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：US20220343193A1

公开(公告)日：2022-10-27

申请号：US17724693

申请日：2022-04-20

Applicant: ABB Schweiz AG

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

IPC: G06N5/04

Abstract: A decision support system and method for an industrial plant is configured and operates to: obtain a causal graph modeling causal assumptions relating to conditional dependence between variables in the industrial plant; obtain observational data relating to operation of the industrial plant; and perform causal inference using the causal graph and the observational data to estimate at least one causal effect relevant for making decisions when operating the industrial plant.

公开(公告)号：US20210129324A1

公开(公告)日：2021-05-06

申请号：US17146499

申请日：2021-01-12

Applicant: ABB Schweiz AG

Inventor： Mithun Acharya ,  Boris Fiedler ,  Benjamin Kloepper ,  Karl Severin

IPC: B25J9/16

Abstract: A method for problem diagnosis in a robot system having one or more robots includes the steps of: a) receiving (S1) a first problem message from a robot of the robot system, the first problem message including one or more data elements descriptive of a problem experienced by the robot; b) receiving (S1) a subsequent problem message from a robot of the robot system; c) if a time elapsed between receipt of the subsequent problem message and receipt of an immediately preceding problem message is shorter than a predetermined threshold (S2), adding the subsequent problem message to a message set which comprises the immediately preceding problem message (S3); and d) if the time elapsed is longer than the predetermined threshold (S2), terminating (S4) the message set of the immediately preceding problem message without adding the subsequent problem message, and establishing (S5, S6) a new message set.

公开(公告)号：US20210011459A1

公开(公告)日：2021-01-14

申请号：US16921957

申请日：2020-07-07

Applicant: ABB Schweiz AG

Inventor： Subanatarajan Subbiah ,  Benjamin Kloepper

IPC: G05B19/4155 ,  G06N20/00

Abstract: A method for controlling an industrial process includes: determining, by a process controller, based at least in part on a set of current values and/or past values of state variables of the industrial process, a set of control outputs to be applied to at least one actor and/or lower-level controller configured to cause a performing of at least one physical action on the process; querying, based on at least a subset of the set of current values and/or past values of state variables and on at least a subset of the set of control outputs, a trained machine-learning model configured to output a classification value, and/or a regression value, that is indicative of a propensity of a watching human operator to at least partially override the control outputs delivered by the process controller; and determining that the classification value, the regression value, and/or the propensity, meets a predetermined criterion.

公开(公告)号：US20190073609A1

公开(公告)日：2019-03-07

申请号：US16178629

申请日：2018-11-02

Applicant: ABB Schweiz AG

Inventor： Martin Hollender ,  Benjamin Kloepper

IPC: G06N99/00 ,  G05B19/418

CPC classification number: G06N20/00 ,  G05B19/41885 ,  G05B23/0232 ,  G05B2219/34457

Abstract: An alarm handling system in plant process automation with a data processing device includes: at least one interface for accessing and/or processing one or more process signals and for determining corresponding process variables; an alarm configuration device for accessing and/or providing alarm configuration information including at least one setpoint for one or more determined process variables; and a prediction device for determining and processing a current rate of change of at least one process variable to predict how long it will take and/or a period until and/or predict at which date and/or time a provided setpoint and/or threshold is reached and/or crossed, and/or for determining whether and/or when at least one of the monitored and/or determined process variable values will cross the respective setpoint.

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：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：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