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 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 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: 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: 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 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 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: 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 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 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 for monitoring turbines of a windmill farm includes: providing a global nominal dataset containing frame data of the turbines of the windmill farm and continuous reference monitoring data of the turbines for a first period in a fault free state, the reference monitoring data including at least two same monitoring variables for each turbine; building a nominal global model based on the global nominal dataset which describes the relationship in between the windmill turbines and clustering the turbines according thereto; assigning the data of the global nominal dataset to respective nominal local datasets according to the clustering; and building a nominal local model for the turbines of each cluster based on the respective assigned nominal local datasets, the nominal local model being built such that a nonconformity index is providable which indicates a degree of nonconformity between data projected on the local model and the model itself.

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.

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: A computer-implemented method for determining an abnormal technical status of a technical system includes: receiving, from the technical system, a plurality of signals, each signal being sampled over time and reflecting the technical status of at least one system component; computing, for each signal with associated high and low alarm thresholds obtained from an alarm management system, at every sampling time point, a univariate distance to its associated alarm thresholds as a maximum of the distances between a value of the respective signal and its associated alarm thresholds to quantify a degree of abnormality for the respective at least one system component; computing, at every sampling time point, based on the univariate distances at the respective sampling time points, an aggregate abnormality indicator reflecting the technical status of the technical system; and providing, to an operator, a comparison of the aggregate abnormality indicator with a predetermined abnormality threshold.

Abstract: An apparatus for alarm information determination includes: an input unit; a processing unit; and an output unit. The input unit provides the processing unit with historical process control data, the process control data including a plurality of data signals, a plurality of alarm data, and data relating to an event of interest. The processing unit determines a plurality of correlation scores for the plurality of data signals paired with the plurality of alarm data, a correlation score being determined for a data signal paired with an alarm data, a high correlation score indicating a higher degree of correlation than a low correlation score. The processing unit identifies at least one first alarm data from the plurality of alarm data, the identification including utilization of the data relating to the event of interest.

Abstract: An anomaly detection module is configured to apply a plurality of machine learning models to received technical status data to detect one or more indicators of an abnormal technical status prevailing in the industrial process system. The plurality of machine learning models are trained on historic raw or pre-processed sensor data and the anomaly detection module configured to generate the anomaly alert based on the one or more indicators. The received technical status data is assigned to signal groups and the generated anomaly alert is a vector with each vector element representing a group anomaly indicator for the respective signal group. Each vector element is determined by applying a respective group specific machine learning model.

Abstract: An alarm handling system in plant process automation with a data processing device includes: at least one interface for accessing and/or processing one or more process signals and for determining corresponding process variables; an alarm configuration device for accessing and/or providing alarm configuration information including at least one setpoint for one or more determined process variables; and a prediction device for determining and processing a current rate of change of at least one process variable to predict how long it will take and/or a period until and/or predict at which date and/or time a provided setpoint and/or threshold is reached and/or crossed, and/or for determining whether and/or when at least one of the monitored and/or determined process variable values will cross the respective setpoint.

Abstract: The present invention discloses a method for controlling a process in a process plant using a controller. The method comprises receivable associated with the process, determining a first value of at least one key performance indicator associated with the transition from the process data of the first process variable between the first steady state and the second steady state, comparing the determined first value of the at least one key performance indicator against a threshold value of the at least one key performance indicator; and determining a correction factor for modifying a set point of the process variable based on the comparison, for controlling the process.