A motor drive system includes a motor drive processor, a motor drive memory component, a server, and a display that displays a machine readable code, wherein the motor drive forms a secure wireless connection with a smart device once the smart device has scanned the machine readable code.

A machine or autonomous system includes a diagnostic controller and is configured to perform a method for monitoring health of a machine. The method includes monitoring a set of first electrical signals indicative of control of a motor of the machine while the machine is operated in a first autonomous mode, determining that an abnormal operating condition has occurred in response to the set of first electrical signals meeting a first criteria, having the machine operate in a test mode in response to the determination that the abnormal operating condition has occurred, monitoring a set of second electrical signals indicative of control of the motor during the test mode, and determining a fault condition in response to the set of second electrical signals meeting a second criteria.

A method of configuring an automation control system using point configuration software. At least an extract from a control program file having program elements and program element attributes for executing in an automatic controller of the automation control system is translated into a common representation including common program element types and common attributes. Mapping template files are applied including mapping rules and mapping parameters defining how the program elements and program element attributes are mapped into at least one automation system point with point attributes, and values for each point attribute. Common program element types and/or common attributes are matched with the mapping template files according to the mapping rules to generate at least one matching program element. The matching program element is converted into an automation system point configuration (system point configuration) which is stored in persistent storage of or accessible by the automation control system.

A system for visual state identification including: a visual display system including a plurality of light displays, each of the light displays provided to different physical locations on an automation system; an execution device operatively connected to the visual display system, including: an input device configured to receive data from the automation system; and computer readable instructions which, if executed by a processor, cause the processor to: receive input data; determine the state of the automation system based on the input data, wherein the state may include an area of interest related to one of the different physical locations; communicate with the visual display system to selectively light a portion of one or more of the plurality of light displays to display the state of the automation system and the physical location of the area of interest.

Disclosed is a method for transmitting sensor measurements from a sensor device to an industrial automation device via a first wireless connection, a wired connection, and a second wireless connection. The sensor device measures operational information associated with an industrial asset, and transmits the operational information to a first gateway device via the first wireless connection. The first gateway device receives the operational information via the first wireless connection, and transmits the operational information to a second gateway device via the wired connection. The second gateway device receives the operational information via the wired connection, and transmits the operational information to the industrial automation device via the second wireless connection. The industrial automation device receives the operational information via the second wireless connection.

An intent-based automation engineering method for automation of a production process includes: receiving an intent model, correlating to process intent, including production process functions, constraints on measurable properties on the production process functions, and/or production process function sequences required for the production process, as a received intent model; receiving a process model, correlating to process knowledge including a production process behavior, as a received process model; determining a machine-readable production model linking the received intent model to the received process model as a provided production model; and determining a control strategy for controlling the production process dependent on the provided production model as a determined control strategy.

A system including a deep learning processor receives one or more control signals from one or more of a factory's process, equipment and control (P/E/C) systems during a manufacturing process. The processor generates expected response data and expected behavioral pattern data for the control signals. The processor receives production response data from the one or more of the factory's P/E/C systems and generates production behavioral pattern data for the production response data. The process compares at least one of: the production response data to the expected response data, and the production behavioral pattern data to the expected behavioral pattern data to detect anomalous activity. As a result of detecting anomalous activity, the processor performs one or more operations to provide notice or cause one or more of the factory's P/E/C systems to address the anomalous activity.

A creation device for creating an instruction for controlling a second industrial machine with a first industrial machine to execute a predetermined operation includes processing circuitry that obtains templates corresponding to operations respectively, control display of a template corresponding to an operation selected from the operations in a creation screen, and create an instruction for the operation selected from the operations based on the operation with respect to the template displayed in the creation screen.

Systems and method for leveraging container orchestration systems to generate visualizations related to components or operations of an industrial automation system. In particular, a visual manager, a primary node of the container orchestration system, may receive a container image for operating an application of the industrial automation system. A visual manager may identify a container host from a cluster of nodes of the container orchestration system for executing the container in response to the container host meeting container orchestration constraints. Upon execution of the constraint, the container host may transmit configuration details for accessing a visualization associated with the container image to a thin client for display. In some embodiments, the container host, itself, may include a thin client device. Accordingly, the thin client device may execute the container and display the corresponding visualization.

In an approach to AI empowered factory automation using an industrial IoT, responsive to receiving a new production task, production requirements are input into an AI engine. Equipment and software containers are selected based on the AI model. An orchestration service is created, where the orchestration service collaborates the equipment and the software containers.