Using a double-blind challenge to evaluate machine-learning-based prognostic-surveillance techniques
US12038830B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 5, 2020 |
| Grant date | Jul 16, 2024 |
| Priority date | — |
| Expiry date | May 18, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2221/2107
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A double-blind comparison is performed between prognostic-surveillance systems, which are located on a local system and a remote system. During operation, the local system inserts random faults into a dataset to produce a locally seeded dataset, wherein the random faults are inserted into random signals at random times with variable fault signatures. Next, the local system exchanges the locally seeded dataset with a remote system, and in return receives a remotely seeded dataset, which was produced by the remote system by inserting different random faults into the same dataset. Next, the local system uses a local prognostic-surveillance system to analyze the remotely seeded dataset to produce locally detected faults. Finally, the local system determines a performance of the local prognostic-surveillance system by comparing the locally detected faults against actual faults in the remotely seeded fault information. The remote system similarly determines a performance of a remote prognostic-surveillance system.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.