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：US20220019209A1

公开(公告)日：2022-01-20

申请号：US17489882

申请日：2021-09-30

Applicant: ABB Schweiz AG

Inventor： Benjamin Kloepper ,  Jan-Christoph Schlake ,  Benedikt Schmidt ,  Bernhard Wullt ,  Anton Ronquist

IPC: G05B23/02

Abstract: An asset condition monitoring method with automatic anomaly detection may include receiving local condition data from an asset fleet, identifying at least one anomaly in the received condition data, identifying a new potential failure case dependent on the identified anomaly, determining a specific condition model dependent on the identified new potential failure case, where the specific condition model is configured for predicting the new potential failure case, and providing the specific condition model to the plurality of assets and/or to digital models of the plurality of assets.

公开(公告)号：US20210260754A1

公开(公告)日：2021-08-26

申请号：US17317920

申请日：2021-05-12

Applicant: ABB Schweiz AG

Inventor： Pablo Rodriguez ,  Benjamin Kloepper ,  Arzam Muzaffar Kotriwala ,  Marcel Dix ,  Debora Clever ,  Fan Dai

IPC: B25J9/16 ,  G05B13/02 ,  G05B13/04

Abstract: A method for applying machine learning to an application includes: a) generating a set of candidate parameters by a learner; b) executing a program in at least one simulated application based on the set of candidate parameters and providing interim results of tested sets of candidate parameters based on a measured performance information of the execution of the program; c) collecting a predetermined number of interim results and providing an end result based on a combination of the candidate parameters and the measured performance information by a trainer; and d) generating a new set of candidate parameters by the learner based on the end result for execution by the unchanged program.

公开(公告)号：US20190294998A1

公开(公告)日：2019-09-26

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

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：US10331119B2

公开(公告)日：2019-06-25

申请号：US15665365

申请日：2017-07-31

Applicant: ABB Schweiz AG

Inventor： Mithun P. Acharya ,  Benjamin Kloepper ,  Jeffrey Harding ,  Thomas Goldschmidt

IPC: G05B19/418 ,  G05B23/02 ,  G07C3/14 ,  G06F11/34 ,  G06K19/067 ,  B60R25/04

Abstract: A system and method for monitoring operating conditions of an industrial installation system including a plurality of pieces of equipment. Each of the pieces of equipment includes a sensor and an electrically identifiable tag configured to identify the equipment. The sensors of each of the plurality of pieces of equipment provide an operating characteristic of the piece of equipment that is provided to an industrial equipment management system. The system is also configured to store the content of the electrically identifiable tag and to store a location identifier of each of plurality of pieces of equipment. Replacement of the identified defective equipment is made with replacement equipment having an identifier that uniquely identifies the replacement device and the location of the replacement device in the industrial installation system.

公开(公告)号：US20190033844A1

公开(公告)日：2019-01-31

申请号：US15665365

申请日：2017-07-31

Applicant: ABB Schweiz AG

Inventor： Mithun P. Acharya ,  Benjamin Kloepper ,  Jeffrey Harding ,  Thomas Goldschmidt

IPC: G05B23/02 ,  G07C3/14 ,  G06F11/34

CPC classification number: G05B23/0289 ,  B60R25/04 ,  G06F11/3495 ,  G06K19/0672 ,  G06Q10/20 ,  G07C3/00 ,  G07C3/143

Abstract: A system and method for monitoring operating conditions of an industrial installation system including a plurality of pieces of equipment. Each of the pieces of equipment includes a sensor and an electrically identifiable tag configured to identify the equipment. The sensors of each of the plurality of pieces of equipment provide an operating characteristic of the piece of equipment that is provided to an industrial equipment management system. The system is also configured to store the content of the electrically identifiable tag and to store a location identifier of each of plurality of pieces of equipment. Replacement of the identified defective equipment is made with replacement equipment having an identifier that uniquely identifies the replacement device and the location of the replacement device in the industrial installation system.

公开(公告)号：US20250004464A1

公开(公告)日：2025-01-02

申请号：US18756100

申请日：2024-06-27

Applicant: ABB Schweiz AG

Inventor： Santonu Sarkar ,  Hadil Abukwaik ,  Reuben Borrison ,  Divyasheel Sharma ,  Marcel Dix ,  Chandrika K R ,  Deepti Maduskar ,  Marie Christin Platenius-Mohr ,  Benjamin Kloepper

IPC: G05B23/02 ,  G05B19/409

Abstract: There is provided an explainer system for explaining an alarm raised by a machine learned model of an industrial automation system. The explainer system is configured to: receive model output from the machine learned model trained to predict anomalous behaviour in the industrial automation system and to raise the alarm; process the model output using at least one prediction explanation technique to identify at least one influential feature which contributed to the model output; use the identified at least one influential feature to extract contextual information from at least one machine-readable information source pertaining to the industrial automation system; and prepare the extracted contextual information for display to an operator of the industrial automation system, to enable the operator to select an appropriate action to take in response to the alarm for ensuring proper functioning of the industrial automation system.

公开(公告)号：US20240126222A1

公开(公告)日：2024-04-18

申请号：US18394328

申请日：2023-12-22

Applicant: ABB Schweiz AG

Inventor： Benjamin Kloepper ,  Rikard Hansson ,  Helge Didriksen ,  Elise Thorud

IPC: G05B13/04

CPC classification number: G05B13/042 ,  G05B13/041 ,  G05B13/048

Abstract: A method for predicting based on the state of an industrial process at a first point in time that is described by a process snapshot record with values of a first set of variables a value of at least one process variable of the industrial process at a second, later point in time, includes mapping using a machine learning model the process snapshot record to at least one initial state record; providing the initial state record to a simulation model; simulating using the simulation model the further development of the process; obtaining from the simulation model a final state record; and determining based on the final state record the sought value of the process variable at the second point in time.

公开(公告)号：US20240019849A1

公开(公告)日：2024-01-18

申请号：US18475681

申请日：2023-09-27

Applicant: ABB Schweiz AG

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

IPC: G05B19/418

CPC classification number: G05B19/4184 ,  G05B2219/34465

Abstract: An assistance system comprises a plant topology repository comprising a representation of the components of the plant and relations between the components; a monitoring subsystem configured for monitoring signals from the components and for monitoring a related event, as a key for the monitored signals; an aggregation subsystem configured for storing a plurality of the monitored signals and the related events, wherein at least one of the events is the abnormal situation; an identification subsystem configured for comparing currently monitored signals to stored monitored signals and the related event; and an evaluation subsystem configured for outputting a predefined action, if the currently monitored signals match to the event that is the abnormal situation.