Characterizing susceptibility of a machine-learning model to follow signal degradation and evaluating possible mitigation strategies
US11921848B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 2, 2020 |
| Grant date | Mar 5, 2024 |
| Priority date | — |
| Expiry date | Jan 5, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The disclosed embodiments relate to a system that characterizes susceptibility of an inferential model to follow signal degradation. During operation, the system receives a set of time-series signals associated with sensors in a monitored system during normal fault-free operation. Next, the system trains the inferential model using the set of time-series signals. The system then characterizes susceptibility of the inferential model to follow signal degradation. During this process, the system adds degradation to a signal in the set of time-series signals to produce a degraded signal. Next, the system uses the inferential model to perform prognostic-surveillance operations on the set of time-series signals with the degraded signal. Finally, the system characterizes susceptibility of the inferential model to follow degradation in the signal based on results of the prognostic-surveillance operations.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.