Patents by Inventor Dennis JANKA

Dennis JANKA has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240168467
    Abstract: A computer-implemented method is provided.
    Type: Application
    Filed: March 12, 2021
    Publication date: May 23, 2024
    Inventors: Arzam Kotriwala, Nuo Li, Jan-Christoph Schlake, Prerna Juhlin, Felix Lenders, Matthias Biskoping, Benjamin Kloepper, Kalpesh Bhalodi, Andreas Potschka, Dennis Janka
  • Publication number: 20240094715
    Abstract: A method of material flow optimization in an industrial process by using an integrated optimizing system is described. The integrated optimizing system includes: a high-level optimizer module describing the material flow by coarse high-level process parameters and including an optimization program for the high-level process parameters, the optimization program being dependent on high-level model parameters and including an objective function subject to constraints; a low-level simulation module for simulating the material flow, the low-level simulation module including a low-level simulation function adapted for obtaining detailed low-level material flow data based on the high-level process parameters; and an aggregator module including an aggregator function adapted for calculating the high-level model parameters based on the low-level material flow data.
    Type: Application
    Filed: January 29, 2021
    Publication date: March 21, 2024
    Inventors: Rickard Lindkvist, Jonas Linder, Kalpesh Bhalodi, Prerna Juhlin, Jan-Christoph Schlake, Dennis Janka, Andreas Potschka
  • Publication number: 20240069518
    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.
    Type: Application
    Filed: December 30, 2020
    Publication date: February 29, 2024
    Inventors: Prerna Juhlin, Arzam Muzaffar Kotriwala, Nuo Li, Jan-Christoph Schlake, Felix Lenders, Matthias Biskoping, Benjamin Kloepper, Kalpesh Bhalodi, Andreas Potschka, Dennis Janka
  • Publication number: 20240069526
    Abstract: A method of industrial processing of a bulk material, the industrial processing including a plurality of process steps, the method including defining a material portion of the bulk material; generating a material portion identifier associated with the material portion processing the material portion in at least two process steps of the plurality of process steps the method including for each process step of the at least two process steps: determining a cost of processing the material portion in the process step; and generating a history data set, wherein the history data set is indicative of the cost, the process step and the material portion identifier and wherein the method further includes determining an aggregated cost based on the history data sets.
    Type: Application
    Filed: December 30, 2020
    Publication date: February 29, 2024
    Inventors: Dennis Janka, Kalpesh Bhalodi, Prerna Juhlin, Andreas Potschka, Jan-Christoph Schlake
  • Patent number: 11880192
    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.
    Type: Grant
    Filed: April 13, 2021
    Date of Patent: January 23, 2024
    Assignee: ABB Schweiz AG
    Inventors: Dennis Janka, Moncef Chioua, Pablo Rodriguez, Mario Hoernicke, Benedikt Schmidt, Benjamin Kloepper
  • Publication number: 20230094914
    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.
    Type: Application
    Filed: September 29, 2022
    Publication date: March 30, 2023
    Applicant: ABB Schweiz AG
    Inventors: Benedikt Schmidt, Ido Amihai, Arzam Muzaffar Kotriwala, Moncef Chioua, Felix Lenders, Dennis Janka, Martin Hollender, Jan Christoph Schlake, Hadil Abukwaik, Benjamin Kloepper
  • Publication number: 20230080873
    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.
    Type: Application
    Filed: November 23, 2022
    Publication date: March 16, 2023
    Applicant: ABB Schweiz AG
    Inventors: Dennis Janka, Benjamin Kloepper, Moncef Chioua, Pablo Rodriguez, Ioannis Lymperopoulos, Marcel Dix
  • Publication number: 20230034769
    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.
    Type: Application
    Filed: October 14, 2022
    Publication date: February 2, 2023
    Applicant: ABB Schweiz AG
    Inventors: Moncef Chioua, Marcel Dix, Benjamin Kloepper, Ioannis Lymperopoulos, Dennis Janka, Pablo Rodriguez
  • Publication number: 20230029400
    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.
    Type: Application
    Filed: September 30, 2022
    Publication date: January 26, 2023
    Applicant: ABB Schweiz AG
    Inventors: Benedikt Schmidt, Ido Amihai, Arzam Muzaffar Kotriwala, Moncef Chioua, Dennis Janka, Felix Lenders, Jan Christoph Schlake, Martin Hollender, Hadil Abukwaik, Benjamin Kloepper
  • Publication number: 20230023896
    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.
    Type: Application
    Filed: September 30, 2022
    Publication date: January 26, 2023
    Applicant: ABB Schweiz AG
    Inventors: Benedikt Schmidt, Ido Amihai, Arzam Muzaffar Kotriwala, Moncef Chioua, Dennis Janka, Felix Lenders, Jan Christoph Schlake, Martin Hollender, Hadil Abukwaik, Benjamin Kloepper
  • Publication number: 20230016668
    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.
    Type: Application
    Filed: September 28, 2022
    Publication date: January 19, 2023
    Applicant: ABB Schweiz AG
    Inventors: Benedikt Schmidt, Ido Amihai, Moncef Chioua, Arzam Kotriwala, Martin Hollender, Dennis Janka, Felix Lenders, Jan Christoph Schlake, Benjamin Kloepper, Hadil Abukwaik
  • Publication number: 20230019404
    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.
    Type: Application
    Filed: September 29, 2022
    Publication date: January 19, 2023
    Applicant: ABB Schweiz AG
    Inventors: Benjamin Kloepper, Benedikt Schmidt, Ido Amihai, Moncef Chioua, Jan Christoph Schlake, Arzam Muzaffar Kotriwala, Martin Hollender, Dennis Janka, Felix Lenders, Hadil Abukwaik
  • Publication number: 20230019201
    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.
    Type: Application
    Filed: September 29, 2022
    Publication date: January 19, 2023
    Applicant: ABB Schweiz AG
    Inventors: Benedikt Schmidt, Ido Amihai, Arzam Muzaffar Kotriwala, Moncef Chioua, Dennis Janka, Felix Lenders, Jan Christoph Schlake, Martin Hollender, Hadil Abukwaik, Benjamin Kloepper
  • Publication number: 20210318671
    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.
    Type: Application
    Filed: April 13, 2021
    Publication date: October 14, 2021
    Inventors: Dennis JANKA, Moncef CHIOUA, Pablo RODRIGUEZ, Mario HOERNICKE, Benedikt SCHMIDT, Benjamin KLOEPPER