OIL DEBRIS MONITORING (ODM) WITH ADAPTIVE LEARNING

A method (700) for debris particle detection with adaptive learning includes: receiving (705) oil debris monitoring (ODM) sensor data from an oil debris monitor sensor and fleet data from a database; detecting (710) a feature in the ODM sensor data; generating an anomaly detection signal based on de...

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Bibliographische Detailangaben
Hauptverfasser: KHIBNIK, Alexander I, LIN, Yiquing, HAGEN, Gregory S, GIERING, Michael J, ERDINC, Ozgur
Format: Patent
Sprache:eng ; fre ; ger
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Zusammenfassung:A method (700) for debris particle detection with adaptive learning includes: receiving (705) oil debris monitoring (ODM) sensor data from an oil debris monitor sensor and fleet data from a database; detecting (710) a feature in the ODM sensor data; generating an anomaly detection signal based on detecting (715) an anomaly by comparing the feature in the ODM sensor data to a limit defined by system information stored in the fleet data; selecting (720) 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 (725) 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.