The present invention facilitates the confirmation of the state of a slave device. This terminal monitoring device (10) comprises: a terminal information acquisition unit (14) for acquiring, from terminals of a network, terminal information including the addresses of the respective terminals, information for each type, and information pertaining to communication states; and a terminal list screen generation unit (151) that generates a terminal list screen on which selection boxes for displaying the terminals and the terminal information are disposed side by side on the basis of the terminal information and that displays the generated screen on a display unit (12).

A control device has first and second control units. The first unit includes a timing unit which measures elapsed time and instructs the start of a first safety signal circuit test based on the time, and a first circuit test control unit which, when a safety signal circuit output signal has been output to a first circuit, or output thereof has stopped, detects whether a safety signal circuit input signal is input, and executes the first safety signal circuit test. The second unit is provided with an implementation verifying unit which verifies whether the first safety signal circuit test is complete, and a second circuit test control unit which, after verification, when a safety signal circuit output signal is output to a second circuit, or when output thereof has stopped, detects whether a safety signal circuit input signal is input, and executes a second safety signal circuit test.

Described are platforms, systems, and methods to discover relationships among equipment in automated industrial or commercial environments by cycling each individual piece of equipment while observing sensors in all other equipment in order to measure how each part reacts to each other part. The platforms, systems, and methods identify a plurality of data sources associated with an automation environment; issue one or more commands to cycle a current data source in the a plurality of data sources; monitor the automation environment for events or state changes in the data sources; detect one or more events or one or more state changes in one or more other data sources in the a plurality of data sources; and determine one or more relationships between the current data source and the one or more other data sources.

Described are platforms, systems, and methods to discover relationships among equipment in automated industrial or commercial environments by cycling each individual piece of equipment while observing sensors in all other equipment in order to measure how each part reacts to each other part. The platforms, systems, and methods identify a plurality of data sources associated with an automation environment; issue one or more commands to cycle a current data source in the a plurality of data sources; monitor the automation environment for events or state changes in the data sources; detect one or more events or one or more state changes in one or more other data sources in the a plurality of data sources; and determine one or more relationships between the current data source and the one or more other data sources.

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.

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.

Embodiments of this present disclosure may include a system with a first power converter and a control system. The first power converter may supply a first current to a first backplane while a second power converter is concurrently supplying a second current to a second backplane. The first backplane and the second backplane may electrically couple to one or more load components and the control system. The control system may cause the first power converter to adjust the first current such that the first current and the second current are imbalanced with respect to each other in response to receiving a request to verify that the second power converter is capable of supplying the one or more load components with a target current value while the first current and the second current are imbalanced.

It is disclosed a PLC Program analysis method where a program is translated into a program model in a logical framework, from which properties are determined. Said properties coupled with interlocking properties are verified by an automated solver. If contraposition of a property is satisfiable, counter-examples representative of model's inputs and internal memory values is provided. Counter-examples are translated into error initial configurations of said model. Execution of the model is simulated with said model error initial configurations, and error intermediary configurations of said model simulation are recorded up to said property violation. Error initial and intermediary configurations of said original program are derived from error initial configurations of said model and error intermediary configurations of said model simulation and displayed. An apparatus for executing said method is provided.

Techniques described herein may involve modification of playback based on the proximity of a user to a playback device. An example technique involves a device determining that a listener is within a given proximity of a first playback device and based on determining that the listener is within the given proximity of the first playback device, causing the first playback device to begin playback of first media and causing a second playback device to modify playback of second media.

A method and apparatus for managing a field device on a network can involve detecting, at a processing device, a connection of the field device in an industrial process control and automation system, receiving data, from the field device, including payload information related to the field device in the industrial process control and automation system, determining, at the processing device, whether the field device uses a specific protocol based on the data, and responsive to determining that the field device uses the specific protocol, generating, at the processing device, a soft modem instance. The method and apparatus can also involve termination of soft modem instance if the field device becomes disconnected. The method and apparatus can also involve demodulating and extracting, using the soft modem instance, the payload information from the data.