Invention Grant
- Patent Title: Application of machine learning to process high-frequency sensor signals of a turbine engine
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Application No.: US16294358Application Date: 2019-03-06
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Publication No.: US11892003B2Publication Date: 2024-02-06
- Inventor: James Ryan Reepmeyer , Johan Michael Reimann , Gagan Adibhatla , Evin Nathaniel Barber , Stefan Joseph Cafaro , Rahim Panjwani , Frederick John Menditto, III , Aaron James Schmitz , Suchot Kongsomboonvech , Richard Anthony Zelinski
- Applicant: General Electric Company
- Applicant Address: US NY Schenectady
- Assignee: General Electric Company
- Current Assignee: General Electric Company
- Current Assignee Address: US NY Schenectady
- Agency: Dority & Manning, P.A.
- Main IPC: F04D27/00
- IPC: F04D27/00 ; G01M15/14 ; G05B13/02 ; G06N3/04 ; F04D27/02 ; G06N3/044 ; G06N3/045

Abstract:
A control system for active stability management of a compressor element of a turbine engine is provided. In one example aspect, the control system includes one or more computing devices configured to receive data indicative of an operating characteristic associated with the compressor element. For instance, the data can be received from a high frequency sensor operable to sense pressure at the compressor element. The computing devices are also configured to determine, by a machine-learned model, a stall margin remaining of the compressor element based at least in part on the received data. The machine-learned model is trained to recognize certain characteristics of the received data and associate the characteristics with a stall margin remaining of the compressor element. The computing devices are also configured to cause adjustment of one or more engine systems based at least in part on the determined stall margin remaining.
Public/Granted literature
- US20200284265A1 Application of Machine Learning to Process High-Frequency Sensor Signals of a Turbine Engine Public/Granted day:2020-09-10
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