公开(公告)号：US20250053885A1

公开(公告)日：2025-02-13

申请号：US18928402

申请日：2024-10-28

Applicant: ABB Schweiz AG

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

IPC: G06N20/20

Abstract: A method for explanation of machine learning results based on using a model collection includes training at least two machine learning models with at least two competing strategies for the at least one dataset; and using the least two machine learning models to yield at least two different predictions and/or at least two explanations for the at least one dataset.

公开(公告)号：US12166335B2

公开(公告)日：2024-12-10

申请号：US17467295

申请日：2021-09-06

Applicant: ABB Schweiz AG

Inventor： Ralf Gitzel ,  Subanatarajan Subbiah ,  Benedikt Schmidt

IPC: H02B3/00 ,  G01J5/00 ,  G06N3/08 ,  G06N20/00 ,  G06T7/13 ,  G06V10/764 ,  G06V10/774 ,  G06V20/00 ,  G06N3/045

Abstract: An apparatus for monitoring a switchgear includes: an input unit; a processing unit; and an output unit. The input unit provides the processing unit with a monitor infra-red image of a switchgear. The processing unit implements a machine learning classifier algorithm to analyse the monitor infra-red image and determine if there is one or more anomalous hot spots in the switchgear. The machine learning classifier algorithm has been trained based on a plurality of different training infra-red images, the plurality of training infra-red images including a plurality of synthetic infra-red images generated from a corresponding plurality of visible images. The output unit outputs information relating to the one or more anomalous hot spots.

公开(公告)号：US20240302832A1

公开(公告)日：2024-09-12

申请号：US18668370

申请日：2024-05-20

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: G05B23/0254 ,  G05B23/027 ,  G05B23/0286

Abstract: A method for training a prediction model includes obtaining training samples representing states of the process that do not cause the undesired event; obtaining based on a process model and a set of predetermined rules that stipulate states having an increased likelihood of the undesired event occurring; training samples representing states with an increased likelihood to cause the undesired event; providing samples to the to-be-trained prediction model to obtain a prediction of the likelihood for occurrence of the undesired event in a state of the process represented by the respective sample; rating a difference between the prediction and the label of the respective sample using a predetermined loss function; and optimizing parameters such that, when predictions are made, the rating by the loss function improves.

公开(公告)号：US12038742B2

公开(公告)日：2024-07-16

申请号：US17228736

申请日：2021-04-13

Applicant: ABB Schweiz AG

Inventor： Heiko Koziolek ,  Julius Rueckert ,  Benedikt Schmidt ,  Benjamin Kloepper

IPC: G05B19/04 ,  G05B19/418 ,  G06N3/08 ,  G06Q10/0637 ,  H04L41/12 ,  G06F16/901 ,  G06Q10/00

CPC classification number: G05B19/41865 ,  G05B19/4183 ,  G05B19/41885 ,  G06N3/08 ,  G06Q10/0637 ,  H04L41/12 ,  G05B2219/23126 ,  G05B2219/23157 ,  G05B2219/24103 ,  G05B2219/31469 ,  G06F16/9024 ,  G06Q10/00

Abstract: A method for providing an attribute of an element in a processing system having a plurality of elements, the processing system being represented as a directed graph having a plurality of nodes and directed edges, each node representing an element, each node having an attribute, and each directed edge representing a relation between two elements of the plurality of elements, the method including: selecting one node of the plurality of nodes as a starting node; constructing a subgraph, the subgraph including all the nodes that are forward-connected by at least one directed edge from the starting node; and outputting all nodes and the attribute of the nodes of the subgraph.

公开(公告)号：US20230384752A1

公开(公告)日：2023-11-30

申请号：US18448523

申请日：2023-08-11

Applicant: ABB Schweiz AG

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

IPC: G05B19/05

CPC classification number: G05B19/056 ,  G05B2219/1204

Abstract: A method includes acquiring state variables that characterize an operational state of an industrial plant; acquiring interaction events of a plant operator interacting with the distributed control system via a human-machine interface; determining based on the interaction events, and with state variables as input data, whether one or more interaction events are indicative of the plant operator executing a task that is not sufficiently covered by engineering of the distributed control system. When this determination is positive, mapping the input data to an amendment and/or augmentation for the engineering tool that has generated the application code.

公开(公告)号：US20230367297A1

公开(公告)日：2023-11-16

申请号：US18361271

申请日：2023-07-28

Applicant: ABB Schweiz AG

Inventor： Martin Hollender ,  Benedikt Schmidt ,  Ruomu Tan ,  Chaojun Xu ,  Lara-Marie Volkmann

IPC: G05B19/418

CPC classification number: G05B19/4184

Abstract: A method for analysing process data related to a segment of a production process includes providing a process data sequence of the segment of the production process exhibiting a data pattern of at least one process variable to be analyzed; providing a set of metadata; determining process data sequences, which are stored in a first database; determining a start timestamp and end timestamp of each of the determined process data sequence, based on the first database; and calculating a similarity value for each of the determined process data sequences compared to the provided process data sequence, based on the data pattern of the at least one process variable, wherein the determined process data sequences for the calculation are provided, based on the related start timestamps and end timestamps, by accessing a second database comprising the process data sequences, for analysing the process 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：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：US11238371B2

公开(公告)日：2022-02-01

申请号：US16441028

申请日：2019-06-14

Applicant: ABB Schweiz AG

Inventor： Benjamin Kloepper ,  Benedikt Schmidt ,  Mohamed-Zied Ouertani

IPC: G06N20/00 ,  H04W4/38 ,  G05B23/02

Abstract: A computer system can be configured to: receive, in a low-precision mode, first status data generated by one or more sensors, the first status data reflecting technical parameters of a technical system, the first status data exhibiting a first precision level; apply a low-precision machine learning model to analyze the first status data for one or more indicators of an abnormal technical status, the machine learning model having been trained with data exhibiting the first precision level; send, based on an abnormal technical status being indicated, instructions for the one or more sensors to generate second status data exhibiting a second precision level, the second precision level being associated with greater accuracy than the first precision level; receive the second status data exhibiting the second precision level based on the sent instructions; providing the second status data to a data analyzer.

公开(公告)号：US20180336019A1

公开(公告)日：2018-11-22

申请号：US15985127

申请日：2018-05-21

Applicant: ABB Schweiz AG

Inventor： Benedikt Schmidt ,  Jeff Harding ,  Thomas Goldschmidt

IPC: G06F8/36 ,  G06F17/27 ,  G06F17/30

Abstract: A system for reusing program code from a first completed application in a second under-development application based on identified patterns matching between the types of data accessed by the first and second applications. The system has an information model database, a pattern database, an API and applications which query the information model through the API, resulting in stored raw access data. The raw access data is extracted and patterns are generated based on similarity of the abstracted patterns as between the first and second applications. Application programmers access the pattern database to create new programs and implement prior computer code in the new program based on a pattern match on data accessed by a prior-developed application.