Abstract: A method for monitoring a continuous industrial process is described. The industrial process includes a number of processing stations for processing material and a material flow between the number of processing stations. Each processing station dynamically provides data representing a state of the processing station. The method includes providing, for each processing station, a processing station layout of the processing station. The method further includes providing, for each processing station, an interface model of the processing station. The method further includes generating an information metamodel from the processing station layout and the interface model of the number of processing stations. The method further includes generating an adaptive simulation model of the industrial process by importing the data representing the state of the processing station provided by the number of processing stations into the adaptive simulation model via the information metamodel.

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: 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: An apparatus for equipment monitoring includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with batches of temporal sensor data for an item of equipment. Each batch of temporal sensor data includes temporal sensor values as a function of time. The processing unit is configured to process the batches of temporal sensor data to determine batches of spectral sensor data. Each batch of spectral sensor data includes spectral sensor values as a function of frequency. The processing unit is configured to implement at least one statistical process algorithm to process the spectral sensor values for the batches of spectral sensor data to determine index values. For each batch of spectral sensor data there is an index value determined by each of the statistical process algorithms.

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 system and method for outlier detection based on process fingerprints from robot cycle data includes a data collection component, which is configured to collect cyclic data, wherein the cyclic data comprises multiple vectors each of which comprises data from one individual cycle of the robot cycle data; a data storage component, wherein which is configured to store the collected cyclic data; and a data processing component, which is configured to perform cloud processing of the stored cyclic data triggered by a cycle-start signal, wherein the data processing component is configured to parse the stored cyclic data and to process the stored cyclic data based on a configuration file defining metadata of the stored cyclic data, wherein the data processing component is configured extract process fingerprints from the stored cyclic data using the metadata.

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: 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 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 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: 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 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: 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 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: An apparatus for equipment monitoring includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with batches of temporal sensor data for an item of equipment. Each batch of temporal sensor data includes temporal sensor values as a function of time. The processing unit is configured to process the batches of temporal sensor data to determine batches of spectral sensor data. Each batch of spectral sensor data includes spectral sensor values as a function of frequency. The processing unit is configured to implement at least one statistical process algorithm to process the spectral sensor values for the batches of spectral sensor data to determine index values. For each batch of spectral sensor data there is an index value determined by each of the statistical process algorithms.

Abstract: An apparatus includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with sensor data for an item of equipment. The processing unit is configured to implement at least one machine learning algorithm, which has been trained on the basis of a plurality of calibration sensor data for the item of equipment. Training of the at least one machine learning algorithm includes processing the plurality of calibration sensor data to determine at least two clusters representative of different equipment states. The processing unit is configured to implement the at least one machine learning algorithm to process the sensor data to assign the sensor data to a cluster of the at least two clusters to determine an equipment state for the item of equipment. The output unit is configured to output the equipment state for the item of equipment.

Abstract: An apparatus for equipment monitoring includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with batches of temporal sensor data for an item of equipment. Each batch of temporal sensor data includes temporal sensor values as a function of time. The processing unit is configured to process the batches of temporal sensor data to determine batches of spectral sensor data. Each batch of spectral sensor data includes spectral sensor values as a function of frequency. The processing unit is configured to implement at least one statistical process algorithm to process the spectral sensor values for the batches of spectral sensor data to determine index values. For each batch of spectral sensor data there is an index value determined by each of the statistical process algorithms.