A system for autonomously measuring workpieces, the system comprising one or more mobile robots, configured to move autonomously in a production environment with a plurality of production facilities that produce a plurality of different workpieces, each of the mobile robots comprising a spatial localization system for deriving a location of the mobile robot in the production environment, an autonomous navigation and propulsion unit configured for providing mobility of the mobile robot in the production environment, a wireless communication interface providing a data link to at least one other mobile robot and/or to computation and storage system, wherein a first mobile robot comprises a sensor setup comprising one or more sensors and is configured to use one or more of the sensors for identifying a workpiece to be measured and for determining an at least rough position of the workpiece that allows collecting or measuring the workpiece.

A modular automation support system for modular plants comprises an automation engineering support system comprising a feedback processing component configured to modify the operation of one or more other components of the automation engineering support system based on user feedback.

Some implementations of the disclosure are directed to a method, comprising: receiving tapped data that was tapped from a controller of an industrial control system (ICS) while the controller executed first control code to control ICS devices of the ICS, the tapped data used during execution of the first control code, and the tapped data comprising input data obtained from one or more input components of the controller communicatively coupled to the ICS devices, or output data obtained from one or more output components of the controller communicatively coupled to the ICS devices; and after receiving the tapped data, executing, using at least the tapped data, second control code that provides an emulation of the controller, the emulation comprising running, using at least the tapped data, a process of the first control code at a faster rate than it is run by the controller executing the first control code.

The disclosure relates to a system (10) for monitoring and/or controlling one or more chemical plant(s) (12), wherein the system (10) comprises a first processing layer (14) associated with the chemical plant (12) and communicatively coupled to a second processing layer (16), wherein the first processing layer (14) and the second processing layer (16) are configured in a secure network (18, 20), wherein the first processing layer (14) provides process or asset specific data (22) of the chemical plant (12) to the second processing layer (16), wherein the second processing layer (16) is configured to contextualize process or asset specific data to generate plant specific data and to provide plant specific data (24) of one or more chemical plant(s) (12) to an interface (26) to an external network.

A diagnosis system diagnoses presence or absence of an abnormality from data pieces collected in a factory. The diagnosis system includes (i) a diagnoser that diagnoses presence or absence of an abnormality by classifying, in accordance with a diagnosis model defining a plurality of groups, the collected data pieces into at least one of the plurality of groups, (ii) an extractor that extracts, from the collected data pieces, a candidate for a data piece to belong to a new group different from the plurality of groups, (iii) a reception device that provides candidate information relating to the candidate extracted by the extractor, (iv) and a learner that learns a new model including the new group. The diagnoser diagnoses presence or absence of an abnormality with the new model after the new model is learned.

A computer implemented method for generating a problem specific representation of a process network to enable controlling or monitoring a process network with at least two interconnected chemical plants, the method comprising the steps of providing a first digital representation of the process network comprising a digital process representation of each plant, its connections to other plants and sensor elements placed in the process network, generating based on the first digital representation a graph structure including vertices representing unit operations, edges linking unit operations representing at least physico-chemical quantities, wherein the edges include edge meta data representing at least physico-chemical quantities, and a measurable tag, generating based on the graph structure a collapsed graph structure including, vertices representing virtual unit operations, edges linking virtual unit operations representing at least physico-chemical quantities, wherein the edges include edge meta data representing observable physico chemical quantities, and their relation to vertices, deriving a set of balance equations from the collapsed graph structure, providing the set of balance equations, and physico- chemical quantities for monitoring and/or controlling operation of a process network is proposed.

According to an aspect of the present disclosure, a computer-implemented includes creating a plurality of basic skill functions for a controllable physical device of an autonomous system. Each basic skill function includes a functional description for using the controllable physical device to interact with a physical environment to perform a defined objective. The method further includes selecting one or more basic skill functions to configure the controllable physical device to perform a defined task. The method also includes determining a decorator skill function specifying at least one constraint. The decorator skill function is configured to impose, at run-time, the at least one constraint, on the one or more basic skill functions. The method further includes generating executable code by applying the decorator skill function to the one or more basic skill functions, and actuating the controllable physical device using the executable code.

A plurality of hierarchy information management devices include a first hierarchy information management unit managing first hierarchy information in which information on instruments is represented in a hierarchy structure, and each hierarchy information management device manages the first hierarchy information of a different one of the instruments. A monitoring device includes: a second hierarchy information management unit generating second hierarchy information based on a plurality of different pieces of the first hierarchy information acquired from the hierarchy information management devices, the second hierarchy information being hierarchy information in which the first hierarchy information is connected in a hierarchy structure; a display unit displaying information; and a display processing unit performing a process of switching between a plurality of different monitoring screens for display on the display unit, the different monitoring screens corresponding to different layers in the second hierarchy information and showing the operating state of the monitoring targets.

A function to optimize factory management using information on operation ability and capability of an operator and a machine is provided.

Provided is a factory management device which makes a plan for a factory, the device including a storage unit which stores operator measurement information obtained by measuring a movement of an operator and machine measurement information obtained by measuring a predetermined value which indicates an operating state of a machine that is production equipment of the factory, an operator ability prediction unit which predicts changes in operation ability of the operator using the operator measurement information, a machine capability prediction unit which predicts changes in operation capability of the machine using the machine measurement information, a production capacity prediction unit which predicts a production capacity of the factory using prediction of changes in the operation ability and capability of the operator and the machine, and a planning unit which makes a plan for the factory that satisfies a predetermined productivity index using the predicted production capacity of the factory.

Methods, systems, and computer programs are presented for predicting the performance of semiconductor manufacturing equipment operations. One method includes an operation for obtaining machine-learning (ML) models, each model related to predicting a performance metric for an operation of a semiconductor manufacturing tool. Further, each ML model utilizes features defining inputs for the ML model. The method further includes an operation for receiving a process definition for manufacturing a product with the semiconductor manufacturing tool. One or more ML models are utilized to estimate a performance of the process definition used in the semiconductor manufacturing tool. Additionally, the method includes presenting, on a display, results showing the estimate of the performance of the manufacturing of the product. In some aspects, the use of hybrid models improves the predictive accuracy of the system by augmenting the capabilities of data-driven models with the reinforcement provided by the physics-based models.