A press and a process for operating it, has an upper tool and a lower tool for shaping a workpiece and a work unit including a press drive and/or a drawing device, is controllable by a controller, to perform the shaping process. The controller outputs, to the work unit, press process values, which define the sequence of events of the shaping process. Workpiece parameters characterize, for example, the material and/or the shape and/or at least one dimension and/or quality of the workpiece, which still has to be shaped or has already been shaped. The controller is assigned a composition matrix which contains, for each defined workpiece parameter and for each defined press process value, an individually changeable function, to describe the relationship between every one of the defined workpiece parameters and the defined press process values. This composition matrix allows a quick and rapid adjustment and adaptation of the press.

A method for detecting an abnormal event performed by a computing device according to an embodiment of the present disclosure includes analyzing log data to identify sequentially executed activities and generating a process model comprising a node indicating each of the activities and an edge indicating an execution predecessor relationship between the activities, and outputting a result of analyzing log data generated in real time based on the process model.

A display unit of a design assistance system highlights an error section of a 3D model and displays at least one of a designation condition recognized by an elimination condition recognition unit and an elimination section.

A method for providing a solution strategy for a current event in industrial process automation includes monitoring a process for events and recording manual user action data, upon occurrence of an event, acquiring the recorded data regarding manual user actions before, during, and after the occurrence of the event, learning a procedure for handling the event based on the acquired data, and applying the learnt procedure to a currently occurring event.

An NFT system operates by receiving NFT data and NFT metadata associated with a manufacturing system, wherein the NFT metadata includes conditions data associated with use conditions; and facilitating, based on the NFT data and the NFT metadata, generation of an NFT associated with the manufacturing system; wherein the manufacturing system enables manufacture of at least one manufactured item via the manufacturing system when the use conditions are met.

Systems and methods for monitoring pharmaceutical manufacturing processes are described and disclosed herein. The systems and methods monitors and creates a historic record of the pharmaceutical manufacturing process and provides it to an end user or a plurality of end users. Consequently, the methods provide a means to perform quality control on an integrated level whereby a pharmaceutical manufacturer can ensure data and product integrity and minimize cost.

A product state estimation device includes: an examination result acquisition device that acquires an examination result related to a state of a product obtained through each shot by a die-casting machine; a time series data acquisition device that acquires time series data based on an output from a sensor that detects an operation state of the die-casting machine at each shot; a time series data manipulation device that performs manipulation that clips data of a predetermined time interval out of the time series data; an estimation model generation device that generates an estimation model by using a neural network with the examination result of the product and the manipulated time series data as learning data; and an estimation device that estimates information related to quality of the product based on the manipulated time series data obtained from a plurality of detection signals at each shot by using the estimation model.

Systems, techniques, and computer-program products that, individually and in combination, permit machine condition monitoring are provided. In some aspects, state estimation and anomaly localization can be determined jointly. To that end, in some embodiments, systems can be configured using at least a synthetic training dataset. The synthetic training dataset includes sensor output data that incorporates synthetic a random amount of noise to each one of multiple sensor devices that probe an industrial machine. The training dataset also includes synthetic information indicative of location of anomalous sensor device(s) of the multiple sensor devices. Therefore, the systems can learn to determine state estimation and anomalous localization concurrently, in a single operation. Accordingly, the training of the systems is consistent with the operation of the systems during machine condition monitoring. Embodiments of the disclosure provide superior predictive performance over conventional machine condition monitoring approaches.

A system for monitoring industrial processes using an evolving set of iso-functional smart nodes, for a distributed mesh network, each node comprising a Linux or Linux-compatible computer hardware architecture and a software stack. Each node receives an execute statement from a program hosted by another node in the mesh and, by execution on the computer hardware architecture of each node, said program is responsible for: two-way communication with other nodes or a central platform (Big Data Management), control of sensors or programmable automatons for monitoring a process or actuators, acquisition and logging of data from the latter, formatting of data and decentralized calculations, said central platform allowing the acquisition, management and storing of a data lake and comprising means for synchronous or asynchronous communication with the distributed mesh network.

There are provided a slave, a work machine, and a method for storing log information, which are capable of appropriately store the log information when a communication abnormality occurs such that communication cannot be kept between a master and the slave in an industrial network. The control section performs first storage processing storing the log information into a volatile storage section when a communication abnormality occurs such that communication with the master cannot be kept, communication abnormality determination processing for determining whether the communication abnormality has occurred, and second storage processing storing the log information into a non-volatile storage section by acquiring the log information from the volatile storage section, in response to a determination made in that the communication abnormality has occurred as a result of the communication abnormality determination processing.