A computing system has a processor that receives client data defining one or more electrical loads of an industrial automation project. The computing system generates one or more motor control lineups based on the client data and historical data associated with a plurality of previous industrial automation projects. The computing system receives a selection of a motor control lineup form a plurality of motor control lineups. A visual representation of the selected motor control lineup is generated and transmitted for display on a graphical user interface.

Provided are embodiments of monitoring an industrial process that include determining a process structural model that defines operational characteristics of equipment used to perform the industrial process, determining an interface application to display a process flow diagram (PFD) for the process, obtaining an interface model for the interface application, determining (based on the process structural model) a grouped process structural model for the process, determining (based on the grouped process structural model) a routed process model for the process, and generating (based on the routed process model) the PFD for the process. The determining of the grouped process structural model including grouping sensors with associated process equipment, grouping process equipment, and grouping connections between pieces of equipment to determine an edge-grouped process structural model for the industrial process, and incorporating the interface model with the edge-grouped process structural model to determine the grouped process structural model.

A computer-implemented method and system for process schedule reconciliation receives a scheduling model and an initial schedule for reconciliation, where the initial schedule includes projected plant data. Current plant data is imported into the system, and dynamic optimization data representing trends in process data at time-varied values for key process and operation parameters are identified. The current plant data and projected plant data is processed using mathematical modeling techniques to identify event boundaries, stream flowrates associated with tanks and process units. The system builds an optimization model applying identified event boundaries, stream flowrates, dynamic optimization data, key scheduling parameters and pre-determined constraints along a period of time that includes priority slots to reconcile the projected plant data of the initial schedule with the current plant data, and then solves the optimization model to develop a reconciled schedule.

A controller includes a setting information storage unit that stores at least a folder search priority order setting defining an order of priority in which folders are searched when a program is called, a call information storage unit that stores call information, which is information relating to a correspondence relationship between a call source program and a call destination program among programs stored in advance in a program storage unit, a call information generation unit that generates call information when there is a possibility that the correspondence relationship between the programs has changed, and a call information determination unit that determines whether or not the correspondence relationship between the programs has changed by comparing the call information generated by the call information generation unit with the call information stored in the call information storage unit.

An industrial integrated development environment (IDE) supports collaborative tools that allow multiple designers and programmers to remotely submit design input to the same automation system project in parallel while maintaining project consistency. These collaborative features can include, for example, brokering between different sets of design input directed to the same portion of the system project, generating notifications to remote designers when a portion of the system project is modified, sharing of development interfaces or environments, facilitating involvement of outside technical support experts to assist with design issues, and other collaborative features.

A method of controlling an industrial system including at least one agent, the method including providing a representation of the industrial system as a finite state machine, the state machine including a plurality of nodes and a plurality of edges, where each node represents a discrete system state of the industrial system, each edge represents an action for a state transition between system states represented by two of the nodes, and at least one execution value is associated with at least one of the edges; executing at least one action by the at least one agent, the at least one action being represented by at least one of the edges; and modifying at least one execution value associated with at least one of the edges representing the at least one executed action, based on an outcome of the at least one executed action.

An information collection system for industrial machines includes processing circuitry that communicates with one or more industrial machines that execute one or more predetermined processes with respect to an object, identify, based on predetermined information received from the one or more industrial machines, the one or more predetermined processes executed with respect to the object, and assigns process identification information related to the one or more predetermined processes to collected information related to the object collected from the one or more industrial machines.

An apparatus comprising: a procedure progress database in which progress status and identification information of a relevant member are recorded for each of pieces of work; a team status evaluation unit which identifies a member requiring support on work in the monitoring and control within a team; an operation support content determination processing unit which determines, as support content, information on the possibility of substitution by a member having the operation authority for the work that the identified member is scheduled to perform; and a presentation information generation processing unit which selects a plurality of persons including the identified member as presentation targets of the determined support content, and generates presentation information in accordance with each of the selected persons.

A distributed control system (DCS) of an industrial process plant includes a data center storing a plant information model that includes a description of physical components, the control framework, and the control network of the plant using a modeling language. A set of exposed APIs provides DCS applications access to the model, and to an optional generic framework of the data center which stores basic structures and functions from which the DCS may automatically generate other structures and functions to populate the model and to automatically create various applications and routines utilized during run-time operations of the DCS and plant. Upon initialization, the DCS may automatically sense the I/O types of its interface ports, detect communicatively connected physical components within the plant, and automatically populate the plant information model accordingly. The DCS may optionally automatically generate related control routines and/or I/O data delivery mechanisms, HMI routines, and the like.

The subject matter herein provides for AI-based prediction of production defects in association with a production system, such as a semiconductor manufacturing machine. In one embodiment, a method begins by receiving production data from the production system. The production data typically comprises non-homogeneous machine parameters and maintenance data, quality test data, and product and process data. Using the production data, a neural network is trained to model an operation of a given machine in the production system. Preferably, the training involves multi-task learning, transfer learning (e.g., using knowledge obtained with respect to a machine of the same type as the given machine), and a combination of multi-task learning and transfer learning. Once the model is trained, it is associated with the given machine operating environment, wherein it is used to provide quality assurance predictions.