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

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：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.

公开(公告)号：US20240069518A1

公开(公告)日：2024-02-29

申请号：US18259799

申请日：2020-12-30

Applicant: ABB Schweiz AG

Inventor： Prerna Juhlin ,  Arzam Muzaffar Kotriwala ,  Nuo Li ,  Jan-Christoph Schlake ,  Felix Lenders ,  Matthias Biskoping ,  Benjamin Kloepper ,  Kalpesh Bhalodi ,  Andreas Potschka ,  Dennis Janka

IPC: G05B19/406

CPC classification number: G05B19/406 ,  G05B2219/31449

Abstract: A method for monitoring a continuous industrial process is described. The industrial process includes a number of processing stations for processing material and a material flow between the number of processing stations. Each processing station dynamically provides data representing a state of the processing station. The method includes providing, for each processing station, a processing station layout of the processing station. The method further includes providing, for each processing station, an interface model of the processing station. The method further includes generating an information metamodel from the processing station layout and the interface model of the number of processing stations. The method further includes generating an adaptive simulation model of the industrial process by importing the data representing the state of the processing station provided by the number of processing stations into the adaptive simulation model via the information metamodel.

公开(公告)号：US20240094715A1

公开(公告)日：2024-03-21

申请号：US18263371

申请日：2021-01-29

Applicant: ABB Schweiz AG

Inventor： Rickard Lindkvist ,  Jonas Linder ,  Kalpesh Bhalodi ,  Prerna Juhlin ,  Jan-Christoph Schlake ,  Dennis Janka ,  Andreas Potschka

IPC: G05B19/418

CPC classification number: G05B19/41865 ,  G05B2219/31376

Abstract: A method of material flow optimization in an industrial process by using an integrated optimizing system is described. The integrated optimizing system includes: a high-level optimizer module describing the material flow by coarse high-level process parameters and including an optimization program for the high-level process parameters, the optimization program being dependent on high-level model parameters and including an objective function subject to constraints; a low-level simulation module for simulating the material flow, the low-level simulation module including a low-level simulation function adapted for obtaining detailed low-level material flow data based on the high-level process parameters; and an aggregator module including an aggregator function adapted for calculating the high-level model parameters based on the low-level material flow data. The method includes approaching an optimum value of the objective function by iteratively modifying the high-level process parameters, wherein an iteration includes: carrying out, by the low-level simulation module, a low-level simulation thereby obtaining the detailed low-level material flow data; aggregating, by the aggregator module, the low-level material flow data thereby calculating, from the low-level material flow data, aggregated high-level model parameters; inputting the aggregated high-level model parameters into the optimization program.

公开(公告)号：US20240069526A1

公开(公告)日：2024-02-29

申请号：US18259665

申请日：2020-12-30

Applicant: ABB Schweiz AG

Inventor： Dennis Janka ,  Kalpesh Bhalodi ,  Prerna Juhlin ,  Andreas Potschka ,  Jan-Christoph Schlake

IPC: G05B19/4155

CPC classification number: G05B19/4155 ,  G05B2219/31449

Abstract: A method of industrial processing of a bulk material, the industrial processing including a plurality of process steps, the method including defining a material portion of the bulk material; generating a material portion identifier associated with the material portion processing the material portion in at least two process steps of the plurality of process steps the method including for each process step of the at least two process steps: determining a cost of processing the material portion in the process step; and generating a history data set, wherein the history data set is indicative of the cost, the process step and the material portion identifier and wherein the method further includes determining an aggregated cost based on the history data sets.