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：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：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：US20240302831A1

公开(公告)日：2024-09-12

申请号：US18669696

申请日：2024-05-21

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: G05B23/024 ,  G05B23/0251

Abstract: A method for determining the state of health of an industrial process executed by at least one industrial plant comprising an arrangement of entities, and the state of each such entity, includes obtaining values of the entity state variables; providing the values to a model to obtain a prediction of the state of health; determining propagation paths for anomalies between said entities; determining importances of the states of health of the individual entities for the overall state of health of the process; and aggregating the individual states of health of the entities to obtain the overall state of health of the process.

公开(公告)号：US20240160160A1

公开(公告)日：2024-05-16

申请号：US18455340

申请日：2023-08-24

Applicant: ABB Schweiz AG

Inventor： Ruomu Tan ,  Marco Gaertler ,  Benjamin Kloepper ,  Sylvia Maczey ,  Andreas Potschka ,  Martin Hollender ,  Benedikt Schmidt

IPC: G05B13/02

CPC classification number: G05B13/027

Abstract: A method for detecting change points, CPs, in a signal of a process automation system, includes, in an offline learning phase, unsupervised, candidate CPs on at least one offline signal using unsupervised detection method are detected, CPs are selected from the candidate CPs; the selected CPs are provided to a supervised process; in the supervised process, an offline machine-learning (ML) system is trained to refine CPs from the selected CPs using a supervised machine learning method; a training data set for an online ML system is created using the offline ML system by projecting the refined CPs on the signal; the online ML system is trained in a supervised manner, using the created training data set; and after the offline learning phase, CPs are detected using the trained online ML system.