System and method for training anomaly detection analytics to automatically remove outlier data
US11703424B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 21, 2020 |
| Grant date | Jul 18, 2023 |
| Priority date | — |
| Expiry date | Sep 13, 2041 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02E10/72
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.