Context encoder-based fiber sensing anomaly detection
US11733089B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 20, 2021 |
| Grant date | Aug 22, 2023 |
| Priority date | — |
| Expiry date | Dec 20, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04B10/071
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
Aspects of the present disclosure describe an unsupervised context encoder-based fiber sensing method that detects anomalous vibrations proximate to a sensor fiber that is part of a distributed fiber optic sensing system (DFOS) such that damage to the sensor fiber by activities producing and anomalous vibrations are preventable. Advantageously, our method requires only normal data streams and a machine learning based operation is utilized to analyze the sensing data and report abnormal events related to construction or other fiber-threatening activities in real-time. Our machine learning algorithm is based on waterfall image inpainting by context encoder and is self-trained in an end-to-end manner and extended every time the DFOS sensor fiber is optically connected to a new route. Accordingly, our inventive method and system it is much easier to deploy as compared to supervised methods of the prior art.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.