Embodiments of a software defined automation system that provides a reference architecture for designing, managing and maintaining a highly available, scalable and flexible automation system. In some embodiments, an SDA system can include a localized subsystem including a system controller node and multiple compute nodes. The multiple compute nodes can be communicatively coupled to the system controller node via a first communication network. The system controller node can manage the multiple compute nodes and virtualization of a control system on a compute node via the first communication network. The virtualized control system includes virtualized control system elements connected to a virtual network that is connected to a second communication network to enable the virtualized control system elements to control a physical control system element via the second communication network connected to the virtual network.

Embodiments of a software defined automation system that provides a reference architecture for designing, managing and maintaining a highly available, scalable and flexible automation system. In some embodiments, an SDA system can include a localized subsystem including a system controller node and multiple compute nodes. The multiple compute nodes can be communicatively coupled to the system controller node via a first communication network. The system controller node can manage the multiple compute nodes and virtualization of a control system on a compute node via the first communication network. The virtualized control system includes virtualized control system elements connected to a virtual network that is connected to a second communication network to enable the virtualized control system elements to control a physical control system element via the second communication network connected to the virtual network.

A method according to the disclosure configures a processor to predict metal loss in a structure for remediation. The method uses a machine learning model, trained based upon historical data, to predict metal loss over locations of a structure at a time of the prediction. The method identifies from among the predicted locations a high-risk location on the structure in which a magnitude of metal loss indicates potential remediation being needed, dispatches a robotic vehicle to the high-risk location on the structure and inspects the high-risk location using the robotic vehicle to confirm whether the magnitude of metal loss at the location requires remediation. In further methods, remediation is performed. In still further methods, a three-dimensional visualization of the structure is generated with an overlay which depicts predicted metal loss over the sections of the structure.

A method for modifying a program of an industrial automation component, wherein the program includes a plurality of program elements, where the program is structured in units, a unit includes a group of program elements and can include both published program elements having an interface that can be referenced by the program elements of other units and unpublished program elements, and where a checksum is calculated and is assigned to the unit using the interfaces, such that if at least one program element of the version of the unit stored in the engineering system is modified, then the modified checksum is compared with the previous checksum version present in the automation component, and only the modified unit is transferred if the checksums match such that consistency of the program is maintained and the software updateable more quickly.

A system for controlling an operation of a machine including a plurality of actuators assisting one or multiple tools to perform one or multiple tasks, in response to receiving an acoustic mixture of signals generated by the tool performing a task and by the plurality of actuators actuating the tool, submit the acoustic mixture of signals into a neural network trained to separate from the acoustic mixture a signal generated by the tool performing the task from signals generated by the actuators actuating the tool to extract the signal generated by the tool performing the task from the acoustic mixture of signals, analyze the extracted signal to produce a state of performance of the task, and execute a control action selected according to the state of performance of the task.

Embodiments disclosed herein comprise a sensor. In an embodiment, the sensor comprises a substrate having a first surface and a second surface opposite from the first surface. In an embodiment, the sensor further comprises a first electrode over the first surface of the substrate, and a second electrode over the first surface of the substrate and adjacent to the first electrode. In an embodiment, the sensor further comprises a barrier layer over the first electrode and the second electrode.

A cloud computing system, which works in cooperation with a plurality of relay devices, is configured to receive measurement data transmitted from each of the relay devices arranged in respective bases and perform accumulation processing in a hierarchical structure of a logical tree form in a measurement database, and perform aggregation analysis processing on the measurement data subjected to the accumulation processing in the respective bases and for each integration target between the bases. The aggregation analysis processing is performed on the measurement data for the each integration target between the bases by recognizing a relationship between the bases under the same starting point on the basis of a measurement unit of a measurement value or a type of a measurement source as for the value in the source accumulated in the database as an ending point of the hierarchical structure.

Provided is a factor analysis device capable of obtaining more useful knowledge relating to the degree of influence of pieces of data. A factor analysis device according to one embodiment of the present invention is provided with: a classification unit for classifying a type of data into a first group or a second group; and an influence degree calculation unit for calculating, as the degree of influence on target data, the degree of influence of the data of the type classified into the second group on the data of the first group type.

The plant control method includes the following. Calculating a first reference amount to be supplied for an amount of hydrogen to be supplied to a second production device (40). Making a decision on whether or not the amount of remaining hydrogen in a storage device (20) at the beginning of a subject term falls within a reference range.

A method and device for treating containers with at least two container treatment machines, between which a transport device for transporting the containers is arranged, where the transport device is divided into several transport sections, where the flow of containers is changed from containers of type A to containers of type B, where a separating device is activated once the first transport section has run empty of containers of type A, where containers of the type B are transported into the first transport section and backed up at the separating device, while containers of type A are transported from the second transport section to the second container treatment machine, and where the separating device is deactivated once the second transport section has run empty of containers of the type A, so that the containers of type B are transported through the second transport section to the second container treatment machine.