Methods, devices, and systems related to process control in manufacturing are described. In an example, a method can include receiving data from a first process control device affixed to a first manufacturing tool of a first type, identifying one or more attributes of the data via a second processing resource of a second process control device affixed to a second manufacturing tool of a second type different from the first type, determining one or more settings for the second manufacturing tool via the second processing resource in response to identifying the one or more attributes of the data, and sending a command including the one or more settings to the second manufacturing tool from the second process control device.

A device for computing correction for control parameter in a manufacturing process executed on a manufacturing apparatus includes circuitry which acquires an index representing fluctuation in a manufacturing apparatus, acquires an apparatus model and a process model, acquires an output from a sensor in the manufacturing apparatus, transforms the output into first fluctuation for a process element, transforms the index into second fluctuation for the process element based on the apparatus model, computes fluctuation for performance indicator from the first and second fluctuation based on the process model, computes correction for the performance indicator from control range for the performance indicator and the fluctuation for the performance indicator, and converts the correction for the performance indicator into correction for each process element based on the process model such that correction for control parameter in process executed on the manufacturing apparatus is computed from the correction converted for each process element.

A method for controlling at least one operational parameter of a plant (1) having a combustion unit (3) can include estimating a status of at least one operational variable of the plant to identify an estimated value for the operational variable. For each operational variable, the estimated value for the operational variable can be compared with a measured value of the operational variable to determine an uncertainty value based on a difference in value between the measured value and the estimated value for the operational variable. A control signal can be generated based on a reference signal, the measured value, and the deviation value for sending to at least one element of the plant (1) for controlling a process of the plant (1).

A power device discovery and visualization system identifies and categorizes smart power and energy devices deployed within an industrial enterprise, and generates interfaces that render customized presentations of a plant's power and energy usage based on data retrieved from the discovered devices. The system also facilitates remote configuration of the discovered smart devices via interaction with a single consolidated interface that affords access to all discovered power and energy devices. The system constructs a device profile for each discovered smart device based on information retrieved from the device, and categorizes each discovered device according to device type (e.g., power meter, IED, VFD, controller, etc.). The system retrieves available historical, real-time, and/or configuration data from each discovered smart device and makes this data available for viewing or modification via the system interface, thereby unifying plantwide power and energy data into a common presentation.

A processing robot system includes a connection setting information group storage device provided inside a robot control device, a teaching operation panel, and a PC. The connection setting information group storage device stores a connection setting information group including information on settings for a digital communication standard between a processing device and the robot control device, and information on the allocation of I/O signals to be transmitted between the processing device and the robot control device via digital communication and which outputs the connection setting information group as one file. The teaching operation panel and the PC have a screen on which each piece of setting information included in the connection setting information group can be displayed and edited. Such a processing robot system can easily make settings for connection with a digital-controlled processing device.

The invention provides methods, systems and computer program products for monitoring field device states within a process control system, and for detecting and responding to state changes associated with one or more field devices. Implementation of the invention involves, for each field device within a selected sub-set of the plurality of field devices, (i) retrieving a reference set or template set of field device parameter state data associated with the field device (ii) retrieving a set of current state data associated with each field device within the selected sub-set of the plurality of field devices, (iii) comparing the two sets of parameter state data for each field device within the selected sub-set of the plurality of field devices and (iv) generating and/or displaying a report representing detected deviations between the two sets of parameter state data for each field device within the selected sub-set of the plurality of field devices.

A production data creation device includes an input unit and a setting unit. The input unit receives an input of one application from among a plurality of applications displayed on a screen. The setting unit sets one or more operation parameters used by a component mounter to mount a component on a board, on the basis of the input application.

Described are platforms, systems, and methods to combine counts of activity correlations over time with a link salience method to identify collections of digital devices in an automated environment to identify sub-systems comprised of portions of the overall environment. The platforms, systems, and methods detect activity in a plurality of data sources associated with an automation environment; determine correlation in the detected activity between two or more of the data sources; store records of determined correlation in the detected activity over time in a data storage system; apply a link salience algorithm to the stored records of determined correlation in the detected activity to determine a salience property; and identify one or more subsystems in the automation environment based on the salience property.

A power tool includes a housing and a sensor, a machine learning controller, a motor, and an electronic controller supported by the housing. The sensor is configured to generate sensor data indicative of an operational parameter of the power tool. The machine learning controller includes a first processor and a first memory and is coupled to the sensor. The machine learning controller further includes a machine learning control program configured to receive the sensor data, process the sensor data using the machine learning control program, and generate an output based on the sensor data using the machine learning control program. The electronic controller includes a second processor and a second memory and is coupled to the motor and to the machine learning controller. The electronic controller is configured to receive the output from the machine learning controller and control the motor based on the output.

In order to enable a seamless configuration modification during operation, a first automation device sends a second automation device a request for parameter modification. The second automation device responds to the request, such that a standby acknowledgement of the request is sent. Immediately with the transmission of the standby acknowledgement in the second automation device, an output process image is frozen, and the modification of the communication parameters for the second automation device is carried out. The first automation device responds, such that after receiving the standby acknowledgement in the first automation device, the communication is immediately stopped and the modification of the communication parameters is carried out for the first automation device. An input process image is frozen.