Provided is an operation system including: an evaluation model generation apparatus configured to generate, by machine learning, an evaluation model configured to output an indicator indicating a result of evaluating a state in a piece of equipment with respect to an intended target based on an operation target in the piece of equipment and a state in the piece of equipment; an operation model generation apparatus configured to generate an operation model configured to output an action corresponding to the state in the piece of equipment, by reinforcement learning in which an output of the evaluation model is set as at least a part of a reward; and a control apparatus configured to apply, to a controlled object in the piece of equipment, a manipulated variable based on the action that is output by the operation model according to the state in the piece of equipment.

Provided is an apparatus including a supply unit suppling a value of a state parameter to an operation model outputting a recommendation value of a control parameter of a piece of equipment in response to a value of a state parameter relating to the piece of equipment being input; a control parameter acquisition unit acquiring a recommendation value of a control parameter output from the operation model in response to the supply unit supplying a value of a state parameter to the operation model; an acquisition unit acquiring a model evaluation value corresponding to a result of having operated the piece of equipment according to the recommendation value acquired by the control parameter acquisition unit; and an evaluation unit evaluating the operation model, based on the model evaluation value and a reference evaluation value corresponding to a result of having operated the piece of equipment through manipulation by a human.

A method for operating an industrial automation system may involve receiving, via a first module of a plurality of modules in a control system, an indication that an error between a measurement associated with a target variable that corresponds with at least a portion of the industrial automation system and a modeled value for the target variable. The method may then involve determining, via the first module, whether the error is within a first range of values and retraining a model used to generate the modeled value for the target variable based on a portion of a plurality of sets of data points acquired via a plurality of sensors disposed in the industrial automation system in response to the error being within the first range of values.

Controlling a conveyor installation of a real warehouse having automated machines and persons that are virtualized in a central computer for storing a virtual model of the conveyor installation having the dimensions of the individual conveyor components and the movement parameters thereof. Images of the objects to be conveyed, automated machines and persons in the conveyor installation are captured by sensors at predefined short time intervals and identified by image recognition, and the positions thereof in the conveyor installation are determined. The virtual model is continuously updated with the identification and position determination of the objects in the central computer such that a virtualized real-time model is generated, and the real conveyor installation is centrally controlled with the aid of the model, where material flow control commands are generated for the real actuators for controlling the conveying movement of the automated machines to avoid endangering the persons.

Robust machine learning predictions. Temporal dependencies of process targets for different machine learning models can be captured and evaluated for the impact on process performance for target. The most robust of these different models is selected for deployment based on minimizing variance for the desired performance characteristic.

A method for operating an industrial automation system may involve receiving, via a first module of a plurality of modules in a control system, an indication that an error between a measurement associated with a target variable that corresponds with at least a portion of the industrial automation system and a modeled value for the target variable. The method may then involve determining, via the first module, whether the error is within a first range of values and retraining a model used to generate the modeled value for the target variable based on a portion of a plurality of sets of data points acquired via a plurality of sensors disposed in the industrial automation system in response to the error being within the first range of values.

Generating a digital representation of a process automation system on a cloud-based service platform uses assets integrated into measurement points. The method includes reading TAG information using an edge device, wherein the TAG information is provided in a character chain data type and represents the hierarchical structure of the respective asset. The method also includes transmitting the TAG information to the cloud-based service platform, and parsing the TAG information using an application, wherein a logic is used for the parsing process, and the name of the asset and the name of the measurement point in which the respective asset is integrated are extracted from the TAG information. A structure plan of the system is generated using the application having all of the system measurement points extracted from the TAG information together with all of the assets which are assigned to the measurement points and are extracted from the TAG information.

An information processing device uses models generated under different conditions, and predicts alarms occurring in the case in which each of a plurality of operation patterns, which is virtually generated in relation to the operation of an actual plant by a worker, is implemented with respect to the actual plant. Then, the information processing device sets the degree of reliability of the prediction result regarding each of the plurality of operation patterns. Subsequently, based on the degree of reliability of the prediction result, the information processing device performs display control with respect to the alarms.

An energy monitoring system includes a memory storing instructions to execute an energy modeling technique and processing circuitry for executing the instructions to operate the energy modeling technique. The energy modeling technique includes receiving energy data from a plurality of segments representative of one or more logical subgroups. The energy modeling technique includes categorizing the energy data of the logical subgroups into a plurality of segments. The energy modeling technique includes organizing the plurality of segments into a plurality of state-based hierarchical levels. The energy modeling technique includes calculating energy usage and factors associated with the plurality of state-based hierarchical levels via an energy model. The energy modeling technique includes outputting a visualization representative of the energy data corresponding to each of the segments to a monitoring and control system, resulting in a graphical representation accessible by a user-viewable screen.

A machine learning module is provided trained to generate from a design data record specifying a design variant, a predictive performance distribution and a constraint compliance distribution of the design variant. A predictive performance distribution and a constraint compliance distribution are generated by the machine learning module. The predictive performance distribution is compared with performance values of previously evaluated design data records. A simulation of the corresponding design variant is either run or skipped. A design evaluation record is output which includes a performance value and constraint compliance data each derived from the simulation if the simulation is run or, otherwise, each derived from the predictive performance distribution and the constraint compliance distribution. Depending on the design evaluation records, a performance-optimizing and constraint-compliant design data record is selected from the variety of design data records. The selected design data record is then output for controlling the production system.