An augmented virtual model of a commercial structure, such as a processing plant and/or a manufacturing plant which includes a virtual design, as built data and experiential measurement data. A User may view the augmented model in a virtual reality setting based with the content presented to the user based upon a location of the user at the time of viewing. Aspects of the Augmented Virtual Model are updated over time and utilized to track performance.

A method, a component, and an electronic device relating to the field of industrial automation technology, particularly relating to the creation of a human machine interface, and are used for automatically creating an HMI of a system. The method for creating an HMI comprises: a first component of a system receives, via an HMI, a first command for creating an HMI for the system; the first component obtains, from each searched and found second component of the system an HMI resource of each of the second components; and the first component creates, on the basis of an HMI resource of the first component and the obtained HMI resource from each of the second components, the HMI for the system. One component of the system collects the HMI resource of another component of the system and creates the HMI for the whole system, providing a method for automatically creating the HMI for the system.

A system is provided for measurement data management in a distributed environment. The system comprises at least one storage system adapted to obtain raw measurement data or intermediate results from at least one measurement site via a network. In addition, the system further comprises a database, operatively connected to the said storage system, adapted to be accessed remotely by the measurement site via the network. The storage system or the measurement site is further adapted to perform successive processing steps on the raw measurement data along a process chain in order to generate measurement results, whereby associating metadata with the raw measurement data and with the measurement results. In this context, the metadata associated with each measurement result of the successive processing steps is provided with a new reference as well as a reference to the reference of the measurement result from the preceding processing step.

A device maintenance apparatus includes a test executor configured to cause a device to output an output signal based on a test pattern that changes the output signal output from the device with an elapse of time, and a change instructor configured to issue a change instruction for changing at least one of a progress of an output of the output signal based on the test pattern and an output value of the output signal to the test executor in accordance with an instruction input while the test executor causes the device to execute the output of the output signal.

A system and method for visualizing process information in industrial process applications, wherein the process information is comprised in data objects related to process elements and data objects are represented as visualized data objects according to pre-defined visualization styles, and wherein a data object is representable by a plurality of different visualization styles, includes running a visualization application for visualizing the process information, selecting a visualization style for a data object during run time of the visualization application, and applying the visualization style according to the selection during run time of the visualization application.

A test procedure system comprising a test component test procedure corresponding to test instructions, a computer model system comprising a test component computer model comprising inspection indicia therein, a head mounted display comprising an alignment system, aligning the test component within a field of view with the computer model to align the inspection indicia and a user interface associated with the head mounted display for entering test results.

An example embodiment involves rules related to repairing software programs, wherein the rules associate indications of software program failures with repair applications that are configured to correct the software program failures. One or more processors are configured to: (i) receive, by a predictive model, a representation of an execution history of a particular software program, wherein the predictive model has been trained on a corpus of execution histories of the software programs; (ii) generate, by the predictive model and from the execution history, a failure prediction for the particular software program; (iii) receive, by an automated repair controller application, the failure prediction from the predictive model; (iv) based on applying the rules to the failure prediction, determine, by the automated repair controller application, a repair application from the repair applications; and (v) cause, by the automated repair controller application, the repair application to be executed within the network.

This disclosure relates to a method and system for monitoring health and predicting failure of an electro-mechanical machine. In an embodiment, the method may include receiving a plurality of operational parameters with respect to the electro-mechanical machine and determining a set of features and a set of events, based on the plurality of operational parameters. The method may further include detecting one or more fault signatures associated the electro-mechanical machine based on at least one of the plurality of operational parameters, the set of features, or the set of events. The method may further include determining at least one of a time to the possible failure and a remaining useful life of the electro-mechanical machine based on at least one of the plurality of operational parameters, the set of features, the set of events, or the one or more fault signature, by using a hybrid machine learning model.

Provided is a monitoring apparatus comprising: an association acquisition unit configured to acquire, for each of a plurality of pieces of equipment, an association with at least one sensor monitoring a state of the each of the plurality of pieces of equipment; a calculation unit configured to calculate, based on at least one measurement value from the at least one sensor associated with each piece of equipment of the plurality of pieces of equipment, a state value of each piece of equipment; a ranking unit configured to rank the plurality of pieces of equipment according to a change width or a change rate in the state value; and a display processing unit configured to perform a processing to display an information screen corresponding to the ranking of the plurality of pieces of equipment.

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.