Abstract: A system and method for monitoring a switchgear includes an infrared camera, a processing unit, and an output unit. The infrared camera acquires an infrared image of the switchgear, and the processing unit converts it into a binary image. Pixels in the infrared image having a temperature equal to or above a first threshold value are given the same first value. Pixels in the infrared image having a temperature below the first threshold value are given the same second value. The processing unit is configured to implement a Siamese neural network to determine if a hot spot exists in the infrared image.

Abstract: A method of integrating modules into a hybrid modular plant comprising a discrete manufacturing part and a continuous manufacturing part includes integrating the discrete part into the continuous part, comprising constructing at least one module definition file mapping one or more discrete-part units of the discrete part to a continuous-part module and importing the module definition file into an orchestration layer of the continuous part. Alternatively, the method comprises integrating the continuous part into the discrete part, comprising constructing one or more interfaces representing each continuous-part module as one or more respective discrete-part units.

Abstract: An explainer system includes a system-monitor machine learning model trained to predict states of a monitored system, a perturbator applying predetermined perturbations to original sample data collected from the monitored system to produce perturbed sample data. The system is configured to input the perturbed sample data to the prediction system. The explainer comprises a tester that receives model output from the prediction system, the model output comprising original model output produced by the system-monitor machine learning model based on the original sample data and deviated model output produced by the system-monitor machine learning model based on the perturbed sample data, the deviated model output comprising deviations from the original model output, the deviations resulting from the applied perturbations. An extractor receives data defining the perturbations and the resulting deviations and extracts therefrom important features for explaining the model output.

Abstract: A method and system for removing undesirable inferences from a machine learning model include a search component configured to receive a rejected explanation of model output provided by the machine learning model, identify data samples to unlearn by selecting training samples from training data that were used to train the machine learning model, the selected training samples being associated with explanations that are similar to the rejected explanation according to a calculated similarity measure, and pass the data samples to unlearn to a machine unlearning unit.

Abstract: A data processing device capable of performing problem diagnosis in a production system with a plurality of robots includes: a first time series obtaining part for obtaining historical event data used for determining some historical alarm indicator in time series and storing the historical event data as first time series data; a historic alarm indicator calculation part for calculating a series of historic alarm indicators using statistic characteristics of the first time series data; a threshold definition part for defining at least one threshold value based on a statistical distribution of the historical alarm indicators; a second time series obtaining part for obtaining operational event data during operation of the robots used for determining some operational alarm indicator in time series and storing the operational event data as second time series data; and an operational alarm indicator calculation part for calculating a series of operational alarm indicators.

Abstract: A model generation system includes input and output units. The input unit receives a plurality of input value trajectories comprising operational input value trajectories and simulation input value trajectories relating to an industrial process. The processing unit implements a simulator of the industrial process and generates behavioral data for at least some of the plurality of input value trajectories. The processing unit further implements a machine learning algorithm that models the industrial process, and trains the machine learning algorithm.

Abstract: An industrial plant operator intervention system for use in an industrial plant includes a processing unit configured to monitor and analyze industrial plant operation data to detect an anomaly in the industrial plant operation data that warrants initiating an operator intervention, and in response to detecting the anomaly, automatically determine a user interface configuration of a user interface to be presented to a designated operator who is to perform the operator intervention. The user interface configuration is determined on the basis of technical context data, including industrial plant operation data associated with the anomaly, and on the basis of operator data pertaining to the designated operator, in such a manner that an anomaly-related and operator-specific user interface configuration is obtained.

Abstract: A method for generating a process model modeling a manual mode procedure instance of a plant process includes providing log events of operational actions; selecting related sequences of manual mode operational actions from the log events; filtering the related sequences according to an individual plant section; identifying a sequential order from the filtered related sequences; determining statistical properties of values of related process variables and/or statistical properties of values of related set point changes to each sequential ordered manual mode operational action from the filtered related sequences; generating the process model of the manual mode procedure instance by arranging related manual mode operational actions with the sequential order of each operational action assigned with the statistical properties of the values of related process variables and/or assigned with the statistical properties of the values of the related set point changes.

Abstract: A method and computer program product including training a machine learning model by means of input data and score data, wherein the machine learning model is an artificial neural net, ANN; running the trained machine learning model by applying the first time-series to the trained machine learning model; and outputting, by the trained machine learning model, an output value, comprising at least a second criticality value of the at least one predicted observable process-value indicative of the abnormal behaviour of the industrial process in a predefined temporal distance.

Abstract: A system and method for programming a robot includes providing a 3D representation of workpieces to be handled by the robot, and of a working environment; synthesizing and displaying a view of the working environment comprising an image of the workpieces at respective initial positions; identifying matching features of the selected workpiece and of the working environment which are able to cooperate to hold the workpiece in a final position in the working environment, and a skill by which the matching features can be brought to cooperate; identifying an intermediate position from where applying the skill to the workpiece moves the workpiece to the final position; and adding to a motion program for the robot a routine for moving the workpiece from its initial position to the intermediate position and for applying the skill to the workpiece at the intermediate position.

Abstract: A system and method provides an impact list of affecting equipment elements that affect an industrial sub-process. The method comprises the steps of selecting, in a topology model, the sub-process, wherein the sub-process is an equipment element that is a part of an industrial plant or process, and wherein the topology model is a graph, whose nodes represent equipment elements and whose edges represent interconnections between the equipment elements; traversing the nodes of the topology model, wherein the traversing starts from the selected sub-process and uses a traversing strategy; and for each of the at least one equipment elements, if the equipment element affects the industrial sub-process by an affecting degree greater than a first predefined affecting degree, adding the equipment element to the impact list of affecting equipment elements.

Abstract: A decision support system and method for an industrial plant is configured and operates to: obtain a causal graph modeling causal assumptions relating to conditional dependence between variables in the industrial plant; obtain observational data relating to operation of the industrial plant; and perform causal inference using the causal graph and the observational data to estimate at least one causal effect relevant for making decisions when operating the industrial plant.

Abstract: The invention relates to a medium voltage switchgear or control gear monitoring system (10), comprising: an infrared camera (20); and a processing unit (30); wherein the infrared camera is configured to be mounted within a medium voltage switchgear or control gear (40); wherein the infrared camera is configured to acquire an infrared image, wherein the infrared image comprises image data of two or three current carrying parts of the switchgear or control gear (50), and wherein the two or three current carrying parts are the same current carry part of two or three equivalent systems within the switchgear or control gear; wherein the infrared camera is configured to provide the infrared image to the processing unit; wherein the processing unit is configured to determine that the two or three current carrying parts are operating correctly or that one of the two or three current carrying parts has a fault, wherein the determination comprises analysis of the infrared image by an autoencoder implemented by the p

Abstract: A computer-implemented resource management method for modular plants may include: receiving data identifying a required module type to be assembled into the modular plant as part of a module pipeline including one or more modules; and executing an optimization algorithm to select, from a plurality of modules having the required module type, a module for inclusion in the module pipeline on the basis of one or more predetermined optimization criteria.

Abstract: A resource management system for modular plants includes a database providing a module library of semantic modules representing respective modules in a module pool. At least one of the semantic modules includes a semantic description of the respective module, where the semantic description includes abstract data according to a semantic data model, and where the abstract data describes attributes of the respective module not found in a standard description file for the module. Based thereon, the system facilitates automated generation and optimization of module pipelines.

Abstract: A computer-implemented method for reverse engineering control logic of a module for a modular industrial plant includes: obtaining module-related data including runtime data relating to prior use of the module during a timeperiod in which at least one piece of equipment of the module transitions from a first equipment state to a second equipment state, the runtime data including tags indicating the equipment state of the equipment at a plurality of timepoints during the timeperiod; and inferring from the module-related data one or more equipment state transition conditions causing the equipment to transition from the first equipment state to the second equipment state.

Abstract: A method for configuring a modular industrial plant using an engineering tool includes using a plant engineering facility of the tool to create a representation of the modular industrial plant identifying modules to be orchestrated in the modular industrial plant. The modules include at least one function module that includes control software for the modular industrial plant. The method further includes using a module engineering facility of the tool to configure the function module for use in the modular industrial plant by editing a placeholder configuration file created by the tool for defining the configuration of the function module. Editing the configuration file causes a representation of the function module in the plant engineering facility to be automatically updated to reflect adaptations made to the function module using the module engineering facility. The method includes instructing the tool to assign the function modules so configured to the modular industrial plant.

Abstract: Systems and methods for event analytics in a modular industrial plant are provided. A method includes: monitoring events in a module of the modular industrial plant during a predetermined time interval; generating a module fingerprint based on the monitored events occurring in the module during the predetermined time interval; and performing module-based event analytics based on the generated module fingerprint.

Abstract: A method for applying machine learning to an application includes: a) generating a set of candidate parameters by a learner; b) executing a program in at least one simulated application based on the set of candidate parameters and providing interim results of tested sets of candidate parameters based on a measured performance information of the execution of the program; c) collecting a predetermined number of interim results and providing an end result based on a combination of the candidate parameters and the measured performance information by a trainer; and d) generating a new set of candidate parameters by the learner based on the end result for execution by the unchanged program.

Abstract: A method for applying machine learning to an application includes: a) generating a candidate policy by a learner; b) executing a program in at least one simulated application based on a set of candidate parameters provided based on the candidate policy and a state of the at least one simulated application, execution of the program providing interim results of tested sets of candidate parameters based on a measured performance information of the execution of the program; c) collecting a predetermined number of interim results and providing an end result based on a combination of the candidate parameters and/or the state with the measured performances information by a trainer; and d) generating a new candidate policy by the learner based on the end result.