公开(公告)号：US20230080873A1

公开(公告)日：2023-03-16

申请号：US17993443

申请日：2022-11-23

Applicant: ABB Schweiz AG

Inventor： Dennis Janka ,  Benjamin Kloepper ,  Moncef Chioua ,  Pablo Rodriguez ,  Ioannis Lymperopoulos ,  Marcel Dix

IPC: G06F30/27

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.

公开(公告)号：US20230019201A1

公开(公告)日：2023-01-19

申请号：US17956076

申请日：2022-09-29

Applicant: ABB Schweiz AG

Inventor： Benedikt Schmidt ,  Ido Amihai ,  Arzam Muzaffar Kotriwala ,  Moncef Chioua ,  Dennis Janka ,  Felix Lenders ,  Jan Christoph Schlake ,  Martin Hollender ,  Hadil Abukwaik ,  Benjamin Kloepper

IPC: G05B13/02

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.

公开(公告)号：US20240168467A1

公开(公告)日：2024-05-23

申请号：US18549428

申请日：2021-03-12

Applicant: ABB Schweiz AG

Inventor： Arzam Kotriwala ,  Nuo Li ,  Jan-Christoph Schlake ,  Prerna Juhlin ,  Felix Lenders ,  Matthias Biskoping ,  Benjamin Kloepper ,  Kalpesh Bhalodi ,  Andreas Potschka ,  Dennis Janka

IPC: G05B19/418

CPC classification number: G05B19/41875 ,  G05B2219/32368

Abstract: A computer-implemented method is provided. The method includes receiving geological data of a material and processing data referring to a plurality of processing stations of an industrial process for manufacturing a product from the material; receiving, for the geological data and the processing data, corresponding product quality data of the manufactured product; and training or retraining a prediction model for the industrial process to determine predicted product quality data for the geological data and the processing data

公开(公告)号：US20230094914A1

公开(公告)日：2023-03-30

申请号：US17956097

申请日：2022-09-29

Applicant: ABB Schweiz AG

Inventor： Benedikt Schmidt ,  Ido Amihai ,  Arzam Muzaffar Kotriwala ,  Moncef Chioua ,  Felix Lenders ,  Dennis Janka ,  Martin Hollender ,  Jan Christoph Schlake ,  Hadil Abukwaik ,  Benjamin Kloepper

IPC: G06N20/00

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.

公开(公告)号：US20240069518A1

公开(公告)日：2024-02-29

申请号：US18259799

申请日：2020-12-30

Applicant: ABB Schweiz AG

Inventor： Prerna Juhlin ,  Arzam Muzaffar Kotriwala ,  Nuo Li ,  Jan-Christoph Schlake ,  Felix Lenders ,  Matthias Biskoping ,  Benjamin Kloepper ,  Kalpesh Bhalodi ,  Andreas Potschka ,  Dennis Janka

IPC: G05B19/406

CPC classification number: G05B19/406 ,  G05B2219/31449

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.

公开(公告)号：US20230023896A1

公开(公告)日：2023-01-26

申请号：US17957592

申请日：2022-09-30

Applicant: ABB Schweiz AG

Inventor： Benedikt Schmidt ,  Ido Amihai ,  Arzam Muzaffar Kotriwala ,  Moncef Chioua ,  Dennis Janka ,  Felix Lenders ,  Jan Christoph Schlake ,  Martin Hollender ,  Hadil Abukwaik ,  Benjamin Kloepper

IPC: G05B19/418 ,  G06N20/00

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.

公开(公告)号：US20230034769A1

公开(公告)日：2023-02-02

申请号：US17966012

申请日：2022-10-14

Applicant: ABB Schweiz AG

Inventor： Moncef Chioua ,  Marcel Dix ,  Benjamin Kloepper ,  Ioannis Lymperopoulos ,  Dennis Janka ,  Pablo Rodriguez

IPC: G05B13/02 ,  G05B23/02

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.

公开(公告)号：US20230016668A1

公开(公告)日：2023-01-19

申请号：US17954485

申请日：2022-09-28

Applicant: ABB Schweiz AG

Inventor： Benedikt Schmidt ,  Ido Amihai ,  Moncef Chioua ,  Arzam Kotriwala ,  Martin Hollender ,  Dennis Janka ,  Felix Lenders ,  Jan Christoph Schlake ,  Benjamin Kloepper ,  Hadil Abukwaik

IPC: G05B13/02 ,  G06N3/08

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.

公开(公告)号：US20240094715A1

公开(公告)日：2024-03-21

申请号：US18263371

申请日：2021-01-29

Applicant: ABB Schweiz AG

Inventor： Rickard Lindkvist ,  Jonas Linder ,  Kalpesh Bhalodi ,  Prerna Juhlin ,  Jan-Christoph Schlake ,  Dennis Janka ,  Andreas Potschka

IPC: G05B19/418

CPC classification number: G05B19/41865 ,  G05B2219/31376

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. The method includes approaching an optimum value of the objective function by iteratively modifying the high-level process parameters, wherein an iteration includes: carrying out, by the low-level simulation module, a low-level simulation thereby obtaining the detailed low-level material flow data; aggregating, by the aggregator module, the low-level material flow data thereby calculating, from the low-level material flow data, aggregated high-level model parameters; inputting the aggregated high-level model parameters into the optimization program.

公开(公告)号：US20240069526A1

公开(公告)日：2024-02-29

申请号：US18259665

申请日：2020-12-30

Applicant: ABB Schweiz AG

Inventor： Dennis Janka ,  Kalpesh Bhalodi ,  Prerna Juhlin ,  Andreas Potschka ,  Jan-Christoph Schlake

IPC: G05B19/4155

CPC classification number: G05B19/4155 ,  G05B2219/31449

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.