Abstract: Systems and methods for establishing a secure session between an industrial device and an end-point device are provided herein. In an example, the method includes establishing, by a client device, a first channel, such as a short-range communication protocol, with an industrial device and extracting, by a software application executing on the client device, configuration information for the industrial device via the first channel. The method also includes establishing, by the client device using the software application, a second channel with an end-point device, generating, by the software application, modified configuration information based on the configuration information extracted from the industrial device, and transmitting, by the client device, the modified configuration information to the end-point device via the second channel.

Abstract: Systems and methods for modifying a configuration of an industrial device using near-field communication (NFC) are provided herein. For example, the method includes receiving, via a graphical user interface (GUI) of a software application, a configuration schema for a group of industrial devices. The configuration schema includes a parameter and a range of available values for the parameter for each industrial device of industrial devices. The method also includes determining, by the software application installed on a client device, a configuration for a respective industrial device within the group of industrial devices based on the configuration schema, where the configuration includes a value selected from the range of available values for the parameter and the value is selected based on an order of transmission of the configuration to the respective industrial device. The method also includes transmitting, via NFC from the client device to the respective industrial device, the configuration.

Abstract: The present technology relates to health metrics corresponding to industrial automation devices and a user experience for viewing and configuring health metrics. Health metrics of a device can be produced by obtaining performance metrics of the device and contextualizing the performance metrics based on contextualization information. The health metrics can be categorized based on applying rule sets to the health metrics. The rule sets can be selectively applied to the health metrics based on a type of a respective health metric. The health metrics and health metric categories can be instantiated in a user interface of a user device based on a request for device health information from the user device.

Abstract: The present technology relates to health metrics corresponding to industrial automation devices and a user experience for configuring calculation devices to produce the health metrics. Health metrics of a device can be produced by obtaining performance metrics of the device and contextualizing the performance metrics based on contextualization information. The health metrics can be categorized based on applying rule sets to the health metrics. Contextualization and application of rule sets can be selectively performed by one or more calculation devices, such as one or more servers, industrial devices, user devices, or the like based on configuration settings. The calculation devices can obtain the performance metrics, operations, and rule sets to produce the health metrics for instantiation on a user interface.

Abstract: The present technology relates to health metrics corresponding to industrial automation devices and a user experience for connecting to devices in an industrial automation environment to configure and view health metrics. Health metrics of a device can be obtained from a server, an industrial device, a controller coupled to the industrial device, or another source, and provided to a user interface device. The user interface device can display indications of the health metrics on a user interface. The user interface device can also establish a connection with a user device and provide the indications of the health metrics to the user device for display on a user interface of the user device.

Abstract: A method for creating a predictive model is disclosed herein, including the steps of determining trends and patterns in electronic data, using at least a first machine language algorithm, refining the determination of the algorithm, searching for social models that describe the identified trends and patterns using at least a second machine language algorithm, verifying causal links, constructing at least one model about human node behavior and interactions, utilizing the social models to do at least one of the following: validate hypotheses, predict future behavior, and examine hypothetical scenarios, automatically updating predictions when new data is introduced, using probabilistic techniques to learn hierarchical structure in unstructured text, continuously updating a set of themes, examining grammatical rules of each component of text, matching grammatical constituents to semantic roles, and reorganizing data into clusters of entities with common attributes.

Abstract: A method for creating a predictive model is disclosed herein, including the steps of determining trends and patterns in electronic data, using at least a first machine language algorithm, refining the determination of the algorithm, searching for social models that describe the identified trends and patterns using at least a second machine language algorithm, verifying causal links, constructing at least one model about human node behavior and interactions, utilizing the social models to do at least one of the following: validate hypotheses, predict future behavior, and examine hypothetical scenarios, and automatically updating predictions when new data is introduced.