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：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：US20230214724A1

公开(公告)日：2023-07-06

申请号：US18184279

申请日：2023-03-15

Applicant: ABB Schweiz AG

Inventor： Arzam Kotriwala ,  Andreas Potschka ,  Benjamin Kloepper ,  Marcel Dix

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A method and system for removing undesirable inferences from a machine learning model include a search component configured to receive a rejected explanation of model output provided by the machine learning model, identify data samples to unlearn by selecting training samples from training data that were used to train the machine learning model, the selected training samples being associated with explanations that are similar to the rejected explanation according to a calculated similarity measure, and pass the data samples to unlearn to a machine unlearning unit.

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：US20220342382A1

公开(公告)日：2022-10-27

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

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：US20220181856A1

公开(公告)日：2022-06-09

申请号：US17543193

申请日：2021-12-06

Applicant: ABB Schweiz AG

Inventor： Ralf Gitzel ,  Ido Amihai ,  Aydin Boyaci ,  Marcel Dix ,  Joerg Gebhardt

IPC: H02B13/065 ,  G01J5/00 ,  H04N5/33

Abstract: The invention relates to a medium voltage switchgear or control gear monitoring system (10), comprising: an infrared camera (20); and a processing unit (30); wherein the infrared camera is configured to be mounted within a medium voltage switchgear or control gear (40); wherein the infrared camera is configured to acquire an infrared image, wherein the infrared image comprises image data of two or three current carrying parts of the switchgear or control gear (50), and wherein the two or three current carrying parts are the same current carry part of two or three equivalent systems within the switchgear or control gear; wherein the infrared camera is configured to provide the infrared image to the processing unit; wherein the processing unit is configured to determine that the two or three current carrying parts are operating correctly or that one of the two or three current carrying parts has a fault, wherein the determination comprises analysis of the infrared image by an autoencoder implemented by the processing unit.

公开(公告)号：US20210011968A1

公开(公告)日：2021-01-14

申请号：US16923110

申请日：2020-07-08

Applicant: ABB Schweiz AG

Inventor： Andreas Burger ,  Heiko Koziolek ,  Sten Gruener ,  Johannes Schmitt ,  Marcel Dix

IPC: G06F16/9538 ,  G06F16/951

Abstract: An industrial information identification and retrieval system includes: a crawler; a search engine; a result processor; and a web user interface “web UL” The crawler identifies devices and their associated Open Platform Communication Unified Architecture “OPC UA” servers within a network as identified OPC UA servers. The crawler browses the identified OPC UA servers and extracts and stores data items in a database as extracted data items. The search engine searches the extracted data items and provides search results to the result processor. The result processor determines a score for each search result. The web UI presents the search results according to the scores.

公开(公告)号：US20190311273A1

公开(公告)日：2019-10-10

申请号：US16443914

申请日：2019-06-18

Applicant: ABB Schweiz AG

Inventor： Andrew Cohen ,  Marcel Dix

IPC: G06N5/02 ,  G06N7/00 ,  G05B19/4155

Abstract: Disclosed is a computer-implemented method for generating a prediction model. The model can be for use in processing machine event data to predict behavior of a plurality of industrial machines under supervision. The prediction model can be configured to determine current and future states of the industrial machines. The method can include: extracting event features from event codes and structuring the event features into feature vectors; and generating the prediction model by clustering the feature vectors into a plurality of vector clusters, the vector clusters being assigned to respective machine states. The prediction model can be constructed based on event data from a first industrial machine and be applied to control an operating state of a second industrial machine.