General Electric Company

Gagan Adibhatla

James Ryan Reepmeyer

Stefan Joseph Cafaro

Johan Michael Reimann

Evin Nathaniel Barber

Suchot Kongsomboonvech

Richard Anthony Zelinski

Rahim Panjwani

Frederick John Menditto, Iii

Aaron James Schmitz

Iii Frederick John Menditto

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.

Application of machine learning to process high-frequency sensor signals of a turbine engine

A monitoring and control system for a flow duct and a method for determining a component status of an operational component disposed within a flow passage of the flow duct utilizing the system are provided. In one exemplary aspect, the system includes at least two sensors that are disposed within the flow passage and configured to sense a characteristic of a fluid flowing therethrough. The sensors may be averaging sensors. Each sensor extends circumferentially about an axial centerline defined by the flow duct. The sensors are arranged in an overlapped arrangement. Particularly, the sensors extend circumferentially about the axial centerline such that the sensors physically overlap one another circumferentially. Additionally, the sensors may be disposed within the same or substantially the same plane axially. Signals generated by the sensors may be utilized to monitor and control the fluid and various operational components disposed within the flow passage.

Monitoring and control system for a flow duct

The subject matter of the present disclosure is directed to a turbine engine having an adaptive prognostic health management control system that passively monitors stall margin reductions and applies corrective trims to a power management schedule of the engine to recover operability over time whilst maintaining a sufficient level of stall margin over the life cycle of the engine. The control system can adjust the power management schedule as needed to sustain a target stall margin, which allows for a more optimized and gradual performance to operability trade-off.

Prognostic health management control for adaptive operability recovery for turbine engines

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Cincinnati, OH, United States of America

Gagan Adibhatla