公开(公告)号：US11755605B2

公开(公告)日：2023-09-12

申请号：US17500972

申请日：2021-10-14

Applicant: ABB SCHWEIZ AG

Inventor： Benedikt Schmidt ,  Martin Hollender ,  Sylvia Maczey

IPC: G06F16/00 ,  G06F16/2458 ,  G06F16/23 ,  G06F16/2455 ,  G05B19/418

CPC classification number: G06F16/2477 ,  G05B19/4183 ,  G06F16/2379 ,  G06F16/24556

Abstract: A control module is adapted to control technical equipment by processing batch-run data from the technical equipment. The control module operates according to parameters that are obtained by a parameter module. The module receives a reference plurality of multi-variate reference time series with data values from sources that are related to the equipment. There are time series with measurement values and time series with data that describes particular manufacturing operations during a batch-run time interval. The module splits the time interval into phases by determining transitions between the particular manufacturing operations, and divides the time series into particular phase-specific partial series. For each phase separately, and for the phase-specific partial series in combination, the module differentiates phase-specific time series into relevant partial time series or non-relevant partial time series and set the parameters accordingly.

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：US20210209189A1

公开(公告)日：2021-07-08

申请号：US17207854

申请日：2021-03-22

Applicant: ABB Schweiz AG

Inventor： Moncef Chioua ,  Matthieu Lucke ,  Emanuel Kolb ,  Martin Hollender ,  Nuo Li ,  Andrew Cohen

IPC: G06F17/15 ,  G06K9/62 ,  G06K9/00 ,  G06F17/18

Abstract: A computer-implemented method for determining an abnormal technical status of a technical system includes: receiving, from the technical system, a plurality of signals, each signal being sampled over time and reflecting the technical status of at least one system component; computing, for each signal with associated high and low alarm thresholds obtained from an alarm management system, at every sampling time point, a univariate distance to its associated alarm thresholds as a maximum of the distances between a value of the respective signal and its associated alarm thresholds to quantify a degree of abnormality for the respective at least one system component; computing, at every sampling time point, based on the univariate distances at the respective sampling time points, an aggregate abnormality indicator reflecting the technical status of the technical system; and providing, to an operator, a comparison of the aggregate abnormality indicator with a predetermined abnormality threshold.

公开(公告)号：US20200333773A1

公开(公告)日：2020-10-22

申请号：US16850010

申请日：2020-04-16

Applicant: ABB Schweiz AG

Inventor： Benedikt Schmidt ,  Martin Hollender ,  Felix Lenders

IPC: G05B19/418

Abstract: A computer-implemented method to control technical equipment that performs a production batch-run of a production process, the technical equipment providing data in a form of time-series from a set of data sources, the data sources being related to the technical equipment, includes: accessing a reference time-series with data from a previously performed batch-run of the production process, the reference time-series being related to a parameter for the technical equipment; and while the technical equipment performs the production batch-run: receiving a production time-series with data, identifying a sub-series of the reference time-series, and comparing the received time-series and the sub-series of the reference time-series, to provide an indication of similarity or non-similarity, in case of similarity, controlling the technical equipment during a continuation of the production batch-run, by using the parameter as control parameter.

公开(公告)号：US12181960B2

公开(公告)日：2024-12-31

申请号：US17159177

申请日：2021-01-27

Applicant: ABB Schweiz AG

Inventor： Andrew Cohen ,  Martin Hollender ,  Nuo Li ,  Moncef Chioua ,  Matthieu Lucke

IPC: G06F11/07 ,  G05B23/02 ,  G06F18/2113

Abstract: An apparatus for alarm information determination includes: an input unit; a processing unit; and an output unit. The input unit provides the processing unit with historical process control data, the process control data including a plurality of data signals, a plurality of alarm data, and data relating to an event of interest. The processing unit determines a plurality of correlation scores for the plurality of data signals paired with the plurality of alarm data, a correlation score being determined for a data signal paired with an alarm data, a high correlation score indicating a higher degree of correlation than a low correlation score. The processing unit identifies at least one first alarm data from the plurality of alarm data, the identification including utilization of the data relating to the event of interest.

公开(公告)号：US20240310817A1

公开(公告)日：2024-09-19

申请号：US18212725

申请日：2023-06-22

Applicant: ABB Schweiz AG

Inventor： Martin Hollender ,  Benedikt Schmidt

IPC: G05B19/418

CPC classification number: G05B19/41875 ,  G05B19/4183 ,  G05B2219/13011

Abstract: A method for automatic identification of important batch events for a batch execution alignment algorithm, including receiving historical batch data of a batch process, wherein the historical batch data comprises a plurality of batch executions, and wherein each of the plurality of batch executions comprises a plurality of batch events, indicating a specific event of the batch process, and at least one time series of a process variable, indicating a development of the process variable during the batch process; determining a distance between the at least one time series of the plurality of the batch executions for each of the plurality of batch events; and identifying at least one important batch event for a batch execution alignment algorithm with a smallest distance using the determined distances.

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：US20230023896A1

公开(公告)日：2023-01-26

申请号：US17957592

申请日：2022-09-30

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: G05B19/418 ,  G06N20/00

Abstract: A method of transfer learning for a specific production process of an industrial plant includes providing data templates defining expected data for a production process, and providing plant data, wherein the data templates define groupings for the expected data according to their relation in the industrial plant; determining a process instance and defining a mapping with the plant data; determining historic process data; determining training data using the determined process instance and the determined historic process data, wherein the training data comprises a structured data matrix, wherein columns of the data matrix represent the sensor data that are grouped in accordance with the data template and wherein rows of the data matrix represent timestamps of obtaining the sensor data; providing a pre-trained machine learning model using the determined process instance; and training a new machine learning model using the provided pre-trained model and the determined training data.

公开(公告)号：US20220035810A1

公开(公告)日：2022-02-03

申请号：US17500972

申请日：2021-10-14

Applicant: ABB SCHWEIZ AG

Inventor： Benedikt Schmidt ,  Martin Hollender ,  Sylvia Maczey

IPC: G06F16/2458 ,  G06F16/2455 ,  G06F16/23 ,  G05B19/418

Abstract: A control module is adapted to control technical equipment by processing batch-run data from the technical equipment. The control module operates according to parameters that are obtained by a parameter module. The module receives a reference plurality of multi-variate reference time series with data values from sources that are related to the equipment. There are time series with measurement values and time series with data that describes particular manufacturing operations during a batch-run time interval. The module splits the time interval into phases by determining transitions between the particular manufacturing operations, and divides the time series into particular phase-specific partial series. For each phase separately, and for the phase-specific partial series in combination, the module differentiates phase-specific time series into relevant partial time series or non-relevant partial time series and set the parameters accordingly.

公开(公告)号：US20200012270A1

公开(公告)日：2020-01-09

申请号：US16576832

申请日：2019-09-20

Applicant: ABB Schweiz AG

Inventor： Martin Hollender ,  Benjamin Kloepper ,  Michael Lundh ,  Moncef Chioua

IPC: G05B23/02 ,  G05B13/04

Abstract: An anomaly detection module is configured to apply a plurality of machine learning models to received technical status data to detect one or more indicators of an abnormal technical status prevailing in the industrial process system. The plurality of machine learning models are trained on historic raw or pre-processed sensor data and the anomaly detection module configured to generate the anomaly alert based on the one or more indicators. The received technical status data is assigned to signal groups and the generated anomaly alert is a vector with each vector element representing a group anomaly indicator for the respective signal group. Each vector element is determined by applying a respective group specific machine learning model.