公开(公告)号：US20230221684A1

公开(公告)日：2023-07-13

申请号：US18184043

申请日：2023-03-15

Applicant: ABB Schweiz AG

Inventor： Benjamin Kloepper ,  Arzam Muzaffar Kotriwala ,  Marcel Dix

IPC: G05B13/02 ,  G05B13/04

CPC classification number: G05B13/0265 ,  G05B13/045

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.

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：US20230050321A1

公开(公告)日：2023-02-16

申请号：US17977355

申请日：2022-10-31

Applicant: ABB Schweiz AG

Inventor： Benedikt Schmidt ,  Marcel Dix ,  Martin Hollender ,  Andrew Cohen ,  Arzam Muzaffar Kotriwala ,  Marco Gaertler ,  Sylvia Maczey ,  Benjamin Kloepper

IPC: G05B19/042

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.

公开(公告)号：US20220004163A1

公开(公告)日：2022-01-06

申请号：US17480165

申请日：2021-09-21

Applicant: ABB Schweiz AG

Inventor： Ido Amihai ,  Subanatarajan Subbiah ,  Arzam Muzaffar Kotriwala ,  Moncef Chioua

IPC: G05B19/4065 ,  G05B23/02 ,  G06N3/04 ,  G06K9/62

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.

公开(公告)号：US20250086514A1

公开(公告)日：2025-03-13

申请号：US18929807

申请日：2024-10-29

Applicant: ABB Schweiz AG

Inventor： Benjamin Kloepper ,  Dawid Ziobro ,  Divyasheel Sharma ,  Benedikt Schmidt ,  Yemao Man ,  Gayathri Gopalakrishnan ,  Joakim Astrom ,  Marcel Dix ,  Arzam Muzaffar Kotriwala

IPC: G06N20/00

Abstract: A method for deciding on a machine learning model result quality based on the identification of distractive samples in the training data includes providing a first result of the model based on initial training data; determining a first performance of the first result of the model; logging input data; providing a second result of the model based on initial training data and the input data, determining a second performance of the second result of the model and thereon based identifying erroneous data within the input data and/or the training data.

公开(公告)号：US20240302831A1

公开(公告)日：2024-09-12

申请号：US18669696

申请日：2024-05-21

Applicant: ABB Schweiz AG

Inventor： Hadil Abukwaik ,  Divyasheel Sharma ,  Benjamin Kloepper ,  Arzam Muzaffar Kotriwala ,  Pablo Rodriguez ,  Benedikt Schmidt ,  Ruomu Tan ,  Chandrika K R ,  Reuben Borrison ,  Marcel Dix ,  Jens Doppelhamer

IPC: G05B23/02

CPC classification number: G05B23/024 ,  G05B23/0251

Abstract: A method for determining the state of health of an industrial process executed by at least one industrial plant comprising an arrangement of entities, and the state of each such entity, includes obtaining values of the entity state variables; providing the values to a model to obtain a prediction of the state of health; determining propagation paths for anomalies between said entities; determining importances of the states of health of the individual entities for the overall state of health of the process; and aggregating the individual states of health of the entities to obtain the overall state of health of the process.

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：US20230393538A1

公开(公告)日：2023-12-07

申请号：US18452313

申请日：2023-08-18

Applicant: ABB Schweiz AG

Inventor： Dawid Ziobro ,  Jens Doppelhamer ,  Benedikt Schmidt ,  Simon Hallstadius Linge ,  Gayathri Gopalakrishnan ,  Pablo Rodriguez ,  Benjamin Kloepper ,  Reuben Borrison ,  Marcel Dix ,  Hadil Abukwaik ,  Arzam Muzaffar Kotriwala ,  Sylvia Maczey ,  Marco Gaertler ,  Divyasheel Sharma ,  Chandrika K R ,  Matthias Berning

IPC: G05B13/02 ,  G05B23/02

CPC classification number: G05B13/0265 ,  G05B23/0216 ,  G05B2223/02

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.

公开(公告)号：US20230274189A1

公开(公告)日：2023-08-31

申请号：US18171732

申请日：2023-02-21

Applicant: ABB Schweiz AG

Inventor： Arzam Muzaffar Kotriwala ,  Benjamin Kloepper ,  Ido Amihai ,  Taisuke Minagawa ,  Dominik Olschewski ,  Kai Merz

IPC: G06N20/00

CPC classification number: G06N20/00

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.

公开(公告)号：US20230237284A1

公开(公告)日：2023-07-27

申请号：US18193809

申请日：2023-03-31

Applicant: ABB Schweiz AG

Inventor： Benedikt Schmidt ,  Marco Gaertler ,  Sylvia Maczey ,  Pablo Rodriguez ,  Benjamin Kloepper ,  Arzam Muzaffar Kotriwala ,  Nuo Li

IPC: G06F40/58 ,  G06F40/30 ,  H04L67/50

CPC classification number: G06F40/58 ,  G06F40/30 ,  H04L67/535

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.