公开(公告)号：US20220003637A1

公开(公告)日：2022-01-06

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.

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.

公开(公告)号：US11880192B2

公开(公告)日：2024-01-23

申请号：US17228730

申请日：2021-04-13

Applicant: ABB Schweiz AG

Inventor： Dennis Janka ,  Moncef Chioua ,  Pablo Rodriguez ,  Mario Hoernicke ,  Benedikt Schmidt ,  Benjamin Kloepper

IPC: G05B19/418 ,  G06F30/18 ,  H04L41/12 ,  H04L41/14

CPC classification number: G05B19/41865 ,  G05B19/4183 ,  G05B19/4185 ,  G05B19/41885 ,  G06F30/18 ,  H04L41/12 ,  H04L41/145

Abstract: A method for determining an interdependency between a plurality of elements in an industrial processing system includes: providing a process flow diagram (PFD) of a topology of the processing system; transforming the PFD into a directed graph, each element of the plurality of elements being transformed into a node and each relation between the plurality of elements being transformed into a directed edge; selecting one node of the plurality of nodes as a starting node; and constructing a subgraph, the subgraph including all the nodes that are forward-connected from the starting node so as to show at least one interdependency between the plurality of elements in the subgraph.

公开(公告)号：US20230029400A1

公开(公告)日：2023-01-26

申请号：US17957609

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: G06N20/00

Abstract: A method of hierarchical machine learning includes receiving a topology model having information on hierarchical relations between components of the industrial plant, determining a representation hierarchy comprising a plurality of levels, wherein each representation on a higher level represents a group of representations on a lower level, wherein the representations comprise a machine learning model, and training an output machine learning model using the determined hierarchical representations.

公开(公告)号：US20230019404A1

公开(公告)日：2023-01-19

申请号：US17956117

申请日：2022-09-29

Applicant: ABB Schweiz AG

Inventor： Benjamin Kloepper ,  Benedikt Schmidt ,  Ido Amihai ,  Moncef Chioua ,  Jan Christoph Schlake ,  Arzam Muzaffar Kotriwala ,  Martin Hollender ,  Dennis Janka ,  Felix Lenders ,  Hadil Abukwaik

IPC: G06N20/20

Abstract: A computer-implemented method for automating the development of industrial machine learning applications includes one or more sub-methods that, depending on the industrial machine learning problem, may be executed iteratively. These sub-methods include at least one of a method to automate the data cleaning in training and later application of machine learning models, a method to label time series (in particular signal data) with help of other timestamp records, feature engineering with the help of process mining, and automated hyper-parameter tuning for data segmentation and classification.

公开(公告)号：US20210149385A1

公开(公告)日：2021-05-20

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: G05B23/02 ,  G06F16/245 ,  G08B29/02

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：US10824963B2

公开(公告)日：2020-11-03

申请号：US16178629

申请日：2018-11-02

Applicant: ABB Schweiz AG

Inventor： Martin Hollender ,  Benjamin Kloepper ,  Moncef Chioua

IPC: G08B21/00 ,  G06N20/00 ,  G05B23/02 ,  G05B19/418

Abstract: An alarm handling system in plant process automation with a data processing device includes: at least one interface for accessing and/or processing one or more process signals and for determining corresponding process variables; an alarm configuration device for accessing and/or providing alarm configuration information including at least one setpoint for one or more determined process variables; and a prediction device for determining and processing a current rate of change of at least one process variable to predict how long it will take and/or a period until and/or predict at which date and/or time a provided setpoint and/or threshold is reached and/or crossed, and/or for determining whether and/or when at least one of the monitored and/or determined process variable values will cross the respective setpoint.

公开(公告)号：US12019432B2

公开(公告)日：2024-06-25

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: G05B23/02 ,  G06F17/15 ,  G06F17/18 ,  G06F18/22

CPC classification number: G05B23/0213 ,  G06F17/15 ,  G06F17/18 ,  G06F18/22 ,  G06F2218/12

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：US11835429B2

公开(公告)日：2023-12-05

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: G06F11/30 ,  G01M99/00

CPC classification number: G01M99/005

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.

公开(公告)号：US20230094914A1

公开(公告)日：2023-03-30

申请号：US17956097

申请日：2022-09-29

Applicant: ABB Schweiz AG

Inventor： Benedikt Schmidt ,  Ido Amihai ,  Arzam Muzaffar Kotriwala ,  Moncef Chioua ,  Felix Lenders ,  Dennis Janka ,  Martin Hollender ,  Jan Christoph Schlake ,  Hadil Abukwaik ,  Benjamin Kloepper

IPC: G06N20/00

Abstract: A computer-implemented method of generating a training data set for training an artificial intelligence module includes providing first and second data sets, the first data set including first data elements indicative of a first operational condition, the second data set including second data elements indicative of a second operational condition that matches the first operational condition. The method further comprises determining a data transformation for transforming the first data elements into the second data elements; applying the data transformation to the first data elements and/or to further data elements of further data sets, thereby generating a transformed data set; and generating a training data set for training the AI module based on at least a part of the transformed data set.