A computer implemented method for the maintenance optimization of a fleet or group of turbomachinery assets is disclosed. The method comprises the step of model training and setup, aiming at setting configurations parameters, that can be executed offline, and the step of online calculation on new input data, which is based on detected data and extracted statistical features. An anomaly identification and classification follow, thus calculating a risk assessment, for estimating the risk that an anomaly might cause any event that requires a maintenance task to be executed on one or more assets of the fleet.

A method for producing material boards in a production plant in which apparatuses form a material into a mat that is pressed to obtain the material board which has specific quality parameters. The production plant and/or the apparatuses are controlled in an open- or closed-loop manner by a controller, which preferably includes a programmable logic controller, and input parameters are received, processed and/or output by the controller. The input parameters are formed at least from settable product parameters for the material board to be produced, from settable and/or recorded plant parameters of the production plant and/or the apparatuses and/or from recorded material parameters. A quality value of at least one quality parameter of the material board to be produced is determined based on the input parameters by an algorithm based on artificial intelligence. The algorithm is trained or formed by a database which has at least one quality parameter and input parameters correlating with the quality parameter.

Systems and methods are provided for detecting events in industrial processes. An acquisition system may include one of a camera and an audio recorder to acquire monitoring data in the form of one of imaging data and acoustic data, respectively. A computer system, may include a machine learning engine and may be programmed to classify the monitoring data under a classifier, quantify, based on the classifier, the monitoring data with at least one quantifier, and detect an event when the at least one quantifier satisfies a predetermined rule corresponding to the at least one quantifier.

A method and dynamic learning system for providing dynamic cross learning is disclosed. The dynamic learning system identifies one or more changes in an environment in which an automated task performing device is scheduled to perform one or more activities. The dynamic learning system initiates a dynamic learning associated with the one or more changes for the automated task performing device based on pre-stored contextual information. Based on the dynamic learning, one or more actions is provided to the automated task performing device to perform the one or more activities in view of the one more changes. Therefore, the present disclosure facilitates dynamic determination and analysis of environment and situation for the automated task performing device for performing the activities. Thus, leading to dynamic decision-making to provide adjustment to the automated task performing device in any situation.

The invention relates to a system (1) for controlling a production plant (3) consisting of a plurality of plant parts (2), in particular a metallurgical production plant for producing industrial goods such as metal semi-finished products and/or metal end products, wherein each plant part (2) has an input quality window (4), an output quality window (5) and a process window (6), wherein the input quality window (4) of a plant part (2) defines the quality characteristics of the input product that are required by the plant part (2) and the output quality window (5) of a plant part (2) defines the quality characteristics of the output product that are allowed by the plant part (2) after processing the input product, wherein, in a production plant (3) consisting of the plurality of plant parts (2), the output quality window (5) of an upstream plant part (2) corresponds to the input quality window (4) of the downstream plant part (2), wherein the process window (6) defines the setting values (7) that can be implemented by the respective plant part (2) for a plant automation unit of the plant part (2), wherein each plant part (2) detects the current state by means of sensors (8) and adapts the process window (6) of the plant part (2) to the detected current state, and wherein the system (1) for controlling the production plant (3) consisting of the plurality of plant parts (2) determines setting values (7) for the respective plant automation unit for each plant part (2), the setting values being within the process windows (6) and that the product produced in the production plant (3) meets the quality characteristics required by the input quality windows (4) and output quality windows (5) of the plurality of plant parts (2).

The invention further relates to a corresponding method and computer program.

Systems and methods are provided for detecting events in industrial processes. An acquisition system may include one of a camera and an audio recorder to acquire monitoring data in the form of one of imaging data and acoustic data, respectively. A computer system, may include a machine learning engine and may be programmed to classify the monitoring data under a classifier, quantify, based on the classifier, the monitoring data with at least one quantifier, and detect an event when the at least one quantifier satisfies a predetermined rule corresponding to the at least one quantifier.

A method for controlling a robot to perform a task. The method includes acquiring a target image data element comprising at least one target image from a perspective of an end-effector of the robot at a target position of the robot in which the robot has performed the task, acquiring an origin image data element comprising at least one origin image from the perspective of the end-effector of the robot at an origin position of the robot, supplying the origin image data element and the target image data element to a machine learning model configured to derive a delta movement between the origin current position and the target position and controlling the robot to move according to the delta movement to perform the task.

According to embodiments, a robot system configured to determine a recipe from an ingredient list obtained by detecting a type and an amount of an ingredient includes: a storage table configured to detect a weight change by a scale installed under each of cells that store ingredients according to types of the ingredients, detect the cell of which a weight is changed according to an ingredient selection of a user to identify the type of the ingredient, and identify the amount of the ingredient based on a degree of the weight change of the cell; and a robot configured to receive the ingredient list obtained from the type and the amount of the ingredient identified by the storage table from the storage table, retrieve menus to be cooked with the ingredient list, and perform cooking by determining the recipe according to a menu selected from the menus.

A robot control device includes: a trained model built by being trained on work data; a control data acquisition section which acquires control data of the robot based on data from the trained model; base trained models built for each of a plurality of simple operations by being trained on work data; an operation label storage section which stores operation labels corresponding to the base trained models; a base trained model combination information acquisition section which acquires combination information when the trained model is represented by a combination of a plurality of the base trained models, by acquiring a similarity between the trained model and the respective base trained models; and an information output section which outputs the operation label corresponding to each of the base trained models which represent the trained model.

A robot system includes a robot, state detection sensors to, a timekeeping unit, a learning control unit, a determination unit, an operation device, and an input unit, and an additional learning unit. The determination unit determines whether or not the work of the robot can be continued under the control of the learning control unit based on the state values detected by the state detection sensors to and outputs determination result. The additional learning unit performs additional learning of the determination result indicating that the work of the robot cannot be continued, the operator operation force, work state output by the operation device and the input unit, and timer signal output by the timekeeping unit.