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.

Various embodiments of the present technology generally relate to solutions for 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 an interface component configured to display a graphical representation of a machine learning asset in an industrial automation environment, wherein the graphical representation includes a visual indicator representative of an output from the machine learning asset. The interface component is further configured to adjust the visual indicator based on the output from the machine learning asset. In addition, a process control component is configured to control an industrial process in the industrial automation environment based at least in part on the output from the machine learning asset.