Oil debris monitoring (ODM) with adaptive learning
US10409275B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 19, 2016 |
| Grant date | Sep 10, 2019 |
| Priority date | — |
| Expiry date | Jun 23, 2037 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02E10/72
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
A system and method for debris particle detection with adaptive learning are provided. The method includes receiving oil debris monitoring (ODM) sensor data from an oil debris monitor sensor and fleet data from a database, detecting a feature in the ODM sensor data, generating an anomaly detection signal based on detecting an anomaly by comparing the feature in the ODM sensor data to a limit defined by system information stored in the fleet data, selecting a maintenance action request based on the anomaly detection signal, and adjusting one or more of the feature, the anomaly, the limit, and the maintenance action request by applying an adaptive learning algorithm that uses the ODM sensor data, fleet data, and feedback from field maintenance of one or more engines that evolves over time.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.