Abstract: A method for determining an interdependency between a plurality of elements in an industrial processing system includes: providing a process flow diagram (PFD) of a topology of the processing system; transforming the PFD into a directed graph, each element of the plurality of elements being transformed into a node and each relation between the plurality of elements being transformed into a directed edge; selecting one node of the plurality of nodes as a starting node; and constructing a subgraph, the subgraph including all the nodes that are forward-connected from the starting node so as to show at least one interdependency between the plurality of elements in the subgraph.

Abstract: An assistance system comprises a plant topology repository comprising a representation of the components of the plant and relations between the components; a monitoring subsystem configured for monitoring signals from the components and for monitoring a related event, as a key for the monitored signals; an aggregation subsystem configured for storing a plurality of the monitored signals and the related events, wherein at least one of the events is the abnormal situation; an identification subsystem configured for comparing currently monitored signals to stored monitored signals and the related event; and an evaluation subsystem configured for outputting a predefined action, if the currently monitored signals match to the event that is the abnormal situation.

Abstract: A method for generating and/or augmenting an execution protocol for an SOP in an industrial plant includes providing at least one SOP of the plant, which includes a plurality of steps; providing measurement data; for each step of the SOP, determining from the measurement data a subset of the measurement data that is indicative of actions performed for the purpose of executing this particular step of the SOP; and aggregating the subset of the measurement data determined for each step of the SOP into at least one instruction for executing this particular step of the SOP, wherein this instruction is part of the sought protocol.

Abstract: The invention relates to an input device comprising capacitive detection device having detection surface including first array of array electrodes; electronic evaluation unit electrically connected to array electrodes to form associated electric measuring field array for spatially resolving detection of capacitive influence on detection surface; handling means disposed on detection surface to be movable along an adjustment path parallel to detection surface, in order to perform an operating input; position indicator; and coupling device with a first surface on which a second array of coupling electrodes is formed, and with a second surface on which a third array including spaced-apart contact surfaces is formed. Based on the position of the handling means, contact surfaces are touch-contacted by the position indicator. The coupling device includes several electrically conductive connections to capacitively influence the measuring field array.

Abstract: 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.

Abstract: A method includes acquiring state variables that characterize an operational state of an industrial plant; acquiring interaction events of a plant operator interacting with the distributed control system via a human-machine interface; determining based on the interaction events, and with state variables as input data, whether one or more interaction events are indicative of the plant operator executing a task that is not sufficiently covered by engineering of the distributed control system. When this determination is positive, mapping the input data to an amendment and/or augmentation for the engineering tool that has generated the application code.

Abstract: A method for analysing process data related to a segment of a production process includes providing a process data sequence of the segment of the production process exhibiting a data pattern of at least one process variable to be analyzed; providing a set of metadata; determining process data sequences, which are stored in a first database; determining a start timestamp and end timestamp of each of the determined process data sequence, based on the first database; and calculating a similarity value for each of the determined process data sequences compared to the provided process data sequence, based on the data pattern of the at least one process variable, wherein the determined process data sequences for the calculation are provided, based on the related start timestamps and end timestamps, by accessing a second database comprising the process data sequences, for analysing the process data.

Abstract: A control module is adapted to control technical equipment by processing batch-run data from the technical equipment. The control module operates according to parameters that are obtained by a parameter module. The module receives a reference plurality of multi-variate reference time series with data values from sources that are related to the equipment. There are time series with measurement values and time series with data that describes particular manufacturing operations during a batch-run time interval. The module splits the time interval into phases by determining transitions between the particular manufacturing operations, and divides the time series into particular phase-specific partial series. For each phase separately, and for the phase-specific partial series in combination, the module differentiates phase-specific time series into relevant partial time series or non-relevant partial time series and set the parameters accordingly.

Abstract: A method for controlling a virtual assistant for an industrial plant includes receiving by an input interface an information request, wherein the information request comprises at least one request for receiving information about at least part of the industrial plant; determining by a control unit a model specification using the received information request; determining by a model manager a machine learning model using the model specification; and providing by the control unit a response to the information request using the determined machine learning model.

Abstract: A rotary knob having (inter alia, adjustable) a coupling electrode and capacitive coupling devices for placing on a touch screen is provided. Depending on the position of the rotary knob or of the coupling electrode, contact of a corresponding partial region of the touchscreen is detected. The rotary knob is designed detect and ignore disturbing influences such as drops of water.

Abstract: A computer-implemented method of generating a training data set for training an artificial intelligence module includes providing first and second data sets, the first data set including first data elements indicative of a first operational condition, the second data set including second data elements indicative of a second operational condition that matches the first operational condition. The method further comprises determining a data transformation for transforming the first data elements into the second data elements; applying the data transformation to the first data elements and/or to further data elements of further data sets, thereby generating a transformed data set; and generating a training data set for training the AI module based on at least a part of the transformed data set.

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 computer-implemented method to control technical equipment that performs a production batch-run of a production process, the technical equipment providing data in a form of time-series from a set of data sources, the data sources being related to the technical equipment, includes: accessing a reference time-series with data from a previously performed batch-run of the production process, the reference time-series being related to a parameter for the technical equipment; and while the technical equipment performs the production batch-run: receiving a production time-series with data, identifying a sub-series of the reference time-series, and comparing the received time-series and the sub-series of the reference time-series, to provide an indication of similarity or non-similarity, in case of similarity, controlling the technical equipment during a continuation of the production batch-run, by using the parameter as control parameter.

Abstract: A method of transfer learning for a specific production process of an industrial plant includes providing data templates defining expected data for a production process, and providing plant data, wherein the data templates define groupings for the expected data according to their relation in the industrial plant; determining a process instance and defining a mapping with the plant data; determining historic process data; determining training data using the determined process instance and the determined historic process data, wherein the training data comprises a structured data matrix, wherein columns of the data matrix represent the sensor data that are grouped in accordance with the data template and wherein rows of the data matrix represent timestamps of obtaining the sensor data; providing a pre-trained machine learning model using the determined process instance; and training a new machine learning model using the provided pre-trained model and the determined training data.

Abstract: A method of hierarchical machine learning includes receiving a topology model having information on hierarchical relations between components of the industrial plant, determining a representation hierarchy comprising a plurality of levels, wherein each representation on a higher level represents a group of representations on a lower level, wherein the representations comprise a machine learning model, and training an output machine learning model using the determined hierarchical representations.

Abstract: An industrial plant machine learning system includes a machine learning model, providing machine learning data, an industrial plant providing plant data and an abstraction layer, connecting the machine learning model and the industrial plant, wherein the abstraction layer is configured to provide standardized communication between the machine learning model and the industrial plant, using a machine learning markup language.

Abstract: A method includes training a first control model by utilizing a first set of input data as first input, resulting in a trained first control model; copying the trained first control model to a second control model, wherein, after copying, the second input layer and the plurality of second hidden layers is identical to the plurality of first hidden layers, and the first output layer is replaced by the second output layer; freezing the plurality of second hidden layers; training the second control model by utilizing the first set of input data as second input, resulting in a trained second control model; and running the trained second control model by utilizing a second set of input data as second input, wherein the second output outputs the quality measure of the first control model.

Abstract: A computer-implemented method for automating the development of industrial machine learning applications includes one or more sub-methods that, depending on the industrial machine learning problem, may be executed iteratively. These sub-methods include at least one of a method to automate the data cleaning in training and later application of machine learning models, a method to label time series (in particular signal data) with help of other timestamp records, feature engineering with the help of process mining, and automated hyper-parameter tuning for data segmentation and classification.

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.