A system that includes: a gas turbine having a combustion system; a control system operably connected to the gas turbine for controlling an operation thereof; and a combustion auto-tuner, which is communicatively linked to the control system, that includes an optimization system having an empirical model of the combustion system and an optimizer; sensors configured to measure the inputs and outputs of the combustion system; a hardware processor; and machine-readable storage medium on which is stored instructions that cause the hardware processor to execute a tuning process for tuning the operation of the combustion system. The tuning process includes the steps of: receiving current measurements from the sensors for the inputs and outputs; given the current measurements received from the sensors, using the optimization system to calculate an optimized control solution for the combustion system; and communicating the optimized control solution to the control system.

Herein provided are systems and methods for operating an aircraft engine during inclement weather. At least one image of a location substantially in line with a heading of the aircraft is acquired. Based on the at least one image, an inclement weather condition in the location is detected. An alert mode of the engine is triggered upon detecting the inclement weather condition. Responsive to the alert mode being triggered, at least one predetermined performance parameter of the engine is monitored. Upon detecting a change in the at least one predetermined performance parameter beyond a predetermined threshold, at least one operating condition of the engine is altered.

The present invention relates to a method for inspecting a component, in particular a component of a turbomachine (1), including the steps of: capturing (S2) at least one X-ray or CT image of the component (10) using an image-capturing device (20); providing (S21) metadata about the component (10), the metadata including, in particular, a component type, a running time of the component (10), a number of remaining life cycles, and/or a repair history; classifying, by a machine learning system (30), the component (10) into a serviceable category or a non-serviceable category based on the image captured by the image-capturing device (20) and the provided metadata.

Computer-implemented methods are disclosed for determining a flow rate of fluid flow at a target time through a pump, such as a centrifugal pump, the pump being driven by a pump motor. An illustrative method includes, in some aspects: receiving at least one set of previous parameter values including a first previous parameter value indicative of a first operational parameter of the pump motor at a previous time, earlier than the target time, and a second previous parameter value indicative of a second operational parameter of the pump motor at the previous time, and receiving a current set of parameter values including a first current parameter value indicative of the first operational parameter of the pump motor at the target time and a second current parameter value indicative of the second operational parameter of the pump motor at the target time.

A method of inspecting blades of a gas turbine engine for abnormalities includes projecting light from a light source into an illumination area; utilizing a sensor to record data of at least one reflection of the projected light from a blade that is part of a gas turbine engine and is disposed in the illumination area; determining, based on the recorded data, whether the blade is abnormal; and based on the determining indicating that the blade is abnormal, providing a blade abnormality notification. A gas turbine engine is also disclosed.

Examples described herein provide a method for aircraft engine anomaly detection based on an odor. The method includes receiving data indicative of the odor associated with an aircraft engine of an aircraft. The method further includes analyzing the data to determine whether an anomaly associated with the aircraft has occurred. The method further includes responsive to determining that an anomaly associated with the aircraft has occurred, implementing a corrective action based at least in part on the data.

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.

An apparatus for monitoring of a pump includes a control module, and an error detection unit, wherein a support vector machine based module is provided that receives an estimated output quantity data value from the control module, processes the estimated output quantity data value to provide a processed estimated output quantity data value via the support vector machine, and supplies the processed estimated output quantity data value to the error detection unit instead of the estimated output quantity data value of the control module.

Systems and methods for adjusting blade tip clearance targets and utilizing the adjusted targets to optimize the clearances between the blade tips and surrounding shrouds of a turbine engine are provided. In one exemplary aspect, one or more engine controllers utilize a machine-learned model to customize blade tip clearance targets based on the way an engine has been uniquely operated in the past for a particular flight mission. Present flight data associated with a present flight of a given flight mission is obtained. A model blade tip clearance target is adjusted based at least in part on the machine-learned model and the present flight data. The machine-learned model is trained at least in part on past flight data indicative of the manner in which the turbine engine has been operated for one or more past flights of the flight mission. An adjusted blade tip clearance target is then generated.

A vacuum pump includes a housing having an inlet and an outlet, at least one rotor arranged in the housing configured to convey a gaseous medium from the inlet to the outlet, a motor configured to rotate the rotor, a control device connected to the motor configured to control the motor, and at least one sensor connected to the control device. The at least one sensor is configured to sense at least one operating parameter of the vacuum pump. The control device comprises a correlation module. The correlation module is configured to correlate the sensed at least one operating parameter with at least one critical parameter. The motor is controlled on the basis of the at least one critical parameter.