A method for collecting error code history includes detecting a fault caused by errors in a machine, initiating a dispatch request from the machine to a service location, generating a first quick response code in response to a first input signal from a technician, where the first quick response code encodes first configuration items that describe the machine and first error codes that characterize the detected errors, presenting a first graphical image of the first quick response code on a display, performing a self-test in the machine in response to a second input signal, generating a second quick response code after the self-test has been completed, where the second quick response code encodes second configuration items that describe the machine and second error codes, and presenting a second graphical image of the second quick response code on the display.

A computer of a resource monitoring system is configured to execute an application for analyzing an operation of an industrial device. A use status acquisition module is configured to acquire a use status of a resource of the computer by the application. A display control module is configured to display the use status on a resource monitoring screen.

A display apparatus that displays log data which indicates when the log data deviates from a normal state and a deviation degree is provided. The display apparatus comprises: a memory to store prediction data including a signal value of a prediction value of the log data and accuracy degree of the prediction value, and a signal value of an actual measurement value of the log data; a control unit to generate a display screen on which a prediction value area indicating an area of the log data in normal state is depicted based on the prediction data and on which a signal wavelength of the signal value of the actual measurement value is depicted based on the signal value of the actual measurement value; and a display unit to display the display screen.

This disclosure relates generally to a method and system for real time monitoring and forecasting of fouling of an air preheater (APH) in a thermal power plant. The system is deploying a digital replica or digital twin that works in tandem with the real APH of the thermal power plant. The system receives real-time data from one or more sources and provides real-time soft sensing of intrinsic parameters as well as that of health, fouling related parameters of APH. The system is also configured to diagnose the current class of fouling regime and the reasons behind a specific class of fouling regime of the APH. The system is also configured to be used as advisory system that alerts and recommends corrective actions in terms of either APH parameters or parameters controlled through other equipment such as selective catalytic reduction or boiler or changes in operation or design.

A method may comprise receiving, at a central controller, a command, from a remote control, to adjust a feature of a first component of an air mattress framework; relaying, from the central controller, the command to the first component; receiving from the first component at the central controller, an indication of the success of the command; and relaying the indication from the central controller to the remote control.

Systems and techniques for active asset monitoring are presented. A system can collect a set of voltage measurements from one or more assets. The system can also perform learning associated with the set of voltage measurements and generate a set of digital signatures that includes a set of patterns regarding the set of voltage measurements. Furthermore, the system can determine monitor performance of an asset based on the set of digital signatures that includes the set of patterns regarding the set of voltage measurements.

This application provides methods and systems for automated condition-based maintenance of mechanical systems. Example systems may at least one memory coupled to one or more computer processors that are configured to receive first data from the mechanical system indicative of performance of a first component of the mechanical system, determine, using the first data, a first performance metric for the first component, determine, using the first performance metric, a probability value that a fault has occurred at the first component, and determine, using the probability value, a predicted length of time until failure of the first component.

Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data and line production data, determine one or more production associations between the cell data and the line production data; evaluate the one or more production associations to identify one or more critical production associations; retrieve the cell data and the line production data associated with the one or more critical production associations; and train a predictive model with the retrieved cell data and the retrieved line production data to predict the production level of the manufacturing assembly line.

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.

An industrial plant operator intervention system for use in an industrial plant includes a processing unit configured to monitor and analyze industrial plant operation data to detect an anomaly in the industrial plant operation data that warrants initiating an operator intervention, and in response to detecting the anomaly, automatically determine a user interface configuration of a user interface to be presented to a designated operator who is to perform the operator intervention. The user interface configuration is determined on the basis of technical context data, including industrial plant operation data associated with the anomaly, and on the basis of operator data pertaining to the designated operator, in such a manner that an anomaly-related and operator-specific user interface configuration is obtained.