Anomaly forecasting and early warning generation
Abstract
The example embodiments are directed to a system and method for forecasting anomalies in feature detection. In one example, the method includes storing feature behavior information of at least one monitoring node of an asset, including a normalcy boundary identifying normal feature behavior and abnormal feature behavior for the at least one monitoring node in feature space, receiving input signals from the at least one monitoring node of the asset and transforming the input signals into feature values in the feature space, wherein the feature values are located within the normalcy boundary, forecasting that a future feature value corresponding to a future input signal from the at least one monitoring node is going to be positioned outside the normalcy boundary based on the feature values within the normalcy boundary, and outputting information concerning the forecasted future feature value being outside the normalcy boundary for display.
- Inventors:
- Issue Date:
- Research Org.:
- General Electric Co., Schenectady, NY (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1986716
- Patent Number(s):
- 11475124
- Application Number:
- 15/594,779
- Assignee:
- General Electric Company (Schenectady, NY)
- DOE Contract Number:
- OE0000833
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 05/15/2017
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Abbaszadeh, Masoud, and Mestha, Lalit Keshav. Anomaly forecasting and early warning generation. United States: N. p., 2022.
Web.
Abbaszadeh, Masoud, & Mestha, Lalit Keshav. Anomaly forecasting and early warning generation. United States.
Abbaszadeh, Masoud, and Mestha, Lalit Keshav. Tue .
"Anomaly forecasting and early warning generation". United States. https://www.osti.gov/servlets/purl/1986716.
@article{osti_1986716,
title = {Anomaly forecasting and early warning generation},
author = {Abbaszadeh, Masoud and Mestha, Lalit Keshav},
abstractNote = {The example embodiments are directed to a system and method for forecasting anomalies in feature detection. In one example, the method includes storing feature behavior information of at least one monitoring node of an asset, including a normalcy boundary identifying normal feature behavior and abnormal feature behavior for the at least one monitoring node in feature space, receiving input signals from the at least one monitoring node of the asset and transforming the input signals into feature values in the feature space, wherein the feature values are located within the normalcy boundary, forecasting that a future feature value corresponding to a future input signal from the at least one monitoring node is going to be positioned outside the normalcy boundary based on the feature values within the normalcy boundary, and outputting information concerning the forecasted future feature value being outside the normalcy boundary for display.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {10}
}
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