Staggered-sampling technique for detecting sensor anomalies in a dynamic univariate time-series signal
US12260304B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 18, 2021 |
| Grant date | Mar 25, 2025 |
| Priority date | — |
| Expiry date | Jan 25, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2218/22
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The disclosed embodiments provide a system that detects sensor anomalies in a univariate time-series signal. During a surveillance mode, the system receives the univariate time-series signal from a sensor in a monitored system. Next, the system performs a staggered-sampling operation on the univariate time-series signal to produce N sub-sampled time-series signals, wherein the staggered-sampling operation allocates consecutive samples from the univariate time-series signal to the N sub-sampled time-series signals in a round-robin ordering. The system then uses a trained inferential model to generate estimated values for the N sub-sampled time-series signals based on cross-correlations with other sub-sampled time-series signals. Next, the system performs an anomaly detection operation to detect incipient sensor anomalies in the univariate time-series signal based on differences between actual values and the estimated values for the N sub-sampled time-series signals. Whenever an incipient sensor anomaly is detected, the system generates a notification.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.