Anomaly detection by self-learning of sensor signals
US10743821B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 10, 2017 |
| Grant date | Aug 18, 2020 |
| Priority date | — |
| Expiry date | Jun 20, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2218/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Accurate detection of anomaly in sensor signals is critical and can have an immense impact in the health care domain. Accordingly, identifying outliers or anomalies with reduced error and reduced resource usage is a challenge addressed by the present disclosure. Self-learning of normal signature of an input sensor signal is used to derive primary features based on valley and peak points of the sensor signals. A pattern is recognized by using discrete nature and strictly rising and falling edges of the input sensor signal. One or more defining features are identified from the derived features based on statistical properties and time and frequency domain properties of the input sensor signal. Based on the values of the defining features, clusters of varying density are identified for the input sensor signal and based on the density of the clusters, anomalous and non-anomalous portions of the input sensor signals are classified.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.