公开(公告)号：US20210364392A1

公开(公告)日：2021-11-25

申请号：US16879828

申请日：2020-05-21

Applicant: General Electric Company

Inventor： Dayu Huang ,  Frederick Wilson Wheeler ,  John Joseph Mihok ,  David C. Korim

IPC: G01M99/00 ,  G06N20/00 ,  G06N5/04

Abstract: A method for detecting anomalies during operation of an asset to improve performance of the asset includes collecting, via a server, data relating to operation of the asset or a group of assets containing the asset. The data includes normal and abnormal asset behavior of the asset or the group of assets containing the asset. Further, the method includes automatically removing, via an iterative algorithm programmed in the server that utilizes one or more inputs or outputs of an anomaly detection analytic, portions of the data containing the abnormal asset behavior to form a dataset containing only the normal asset behavior. The method also includes training, via a computer-based model programmed in the server, the anomaly detection analytic using, at least, the dataset containing only the normal asset behavior. Moreover, the method includes applying, via the server, the anomaly detection analytic to the asset so as to monitor for anomalies during operation thereof.

公开(公告)号：US11703424B2

公开(公告)日：2023-07-18

申请号：US16879828

申请日：2020-05-21

Applicant: General Electric Company

Inventor： Dayu Huang ,  Frederick Wilson Wheeler ,  John Joseph Mihok ,  David C. Korim

IPC: G01M99/00 ,  G06N20/00 ,  G06N5/04 ,  G06F11/07 ,  G06F17/10 ,  H04L9/40

CPC classification number: G01M99/005 ,  G06F11/0748 ,  G06F17/10 ,  G06N5/04 ,  G06N20/00 ,  H04L63/1425

Abstract: A method for detecting anomalies during operation of an asset to improve performance of the asset includes collecting, via a server, data relating to operation of the asset or a group of assets containing the asset. The data includes normal and abnormal asset behavior of the asset or the group of assets containing the asset. Further, the method includes automatically removing, via an iterative algorithm programmed in the server that utilizes one or more inputs or outputs of an anomaly detection analytic, portions of the data containing the abnormal asset behavior to form a dataset containing only the normal asset behavior. The method also includes training, via a computer-based model programmed in the server, the anomaly detection analytic using, at least, the dataset containing only the normal asset behavior. Moreover, the method includes applying, via the server, the anomaly detection analytic to the asset so as to monitor for anomalies during operation thereof.