PARAMETRIC TRENDING ARCHITECTURE CONCEPT AND DESIGN

    公开(公告)号:US20180136082A1

    公开(公告)日:2018-05-17

    申请号:US15350933

    申请日:2016-11-14

    CPC classification number: G01M15/14 F01D21/003 F05D2220/32

    Abstract: A method for foreign object debris detection in a gas turbine engine may comprise receiving, by a controller, a plurality of master configuration parameters, receiving, by the controller, a plurality of individual configuration parameters in response to the plurality of master configuration parameters, receiving, by the controller, a first time-series data from a database, pre-processing, by the controller, the first time-series data to generate a second time-series data, implementing, by the controller, an anomaly detector model, the anomaly detector model configured to generate a third time-series data, and implementing, by the controller, a foreign object debris (FOD) damage model, the FOD damage model receiving the third time-series data. The pre-processing the first time-series data, the implementing the anomaly detector model, and the implementing the FOD damage model may be defined by the plurality of individual configuration parameters.

    SYSTEM AND METHOD FOR DETECTING LUBRICATED BEARING CONDITION

    公开(公告)号:US20180096582A1

    公开(公告)日:2018-04-05

    申请号:US15284057

    申请日:2016-10-03

    Abstract: A monitoring system includes an analytical engine system coupled to a sensor of an engine system. The analytical engine system is configured to receive data corresponding to operation of the engine system, to determine a distance metric corresponding to the operating parameters of the engine system, to compare the distance metric for a monitored lubricant temperature to a model threshold, and to generate a lubricant alert signal when the distance metric for the monitored lubricant temperature is greater than the model threshold. The received data includes the monitored lubricant temperature of a bearing and operating parameters of the engine system. The distance metric is based at least in part on the monitored lubricant temperature relative to a lubricant temperature statistical model, which is based at least in part on the operating parameters of the engine system.

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