公开(公告)号：US20240303175A1

公开(公告)日：2024-09-12

申请号：US18598034

申请日：2024-03-07

Applicant: ABB Schweiz AG

Inventor： Jan Christoph Schlake ,  Madapu Amarlingam ,  Marie Christin Platenius-Mohr ,  Santonu Sarkar ,  Reuben Borrison

IPC: G06F11/34

CPC classification number: G06F11/3409

Abstract: A method for system monitoring in a hierarchical network of distributed edge devices includes a master edge, first and second client edges connected via a first communication interface to the master edge, the method including receiving sensor data from a sensor device via a second communication interface, determining a first local model parameter representing a machine learning (ML) model of the at least first client edge based on the sensor data; storing the first local model parameter in a data storage of the at least first client edge; collecting the first local model parameter from the at least first client edge; and generating a global ML model based on the at least first local model parameter, wherein the global ML model is used for monitoring a system performance or a condition of the system.

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.