公开(公告)号：US12007745B2

公开(公告)日：2024-06-11

申请号：US17480165

申请日：2021-09-21

Applicant: ABB Schweiz AG

Inventor： Ido Amihai ,  Subanatarajan Subbiah ,  Arzam Muzaffar Kotriwala ,  Moncef Chioua

IPC: G05B23/02 ,  G05B19/4065 ,  G06F18/23213 ,  G06N3/045

CPC classification number: G05B19/4065 ,  G05B23/024 ,  G06F18/23213 ,  G06N3/045 ,  G05B2219/37252

Abstract: An apparatus includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with sensor data for an item of equipment. The processing unit is configured to implement at least one machine learning algorithm, which has been trained on the basis of a plurality of calibration sensor data for the item of equipment. Training of the at least one machine learning algorithm includes processing the plurality of calibration sensor data to determine at least two clusters representative of different equipment states. The processing unit is configured to implement the at least one machine learning algorithm to process the sensor data to assign the sensor data to a cluster of the at least two clusters to determine an equipment state for the item of equipment. The output unit is configured to output the equipment state for the item of equipment.

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：US20230080873A1

公开(公告)日：2023-03-16

申请号：US17993443

申请日：2022-11-23

Applicant: ABB Schweiz AG

Inventor： Dennis Janka ,  Benjamin Kloepper ,  Moncef Chioua ,  Pablo Rodriguez ,  Ioannis Lymperopoulos ,  Marcel Dix

IPC: G06F30/27

Abstract: A model generation system includes input and output units. The input unit receives a plurality of input value trajectories comprising operational input value trajectories and simulation input value trajectories relating to an industrial process. The processing unit implements a simulator of the industrial process and generates behavioral data for at least some of the plurality of input value trajectories. The processing unit further implements a machine learning algorithm that models the industrial process, and trains the machine learning algorithm.

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：US10606251B2

公开(公告)日：2020-03-31

申请号：US15545722

申请日：2016-01-22

Applicant: ABB Schweiz AG

Inventor： Jinendra Gugaliya ,  Naveen Bhutani ,  Nandkishor Kubal ,  Kaushik Ghosh ,  Moncef Chioua

IPC: G05B17/02 ,  G05B19/418 ,  G05B19/042 ,  G05B19/05 ,  G05B23/02

Abstract: The present invention discloses a method for controlling a process in a process plant using a controller. The method comprises receivable associated with the process, determining a first value of at least one key performance indicator associated with the transition from the process data of the first process variable between the first steady state and the second steady state, comparing the determined first value of the at least one key performance indicator against a threshold value of the at least one key performance indicator; and determining a correction factor for modifying a set point of the process variable based on the comparison, for controlling the process.

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：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：US20220236144A2

公开(公告)日：2022-07-28

申请号：US17480163

申请日：2021-09-21

Applicant: ABB Schweiz AG

Inventor： Moncef Chioua ,  Subanatarajan Subbiah ,  Arzam Muzaffar Kotriwala ,  Ido Amihai

IPC: G01M99/00

Abstract: An apparatus for equipment monitoring includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with batches of temporal sensor data for an item of equipment. Each batch of temporal sensor data includes temporal sensor values as a function of time. The processing unit is configured to process the batches of temporal sensor data to determine batches of spectral sensor data. Each batch of spectral sensor data includes spectral sensor values as a function of frequency. The processing unit is configured to implement at least one statistical process algorithm to process the spectral sensor values for the batches of spectral sensor data to determine index values. For each batch of spectral sensor data there is an index value determined by each of the statistical process algorithms.