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：US20230244212A1

公开(公告)日：2023-08-03

申请号：US18297107

申请日：2023-04-07

Applicant: ABB Schweiz AG

Inventor： Heiko Koziolek ,  Andreas Burger ,  Marie Christin Platenius-Mohr ,  Hadil Abukwaik ,  Julius Rueckert

IPC: G05B19/4155

CPC classification number: G05B19/4155 ,  G05B2219/32168

Abstract: A method for visualizing a rule of an industrial process includes providing a topology model of the industrial process, wherein the industrial process comprises at least one sensor and at least one actuator; attributing the topology model with a rule comprising a triple , wherein the cause comprises a range of values from the at least one sensor, the effect comprises an action performed by the at least one actuator, and the traversal comprises a relation between the cause and the effect; marking the cause, the traversal and/or the effect; and visualizing the elements of the rule in the topology model.

公开(公告)号：US20230229144A1

公开(公告)日：2023-07-20

申请号：US18186411

申请日：2023-03-20

Applicant: ABB Schweiz AG

Inventor： Hadil Abukwaik ,  Heiko Koziolek ,  Alejandro Carrasco

IPC: G05B19/418 ,  G06F11/07

CPC classification number: G05B19/4184 ,  G06F11/079 ,  G05B19/4185

Abstract: A safety interlock recommendation system includes at least one process data source, an edge device, wherein the process data source is configured for providing IOS device stream data to the edge device; wherein the edge device comprises an operational technology edge application unit, OT edge application unit, and a stream analysis unit; wherein the OT edge application unit is configured for providing operation technology stream data, OT stream data; wherein the stream analysis unit comprises an online machine learning model, being configured for determining online analysis data using the provided process stream data and the provided OT stream data; wherein the OT edge application unit is configured for determining a short-term recommendation using the online analysis data.

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：US20250110493A1

公开(公告)日：2025-04-03

申请号：US18978207

申请日：2024-12-12

Applicant: ABB Schweiz AG

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

IPC: G05B23/02

Abstract: A method for recommending an operational command includes receiving an alarm from a sensor and/or an operator; obtaining the current state of the plant that includes a current process value and/or operational command; comparing the current state to a list of historic states, each comprising a plurality of historic process values and/or historic operational commands; when the current state matches a subset of at least one of the historic states, starting a simulation and running a plurality of simulations, each based on a variation of at least one of the historic operational commands; determining, for each simulation of the plurality of simulations, a quality value, based on at least one quality criterion; and recommending the variation of the operational command that resulted in the simulation with the highest quality value.

公开(公告)号：US20240005232A1

公开(公告)日：2024-01-04

申请号：US18448535

申请日：2023-08-11

Applicant: ABB Schweiz AG

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

IPC: G06Q10/0631

CPC classification number: G06Q10/063112 ,  G06Q10/06312

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.

公开(公告)号：US11774941B2

公开(公告)日：2023-10-03

申请号：US17725490

申请日：2022-04-20

Applicant: ABB Schweiz AG

Inventor： Hadil Abukwaik ,  Jens Doppelhamer ,  Marcel Dix ,  Benjamin Kloepper ,  Pablo Rodriguez

IPC: G05B19/4065

CPC classification number: G05B19/4065 ,  G05B2219/31057

Abstract: A system and method provides an impact list of affecting equipment elements that affect an industrial sub-process. The method comprises the steps of selecting, in a topology model, the sub-process, wherein the sub-process is an equipment element that is a part of an industrial plant or process, and wherein the topology model is a graph, whose nodes represent equipment elements and whose edges represent interconnections between the equipment elements; traversing the nodes of the topology model, wherein the traversing starts from the selected sub-process and uses a traversing strategy; and for each of the at least one equipment elements, if the equipment element affects the industrial sub-process by an affecting degree greater than a first predefined affecting degree, adding the equipment element to the impact list of affecting equipment elements.

公开(公告)号：US20230074570A1

公开(公告)日：2023-03-09

申请号：US17899856

申请日：2022-08-31

Applicant: ABB Schweiz AG

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

IPC: G05B23/02

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.

公开(公告)号：US20230029400A1

公开(公告)日：2023-01-26

申请号：US17957609

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: G06N20/00

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.

公开(公告)号：US20230019404A1

公开(公告)日：2023-01-19

申请号：US17956117

申请日：2022-09-29

Applicant: ABB Schweiz AG

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

IPC: G06N20/20

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.