Patent ID: 12253431

DETAILED DESCRIPTION

FIG.1schematically illustrates a gas turbine engine20. The example gas turbine engine20is a turbofan that generally incorporates a fan section22, a compressor section24, a combustor section26and a turbine section28. The fan section22drives air along a bypass flow path B in a bypass duct defined within a nacelle30. The gas turbine engine20intakes air along a core flow path C into the compressor section24for compression and communication into the combustor section26. In the combustor section26, the compressed air is mixed with fuel from a fuel system32and ignited by igniter34to generate an exhaust gas flow that expands through the turbine section28and is exhausted through exhaust nozzle36. Although a turbofan gas turbine engine is depicted and described, it should be understood that the concepts described herein are not limited to use with such configurations, and the teachings of this disclosure may be applied to any other types of aircraft engines.

The gas turbine engine20further includes at least one bleed valve38in the compressor section24and a plurality of sensors40. As will be described further below, the bleed valve38diverts a portion of air from the core airflow C during certain operating conditions. In some examples, the diverted air is subsequently routed to other sections of the gas turbine engine20, such as the turbine section28, and used for cooling applications.

The sensors40may be located in any one or more of the compressor section24, the combustor section26, and the turbine section28, and are configured to obtain operational data of the gas turbine engine20. The sensors40are configured to communicate signals indicative of this data to a computing device102(shown inFIG.4), which may be remote from the gas turbine engine20.

In this disclosure “operational data” relates to the operational parameters of the gas turbine engine20, and may include data relating to a combustor section26exit temperature, a compressor section24exit pressure, a compressor section24rotational speed, an inter turbine temperature in the turbine section28, a fuel flow rate of the fuel system32, a pressure ratio of the compressor section24, or any other parameter relating to power output or operational performance of the gas turbine engine20. The operational data may be obtained during any operating condition of the gas turbine engine20, such as during take-off, cruise, acceleration, deceleration, etc., and may be obtained over multiple missions (i.e., flights) of the gas turbine engine20. More relevant to helicopter engines, which fall within the scope of this disclosure, the operational data may be obtained during a power assurance check and may comprise a power assurance check margin.

FIGS.2and3illustrate an example bleed valve38in a closed position (FIG.2) and an open position (FIG.3). The bleed valve38is configured to selectively allow fluid communication between an inlet44and an outlet46depending on the operating condition of the gas turbine engine20. The bleed valve38includes a piston assembly48and a pressure regulating assembly50. The pressure regulating assembly50includes a pressure regulating fluid R and controls a threshold pressure of the core airflow C required for the bleed valve38to move from the closed to the open position.

The piston assembly48generally includes a piston52that is received in a piston guide54and biased by a spring56. The piston guide54constrains the piston52to linear movement between the closed position and the open position. The spring56provides a bias on the piston52towards the pressure regulating fluid R of the pressure regulating assembly50, and accordingly, provides a bias towards the open position. The piston52further includes a top portion58that is configured to block fluid flow between the inlet44and the outlet46in the closed position, and a bottom portion60configured to engage with and receive a force from the pressure regulating fluid R.

The pressure regulating assembly50includes an inlet62, an outlet64, a cavity66, and an adjustment stem68. The inlet62is configured to receive the pressure regulating fluid R and the outlet64is configured to discharge the pressure regulating fluid R. Between the inlet62and the outlet64, the pressure regulating fluid R is communicated to the cavity66, which also houses the bottom portion60of the piston52. The adjustment stem68is configured to adjust the pressure of the pressure regulating fluid R.

The bleed valve38also includes one or more seals. In this example, the bleed valve38includes a first O-ring seal70between the top portion58of the piston52and the inlet44that prevents core airflow C from leaking through the inlet44when the bleed valve38is in the closed position. A second rolling diaphragm seal71is provided between the bottom portion60of the piston52and the walls of the cavity66to prevent leakage of the pressure regulating fluid R.

During operation of the gas turbine engine20in low power settings, it is desirable for the bleed valve38to remain in the open position. The bleed valve38stays in the open position as long as the combined force of the spring56plus the force that the core airflow C applies to the top portion58of the piston52is greater than the force applied by the pressure regulating fluid R against the bottom portion60of the piston52. On the other hand, during higher power operating conditions, such as during acceleration, it is desirable for the bleed valve38to be biased to the closed position. During such operating conditions, the pressure of the pressure regulating fluid R may increase and thereby overcome the combined forces provided by the spring56and the core airflow C to close the bleed valve38.

Accordingly, during operation of the gas turbine engine20, the bleed valve38modulates between the open and closed position to accommodate different operating conditions of the gas turbine engine20. When pressure of the core airflow C in the compressor section24exceeds the threshold set by the pressure regulating assembly50, the bleed valve38opens to reduce pressure and maintain stability in the compressor section24. The operation of the bleed valve38may be as known.

It should be understood that the specific bleed valve38configuration shown inFIGS.2and3is exemplary, and the systems and methods of this disclosure are equally applicable to other bleed valve configurations.

In the harsh environment of the gas turbine engine20, over time the bleed valve38may experience any number of component failures due to wear. For example, one of the seals70,71may fail and allow leakage of the core airflow C between the top portion58of the piston52and the inlet44, or allow leakage of the pressure regulating fluid R between the bottom portion60of the piston52and the walls of the cavity. The piston assembly48may fail via deformation or otherwise, such that the bleed valve38may no longer fully open or close. The pressure regulating assembly50may also experience a component failure, and therefore fail to set an appropriate pressure threshold for modulation of the bleed valve38.

These component failures may cause the bleed valve38to leak more or less core airflow C then desired or cause the bleed valve38to have a delayed or lagging response. These failures of the bleed valve38may, in turn, have deleterious effects on various operational parameters of the gas turbine engine20. For example, a failure of the bleed valve38may cause the compressor section24to deliver lower pressure air to the combustor section26then desired. Combustion of this lower pressure air may lead to a lower temperature of combustion products exiting the combustor section26. The controls of the gas turbine engine20may then attempt to compensate by increasing a rate of fuel pushed through the fuel system32and increasing the rotational speed of the compressor section24. As another example, temperatures in the turbine section28, such as an inter-turbine temperature, may increase due to the bleed valve38failing to communicate a sufficient amount of cooling airflow.

Accordingly, a failure in the bleed valve38will have an effect on the operational data detected by the sensors40, and different failure modes of the bleed valve38may affect the operational data in different ways. This application utilizes one or more neural networks to analyze the operational data to determine when a failure has occurred or is imminent and determine a specific failure mode, or root cause, of such a failure. While this application refers to neural networks, it should be understood that the contents of this disclosure may be applied with any type of machine learning methodology.

FIG.4is a schematic view of an example system100for monitoring a bleed38valve of a gas turbine engine (e.g., the gas turbine engine20ofFIG.1). The example system includes a computing device102and a user interface104. The computing device102includes processing circuitry106operatively connected to a memory108and a communication interface110.

The processing circuitry106may include one or more microprocessors, microcontrollers, application specific integrated circuits (ASICs), or the like. The processing circuitry106may be operable to perform the steps of method300described below.

The memory108can include any one or combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, VRAM, etc.) and/or nonvolatile memory elements (e.g., ROM, hard drive, tape, CD-ROM, etc.). Moreover, the memory108may incorporate electronic, magnetic, optical, and/or other types of storage media. The memory108can also have a distributed architecture, where various components are situated remotely from one another, but can be accessed by the processing circuitry. The memory108further includes one or more neural networks112.

The communication interface110is configured to receive the data from the sensors40and to communicate with the user interface104. In some examples, the communication interface110further communicates with a controller of the gas turbine engine20, or a controller of the aircraft mounting the gas turbine engine20.

FIG.5illustrates a flowchart200for training the one or more neural networks112of system100. At step202a gas turbine engine20with a bleed valve38failure is identified. Step202may include manual determinations of the likely presence of a bleed valve issue. For example, variations in a compressor section24running line may indicate some form of bleed valve38failure to an operator or inspector of the gas turbine engine20.

At step204an inspector runs diagnostic tests or troubleshooting on the gas turbine engine20. Step204may include disassembly and visual inspection of the bleed valve of the gas turbine engine. Step204may also include testing the gas turbine engine20in a test cell where flight scenarios are recreated and data is recorded. At step206, based on the testing and troubleshooting of step204, the inspector confirms the presence of a bleed valve38failure in the gas turbine engine20and determines a root cause of the failure. For example, the inspector may determine that any one or more of the seals70,71the piston assembly48, or the pressure regulating assembly50have failed and thereby caused the bleed valve to stop functioning properly.

At step208, operational data of the gas turbine engine20is obtained. Step208may include obtaining operational data both prior to and after the occurrence of the bleed valve38failure with sensors40on the gas turbine engine20and communicating that data to a computing device102.

At step210a root cause data point is developed which associates the manually determined root cause of the bleed valve38failure with the operational data for the gas turbine engine20obtained at step208. Step210may include the inspector inputting the manually determined root cause via the user interface104of the computing device102.

Steps202through210are iterated for a plurality of gas turbine engines experiencing bleed valve failures. The root cause data points developed for each of the gas turbine engines collectively establish a root cause dataset212. In one example, the plurality of gas turbine engines included in the root cause dataset212all include identical configurations, however the root cause dataset212may also include data from a verity of different gas turbine engine configurations.

At step214the root cause dataset212is provided to the one or more neural networks112as training data.

At step216a performance profile dataset218is also provided to the one or more neural networks112as training data. The performance profile dataset218includes associations between operational data from a plurality of gas turbine engine missions with extrinsic factors affecting engine performance during those missions.

In this disclosure “extrinsic factors” refers to any factor other than a bleed valve issue that may affect operational data obtained from the gas turbine engine20. As an example, gas turbine engines naturally lose efficiency over time, and thus there is a correlation between the length of time the engine has been in operation (sometimes referred to as “engine time on wing”) and the operational data. As other non-limiting examples, the weight of an aircraft mounting the gas turbine engine20, the load requirements of an accessory gearbox of the gas turbine engine20, and external environmental factors (i.e., external temperature and altitude) may all impact operation of the gas turbine engine20and thus the operational data.

Steps214and216may include supervised learning, unsupervised learning, reinforcement learning, or any other appropriate method of training the one or more neural networks112.

FIG.6illustrates a method300for monitoring the health of the bleed valve38of a gas turbine engine20with the system100. At step302operational data of the gas turbine engine20is obtained from at least one sensor40. At step304extrinsic data relating to extrinsic factors affecting operational performance of the gas turbine engine20is obtained. The extrinsic data of step304may also be captured by sensors40, or may be inputted by an operator using user interface104, stored in the memory108, or communicated from any other external computing device or controller, such as a controller of the gas turbine engine20or the controller of an aircraft mounting the gas turbine engine20.

At step306, at least one neural network112determines an expected performance profile of the gas turbine engine20by associating the extrinsic data with the performance profile dataset218. The expected performance profile is a determination of how the gas turbine engine20is expected to perform in view of the extrinsic factors.

At step308, the at least one neural network112analyzes the operational data obtained from the sensors40in comparison to the expected performance profile to determine the presence of any deviations. A detected deviation between the expected performance profile of the gas turbine engine20and the operational data obtained for the gas turbine engine20may be indicative of a failure of the bleed valve38.

It should be understood that the operational data may be obtained over time and over a plurality of missions of the gas turbine engine20. Accordingly, step308may include analyzing trends in the operational data of the gas turbine engine20. For example, prior to any complete failure of the bleed valve38, there may be a negative trend in the operational parameters as the bleed valve38approaches a certain failure mode.

The extrinsic factors experienced by the gas turbine engine20may also vary over time and thus the expected performance profile generated at step306may also vary. This variation serves to normalize the operational data, or isolate changes in the operational data that may be attributed to a failure of the bleed valve38.

At step310, the at least one neural network112compares any deviation or trend detected at step308with the root cause dataset212to determine a health status of the bleed valve38. In an example, step310includes a comparison of a deviation or trend identified at step308for the subject gas turbine engine20with operational data of gas turbine engines included in the root cause dataset during, or just prior to, the occurrence of a particular bleed valve failure mode. Step310may include determining that the bleed valve38is functioning properly, that the bleed valve38has failed, or that the bleed valve38is likely to fail after a threshold amount of further time in use in the gas turbine engine20.

Step310may further include determining a likely root cause or specific failure mode of a failure in the bleed valve38. As noted above, potential failure modes of the bleed valve include a failure of the seals70,71failure in the piston assembly48, such as a failure of the spring56, or failure in the pressure regulating assembly50. Step310may include the at least one neural network112assigning a confidence score to each potential failure mode.

At step312an alert is provided to the user interface104based on the health status determined in step310. The alert may indicate to an operator that the bleed valve38has failed and requires maintenance, and may also provide information relating to the most likely failure mode of the bleed valve38. The alert may also indicate that the bleed valve38is expected to fail after a threshold amount of operational time.

The systems and methods of this disclosure may be used to detect when a bleed valve is trending towards a failure and raise advanced warning for preventative maintenance activity, such as repair or replacement of the bleed valve. Accordingly, in-flight failure of the bleed valve, and the associated negative effects on engine operational performance may be avoided. Further, by providing an indication of a specific failure mode of the bleed off valves, the disclosed systems and methods simplify troubleshooting and diagnostic testing and reduce maintenance costs.

Although embodiments have been disclosed, a worker of skill in this art would recognize that modifications would come within the scope of this disclosure. For that reason, the following claims should be studied to determine the true scope and content.