Patent Publication Number: US-2020284265-A1

Title: Application of Machine Learning to Process High-Frequency Sensor Signals of a Turbine Engine

Description:
FIELD 
     The subject matter of the present disclosure relates generally to control systems for gas turbine engines, and more particularly to real-time processing of high-frequency sensor data of gas turbine engines using machine-learned models. 
     BACKGROUND 
     Gas turbine engines can include one or more compressors. A compressor active stability management (CASM) system can be used to protect the engine from compressor stall. For instance, CASM systems can receive sensor data and interpret the sensor data in some way to represent impending stall or stall margin remaining. Then, based on the interpreted signal, CASM systems can rapidly cut fuel to the engine and/or cause variable geometry components to move to avoid stall. However, conventional CASM systems have been unable to capture certain features in the sensor data that can be indicative of degrading stall margin. Moreover, conventional CASM systems typically require long processing times to interpret the received sensor data. As a result, the stall margin remaining is generated too slowly or not accurate enough to support an active CASM system that directly controls to a specified operability margin. 
     Accordingly, an engine having a control system that addresses one or more of the challenges noted above would be useful. Moreover, a method for controlling an engine that addresses one or more of the challenges noted above would be beneficial. 
     BRIEF DESCRIPTION 
     Aspects and advantages of the invention will be set forth in part in the following description, or may be obvious from the description, or may be learned through practice of the invention. 
     In one aspect, a control system for an engine having a compressor element is provided. The control system includes one or more computing devices having one or more memory devices and one or more processing devices, the one or more memory devices storing computer-readable instructions that can be executed by the one or more processing devices to perform operations. In performing the operations, the one or more processing devices are configured to: receive data indicative of an operating characteristic associated with the compressor element and determine, by a machine-learned model, a stall margin remaining of the compressor element based at least in part on the received data. 
     In another aspect, a method is provided. The method includes operating an engine having a compressor element. Further, the method includes receiving, by one or more computing devices, data indicative of an operating characteristic associated with the compressor element of the engine. The method also includes determining, by a machine-learned model of the one or more computing devices, a stall margin remaining of the compressor element based at least in part on the received data. 
     In yet another aspect, a method for controlling a turbine engine is provided. The method includes operating the turbine engine; setting, by one or more computing devices, a target signal associated with the turbine engine; receiving, by the one or more computing devices, data indicative of an operating characteristic associated with the turbine engine; predicting, by a machine-learned model of the one or more computing devices, a signal based at least in part on the received data; determining, by the one or more computing devices, a signal error based at least in part on the predicted signal and the target signal, and causing, by the one or more computing devices, adjustment of one or more engine systems of the turbine engine based at least in part on the determined signal error. 
     These and other features, aspects and advantages of the present invention will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A full and enabling disclosure of the present invention, including the best mode thereof, directed to one of ordinary skill in the art, is set forth in the specification, which makes reference to the appended figures, in which: 
         FIG. 1  provides a schematic cross-sectional view of a gas turbine engine in accordance with an example embodiment of the present subject matter; 
         FIG. 2  provides a schematic view of the gas turbine engine of  FIG. 1  and depicts a control system thereof; 
         FIG. 3  provides a schematic view of the control system of  FIG. 2 ; 
         FIG. 4  provides a graph depicting a data signal as a function of time in accordance with an example embodiment of the present subject matter; 
         FIG. 5  provides a graph depicting the stall margin remaining of a compressor element versus time during a transient event for various control schemes in accordance with an example embodiment of the present subject matter; 
         FIG. 6  provides a block diagram of an example control loop for active stability management of a compressor element in accordance with an example embodiment of the present subject matter; 
         FIG. 7  provides a graph depicting a spliced raw data signal as a function of time and a corresponding determined stall margin remaining signal in accordance with an example embodiment of the present subject matter; 
         FIG. 8  provides another graph depicting a spliced raw data signal as a function of time and a corresponding determined stall margin remaining signal in accordance with an example embodiment of the present subject matter; 
         FIG. 9  provides a graph depicting a first signal and a second signal overlaying one another over time in accordance with an example embodiment of the present subject matter; 
         FIG. 10  provides a graph depicting a stall margin remaining output by a machine-learned model of the present disclosure versus the actual stall margin remaining of the compressor element for a given engine power setting and a stall margin remaining output by a conventional control system versus the actual stall margin remaining of the compressor element for a given engine power setting in accordance with an example embodiment of the present subject matter; 
         FIG. 11  provides a flow diagram of an exemplary method in accordance with an example embodiment of the present subject matter; 
         FIG. 12  provides a flow diagram of another exemplary method in accordance with an example embodiment of the present subject matter; and 
         FIG. 13  provides an example computing system in accordance with an example embodiment of the present subject matter. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to present embodiments of the invention, one or more examples of which are illustrated in the accompanying drawings. The detailed description uses numerical and letter designations to refer to features in the drawings. Like or similar designations in the drawings and description have been used to refer to like or similar parts of the invention. As used herein, the terms “first”, “second”, and “third” may be used interchangeably to distinguish one component from another and are not intended to signify location or importance of the individual components. The terms “upstream” and “downstream” refer to the relative flow direction with respect to fluid flow in a fluid pathway. For example, “upstream” refers to the flow direction from which the fluid flows, and “downstream” refers to the flow direction to which the fluid flows. Further, as used herein, the terms “axial” or “axially” refer to a dimension along a longitudinal axis of an engine. The term “forward” used in conjunction with “axial” or “axially” refers to a direction toward the engine inlet, or a component being relatively closer to the engine inlet as compared to another component. The term “rear” used in conjunction with “axial” or “axially” refers to a direction toward the engine nozzle, or a component being relatively closer to the engine nozzle as compared to another component. The terms “radial” or “radially” refer to a dimension extending between a center longitudinal axis of the engine and an outer engine circumference. Furthermore, as used herein, terms of approximation, such as “approximately,” “substantially,” or “about,” refer to being within a fifteen percent (15%) margin of error unless otherwise stated. Furthermore, as used herein, the term “real time” refers to at least one of the time of occurrence of the associated events, the time of measurement and collection of predetermined data, the time to process the data, and the time of a system response to the events and the environment. In the embodiments described herein, these activities and events occur substantially instantaneously. 
     A control system for a turbine engine is provided. More particularly, a control system for active stability management of a compressor element is provided. In one example aspect, the control system includes one or more computing devices having one or more memory devices and one or more processing devices. The one or more memory devices store computer-readable instructions that can be executed by the one or more processing devices to perform operations. In performing the operations, the one or more processing devices are 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 fluctuations caused by blade tip vortices at the compressor element. The frequency at which the sensor reads the operating characteristic associated with the compressor element (e.g., the pressure fluctuations at the compressor element) can be on the order of two thousand times (2,000×) the update rate of the one or more computing devices. 
     The one or more processing devices are also configured to determine, by a machine-learned model in real time, a stall margin remaining of the compressor element based at least in part on the received data. The machine-learned model can be a neural network, e.g., a recurrent or convolutional neural network. The machine-learned model can be trained to recognize certain characteristics of the received data. For instance, the machine-learned model can be trained to recognize one or more characteristics of the AC component of the received raw data, e.g., frequency characteristics, amplitude characteristics, and/or other pattern or characteristics of the AC component of the data. The machine-learned model can be trained to associate the frequency characteristics, amplitude characteristics, and/or other pattern or characteristics of the AC component of the data sensor with a stall margin remaining of the compressor element. In some embodiments, the machine-learned model is trained to recognize one or more characteristics of the DC component of the received data to enhance the accuracy of the determined stall margin remaining signal. 
     The one or more processing devices are further configured to determine a stall margin error based at least in part on the determined stall margin remaining and a target stall margin remaining. Then, the one or more processing devices are configured to cause adjustment of one or more engine systems based at least in part on the determined stall margin remaining, or more particularly, based at least in part on the stall margin error. The one or more processing devices can continuously cause adjustment of the engine systems to drive the stall margin error to zero (0). That is, the one or more processing devices can continuously cause adjustment of the engine systems to drive the predicted stall margin remaining (i.e., the stall margin remaining output by the machine-learned model) to the target stall margin. Methods for controlling turbine engines using such a control system are also provided. 
     The systems and methods described herein may provide a number of technical effects, benefits, and improvements to control systems for turbine engines and computing technology thereof In one aspect, the machine-learned model of the computing system(s) of the present disclosure can produce a highly accurate and high bandwidth stall margin remaining signal in real time that enables the use of an active stability management system. Enabling active real-time control over compressor stall margin requires a robust, accurate, and high bandwidth stall margin remaining signal. The improved stall margin remaining signal enables downstream control logic to push the operating line closer to stall than stall margin remaining signals produced by conventional control schemes. This allows for thrust growth and/or for improved cycle efficiency. Additionally, the stall margin remaining signal generated by the machine-learned model disclosed herein can be further leveraged to assist in prognostic health management (PHM) applications, including enhanced digital/embedded models and compressor health monitoring. 
     In addition, in utilizing a machine-learned model to generate a stall margin remaining signal, computer-processing times may be significantly reduced and processing resources may be used for other core processing functions, among other benefits. Conventional methods are unable to fully capture all features in the sensor data that can be indicative of degrading stall margin and thus require longer periods of processing time to produce an accurate signal. The result is a signal too slow to support an active stability management control system. The control system described herein is operable to produce an accurate stall margin remaining signal that satisfies the criteria required to enable an active stability management control system. 
       FIG. 1  provides a schematic cross-sectional view of a gas turbine engine in accordance with an example embodiment of the present disclosure. For the depicted embodiment of  FIG. 1 , the gas turbine engine is a high-bypass turbofan jet engine  10 , referred to herein as “turbofan  10 .” The turbofan  10  can be mounted to an aerial vehicle, such as e.g., a fixed-wing aircraft. As shown in  FIG. 1 , the turbofan  10  defines an axial direction A, a radial direction R, and a circumferential direction. Moreover, the turbofan  10  defines an axial centerline or longitudinal axis  12  that extends therethrough for reference purposes. In general, the longitudinal axis  12  and the axial direction A extend parallel to one another, the radial direction R extends inward toward and outward from the longitudinal axis  12  orthogonal to the axial direction A, and the circumferential direction extends concentrically around the longitudinal axis  12 . 
     The turbofan  10  includes a fan section  14  and a core turbine engine  16  disposed downstream of the fan section  14 . The core turbine engine  16  includes a substantially tubular outer casing  18  that defines an annular core inlet  20 . The outer casing  18  encases, in serial flow relationship, a compressor section including a booster or low pressure (LP) compressor  22  and a high pressure (HP) compressor  24 ; a combustor section including a combustor  26 ; a turbine section including a high pressure turbine  28  and a low pressure turbine  30 ; and a jet exhaust nozzle section  32 . A high pressure shaft or spool  34  drivingly connects the HP turbine  28  to the HP compressor  24 . A low pressure shaft or spool  36  drivingly connects the LP turbine  30  to the LP compressor  22 . 
     Each compressor  22 ,  24  can include a plurality of compressor elements. Particularly, for this embodiment, each compressor  22 ,  24  of the turbofan  10  includes a plurality of compressor stages, with each stage including both an annular array of stationary compressor vanes  82  and an annular array of rotating compressor blades  84  positioned immediately downstream of the compressor vanes  82 . Additionally, the compressors  22 ,  24  can include one or more variable geometry components  86  ( FIG. 2 ), including inlet guide vanes (IGVs), outlet guide vanes (OGVs), variable stator vanes, etc. Further, one or more bleed valves  88  ( FIG. 2 ) can be positioned along the compressor  24  and/or compressor  22  and are operable to move open and closed such that pressurized air can be bled from the core air flowpath of the core engine  16  of the turbofan  10 . The bleed air bled through the bleed valves  88  can be routed to other engine systems, such as e.g., an active clearance control system for the HP turbine  28 , or to various vehicle systems to which the engine is attached, such as e.g., a cabin air pressure system for pressurizing the cabin of an aircraft. 
     For the embodiment depicted, the fan section  14  includes a variable pitch fan  38  having a plurality of fan blades  40  coupled to a disk  42  in a spaced apart manner. As depicted, the fan blades  40  extend outwardly from disk  42  generally along the radial direction R. Each fan blade  40  is rotatable relative to the disk  42  about a pitch axis P by virtue of the fan blades  40  being operatively coupled to a suitable actuation member  44  configured to collectively vary the pitch of the fan blades  40  in unison. The fan blades  40 , disk  42 , and actuation member  44  are together rotatable about the longitudinal axis  12  by LP shaft  36 . 
     Referring still to the exemplary embodiment of  FIG. 1 , the disk  42  is covered by rotatable spinner  48  aerodynamically contoured to promote an airflow through the plurality of fan blades  40 . Additionally, the exemplary fan section  14  includes an annular fan casing or outer nacelle  50  that circumferentially surrounds the fan  38  and/or at least a portion of the core turbine engine  16 . It should be appreciated that the nacelle  50  may be configured to be supported relative to the core turbine engine  16  by a plurality of circumferentially-spaced outlet guide vanes  52 . Moreover, a downstream section  54  of the nacelle  50  can extend over an outer portion of the core turbine engine  16  so as to define a bypass airflow passage  56  therebetween. 
     During operation of the turbofan engine  10 , a volume of air  58  enters the turbofan  10  through an associated inlet  60  of the nacelle  50  and/or fan section  14 . As the volume of air  58  passes across the fan blades  40 , a first portion of the air  58  as indicated by arrows  62  is directed or routed into the bypass airflow passage  56  and a second portion of the air  58  as indicated by arrow  64  is directed or routed into the LP compressor  22 . The ratio between the first portion of air  62  and the second portion of air  64  is commonly known as a bypass ratio. The pressure of the second portion of air  64  is then increased as it is routed through the high pressure (HP) compressor  24  and into the combustor  26 , where it is mixed with fuel and burned to provide combustion gases  66 . 
     The combustion gases  66  are routed through the HP turbine  28  where a portion of thermal and/or kinetic energy from the combustion gases  66  is extracted via sequential stages of HP turbine stator vanes  68  that are coupled to the outer casing  18  and HP turbine rotor blades  70  that are coupled to the HP shaft or spool  34 , thus causing the HP shaft or spool  34  to rotate, thereby supporting operation of the HP compressor  24 . The combustion gases  66  are then routed through the LP turbine  30  where a second portion of thermal and kinetic energy is extracted from the combustion gases  66  via sequential stages of LP turbine stator vanes  72  that are coupled to the outer casing  18  and LP turbine rotor blades  74  that are coupled to the LP shaft or spool  36 , thus causing the LP shaft or spool  36  to rotate, thereby supporting operation of the LP compressor  22  and/or rotation of the fan  38 . 
     The combustion gases  66  are subsequently routed through the jet exhaust nozzle section  32  of the core turbine engine  16  to provide propulsive thrust. Simultaneously, the pressure of the first portion of air  62  is substantially increased as the first portion of air  62  is routed through the bypass airflow passage  56  before it is exhausted from a fan nozzle exhaust section  76  of the turbofan  10 , also providing propulsive thrust. The HP turbine  28 , the LP turbine  30 , and the jet exhaust nozzle section  32  at least partially define a hot gas path  78  for routing the combustion gases  66  through the core turbine engine  16 . 
     It will be appreciated that, although described with respect to turbofan  10  having core turbine engine  16 , the present subject matter may be applicable to other types of turbomachinery. For example, the present subject matter may be suitable for use with or in turboprops, turboshafts, turbojets, industrial and marine gas turbine engines, and/or auxiliary power units. 
       FIG. 2  provides a schematic view of the turbofan  10  of  FIG. 1  and depicts a control system  100  thereof. For this embodiment, the control system  100  can be a compressor active stability management (CASM) system. As shown, the control system  100  can include a number of sensors for sensing various operating characteristics of the engine, e.g., during operation of the engine. Particularly, the control system  100  includes a sensor  110  operable to sense an operating characteristic associated with a compressor element of the LP compressor or booster  22 . For this embodiment, the sensor  110  is a pressure sensor operable to sense pressure fluctuations at a compressor element of the booster  22 . Thus, the operating characteristic is pressure or the pressure at a compressor element of the booster  22  in this example. Further, for this embodiment, the sensor  110  is a high bandwidth pressure sensor. However, in alternative embodiments, the sensor  110  can sense other operating characteristics associated with the compressor element of the booster  22 , such as e.g., temperature. As used herein, the term “compressor element” can mean an array of vanes or blades in a particular stage of a compressor, a single vane or blade of a stage of the compressor, multiple stages of vanes and blades, and/or the compressor as a whole. 
     The control system  100  also includes a sensor  112  operable to sense an operating characteristic associated with a compressor element of the HP compressor  24 . For this embodiment, the sensor  112  is a pressure sensor operable to sense pressure fluctuations at a compressor element of the HP compressor  24 . Further, the sensor  112  is a high bandwidth pressure sensor. However, in alternative embodiments, the sensor  112  can sense other operating characteristics of the HP compressor  24 , such as e.g., temperature. The control system  100  also includes a sensor  114  operable to sense an operating characteristic associated with a compressor element of the fan  38 . For this embodiment, the sensor  114  is a pressure sensor operable to sense pressure fluctuations at a compressor element of the fan  38 , such as e.g., pressure fluctuations at the fan blades  40 . Further, the sensor  114  is a high bandwidth pressure sensor. However, in alternative embodiments, the sensor  114  can sense other operating characteristics of the fan  38 , such as e.g., temperature. 
     In addition, the control system  100  of the turbofan  10  includes one or more computing devices for controlling operation of the turbofan  10 . Particularly, for this embodiment, the control system  100  includes a controller  120  configured for controlling various aspects of the turbofan  10 . Specifically, the controller  120  is configured to perform one or more operations or functions for stability control of one or both compressors  22 ,  24  of the turbofan  10  and for providing improved performance of the engine during its life cycle. The controller  120  can include one or more memory devices and one or more processing devices. The one or more memory devices can store computer-readable instructions that can be executed by the one or more processing devices to perform operations. Particularly, the controller  120  can be configured in substantially the same manner as one of the computing devices of the exemplary computing system  500  described below with reference to  FIG. 13  (and can be configured to perform one or more of the operations described herein, such as some or all of the operations of the methods ( 400 ), ( 450 ) described herein). The controller  120  can be a system of controllers or a single controller. In some embodiments, the controller  120  can be an engine controller. The controller  120  can be, for example, an Electronic Engine Controller (EEC) or an Electronic Control Unit (ECU). The controller  120  can be operated as a control device of a Full Authority Digital Engine Control (FADEC) system of a vehicle, such as e.g., an aerial vehicle. 
     The controller  120  is communicatively coupled with various components of the turbofan engine  10  as shown in  FIG. 2  (e.g., via one or more suitable wired and/or wireless communication links). For this embodiment, the controller  120  is communicatively coupled with the sensors  110 ,  112 ,  114 , fuel metering device  92  of the fuel system  90 , variable geometry components  86  of the LP and HP compressors  22 ,  24 , as well as the actuation member  44  of the fan  38 . As will be appreciated the controller  120  can be communicatively coupled with other components of the turbofan engine  10 , to other computing systems or components of a vehicle to which the turbofan engine  10  is mounted, to various remote computing systems  200  or components (e.g., a ground system, a maintenance entity, an analytics entity, etc.), as well as other components or computing systems. 
       FIG. 3  provides a schematic view of the control system  100  and depicts various models and control logic of the controller  120 . As shown, the controller  120  includes control logic  124 , a machine-learned model  126 , and a data consolidation model  128 . In some embodiments, the controller  120  can optionally not include the data consolidation model  128 . 
     Generally, the machine-learned model  126  of the controller  120  is configured to receive raw data  130  from a high frequency sensor, such as e.g., one or more of sensors  110 ,  112 ,  114  and translate or convert the high frequency sensor data  130  into a stall margin remaining  132  (denoted as “SMR” in  FIG. 3 ) of a compressor element in real time with significant improvements in signal accuracy and variation over conventional methods. Thus, in some embodiments, at least one input to the machine-learned model  126  is high frequency, high bandwidth sensor data  130  and the output of the machine-learned model  126  is a stall margin remaining  132  of a compressor element. Improvements in stall margin remaining signal accuracy and processing speed achieved by use of the machine-learned model  126  can provide enhanced active stability management of the compressor element. 
     In some embodiments, the machine-learned model  126  is a neural network. For instance, in some embodiments, the machined-learned model  126  is a recurrent neural network (RNN), e.g., a long short-term memory recurrent neural network (LSTM). In yet other embodiments, the machined-learned model  126  is a convolutional neural network (CNN). In some further embodiments, the machine-learned model can be another type of neural network. Moreover, in some embodiments, the machine-learned model  126  can be a model structured as one of a linear discriminant analysis model, a partial least squares discriminant analysis model, a support vector machine model, a random tree model, a logistic regression model, a naive Bayes model, a K-nearest neighbor model, a quadratic discriminant analysis model, an anomaly detection model, a boosted and bagged decision tree model, a C4.5 model, and/or a k-means model. 
     In some embodiments, supervised training techniques can be used to train the machine-learned model  126 . For instance, supervised training techniques can be used on a labeled training data set to train the machine-learned model  126 . The labeled training data set can include raw data from a high frequency sensor at known operating conditions of the engine and expected stall margin remaining calculations that correspond to the raw data. The training process iterates until the error delta that describes a difference between the output of the machine-learned model  126  and the expected stall margin remaining output is within a threshold percentage. In this way, the machine-learned model is constructed within arbitrarily good precision to the training data set. The machine-learned model  126  can be trained using the labeled training data and can also be validated via a validation data set. In yet other embodiments, the machine-learned model  126  can be trained using one or more unsupervised training techniques. Once trained, and in some instances validated, the machine-learned model  126  is operable to receive high bandwidth, high frequency sensor data and predict compressor element stall margin with significant improvements in signal accuracy and variation over conventional methods, as noted above. 
     For this embodiment, the controller  120  also includes data consolidation model  128 . The data consolidation model  128  can be a single model or a plurality of models. The data consolidation model  128  can include physics-based models, mathematical models, statistical models, a combination thereof, etc. Generally, the data consolidation model  128  of the controller  120  is configured to receive raw data  130  from a high frequency sensor, such as e.g., one or more of sensors  110 ,  112 ,  114 , and ultimately output a correlation value  134 , denoted as “CV” in  FIG. 3 . The correlation value  134  can be routed to and input into the machine-learned model  126 . Thus, in some embodiments, the machine-learned model  126  is a multi-input machine-learned model. Stated generally, the correlation value  134  can provide insight or information pertaining to the physics or behavior of the compressor element. For instance, as one example, the correlation value can be representative of how well or close the raw data signal from one revolution of the engine matches the raw data signal from a subsequent or second revolution of the engine. For instance, the data consolidation model  128  can use one or more Fourier Transforms to determine various characteristics of the raw data signals so that the signals can be compared more easily and accurately. Generally, the more comparable the raw data signals between revolutions, the healthier the compressor element; in contrast, the less comparable the raw data signals from one revolution to the next, the less healthier the compressor element. The machine-learned model  126  can use the correlation value  134  output by the data consolidation model in addition to its machine-learned algorithm to determine the stall margin remaining  132  of the compressor element. The insight into the physics of the compressor element can provide enhanced accuracy, among other benefits and advantages. 
     Further, the controller  120  also includes control logic  124 . As shown, the control logic  124  receives the determined stall margin remaining  132  and can cause adjustment of one or more engine systems  95  based at least in part on the determined stall margin remaining  132 . For instance, based on the determined stall margin remaining  132 , the control logic  124  can generate one or more control commands  136  indicative of instructions for adjusting one or more of the engine systems  95  of the engine. As shown in  FIG. 3 , the one or more engine systems  95  can include one or more variable geometry components  86 , fuel metering device  92  ( FIG. 2 ) of fuel system  90 , fan  38 , and/or bleed valves  88  of the engine. As the stall margin remaining signal  132  is processed with improved speed and accuracy via use of the machine-learned model  126 , the control logic  124  can more accurately determine optimal control commands  136  for controlling the engine system  95  to ultimately provide improved stall prevention capabilities and operation closer to the stall line for a given engine speed. More aggressive compressor active stability management and control logic  124  can translate to increased thrust for engines. 
     With reference generally to  FIGS. 2 and 3 , the control system  100  can control the turbofan engine  10  to operate closer to stall with improved performance to achieve the benefits and advantages described above in the following example manner. As depicted, the controller  120  is configured to receive data  130  indicative of an operating characteristic associated with a compressor element of the engine. For instance, the controller  120  can receive data  130  from sensor  110  that is indicative of an operating characteristic associated with a compressor element of the booster  22 . Additionally or alternatively, the controller  120  can receive data  130  from sensor  112  that is indicative of an operating characteristic associated with a compressor element of the HP compressor  24 . Additionally or alternatively, the controller  120  can receive data  130  from sensor  114  that is indicative of an operating characteristic associated with a compressor element of the fan  38 . The data  130  can be routed from one or more of the sensors  110 ,  112 ,  114  to the controller  120  via a suitable wired or wireless communication link. The raw data  130  can be routed to the controller  120  continuously during operation of the turbofan  10 . The data  130  can be routed as an analog and/or digital signal. For instance,  FIG. 4  graphically depicts a raw data signal  130  as a function of time that can be received by the controller  120 . The raw data signal  130  depicted in  FIG. 4  is an analog signal. The raw data signal  130  can be indicative of an operating characteristic associated with the compressor element. 
     For instance, in some example embodiments, the operating characteristic associated with the compressor element is at least one of a pressure and a temperature at the compressor element. As noted above, the sensors  110 ,  112 ,  114  can be high bandwidth, high frequency pressure sensors. In alternative embodiments, the sensors  110 ,  112 ,  114  can be high bandwidth, high frequency temperature sensors. In some example embodiments, the compressor element can be an array of compressor blades  84  ( FIG. 1 ) of one of the compressors  22 ,  24  and the operating characteristic can be the pressure at the array of compressor blades  84 . Particularly, the pressure sensor can be operable to sense pressure fluctuations caused by blade tip vortices as the blades of the array rotate past the sensor. In yet another example, the compressor element can be the compressor  22  or the compressor  24  and the operating characteristic can be the pressure at the compressor  22  or the compressor  24 . As yet another example, the compressor element can be a single compressor blade  84  of one of the compressors  22 ,  24  and the operating characteristic can be the pressure at the single compressor blade  84 . In a further example, the compressor element can be the fan  38  or a single blade  40  of the fan  38  and the operating characteristic can be the pressure at the blades  40 . 
     The raw data  130  can be routed directly to the machine-learned model  126 , and optionally, directly to the data consolidation model  128  as well. The controller  120  is configured to determine, by the machine-learned model  126 , the stall margin remaining  132  of the compressor element based at least in part on the received data  130 . For instance, the machine-learned algorithm of the machine-learned model  126  can receive the raw data  130  and can generate or determine a highly accurate and high bandwidth stall margin remaining signal  132 . As noted above, the machine-learned model  126  can be an RNN or CNN model. In some embodiments, the machine-learned model  126  is trained to recognize one or more characteristics of the AC component of the received raw data  130 . For instance, the machine-learned model  126  can recognize frequency characteristics, amplitude characteristics, and/or other pattern or characteristics of the AC component of the raw data sensor signal. The machine-learned model can be trained to associate the frequency characteristics, amplitude characteristics, and/or other pattern or characteristics of the AC component of the raw data sensor signal with a stall margin remaining of the compressor element. In embodiments in which the machine-learned model  126  is an RNN, the RNN can use its internal state or memory to process sequences of inputs to accurately and quickly determine the stall margin remaining  132 . Additionally, in yet other embodiments, the machine-learned model  126  is trained to recognize one or more characteristics of the DC component of the received raw data signal  130  to enhance the accuracy of the determined stall margin remaining signal  132 . The determined stall margin remaining signal  132  can be output by the model with equal to or greater than about ten times (10×) improvement in signal variation over conventional methods for determining stall margin remaining. 
       FIG. 5  provides a graph depicting the stall margin remaining of the compressor element versus time during a transient event for various control schemes. The transient event can be a burst, for example. As depicted, the control schemes depicted in  FIG. 5  include a “no CASM” system control scheme NO_CASM, a conventional CASM system control scheme C_CASM, and in accordance with inventive aspects of the present disclosure, a machine learning CASM system control scheme ML_CASM. 
     For the NO_CASM control scheme, there is no CASM system used to prevent stall during the transient event as depicted in  FIG. 5 . As shown, the stall margin remaining for the compressor element initially decreases rapidly and eventually settles at a required margin, denoted as R_TSM, during the transient event. Notably, with the no CASM system, stall is prevented as the system operates with a relatively high required margin, or R_TSM. However, as the system settles relatively far away from stall, performance during the transient event is not ideal. 
     For the C_CASM control scheme, a conventional CASM system is used to prevent stall during the transient event and to drive the stall margin remaining to a stall margin remaining target, denoted as C_TSM. As illustrated, the stall margin remaining for the compressor element decreases rapidly initially and slightly undershoots the target stall margin C_TSM during the transient event. Thereafter, the conventional system C_CASM actively drives the stall margin remaining of the compressor element to the target stall margin C_TSM, e.g., by adjusting the fuel to the engine. Thus, for this example, stall is prevented utilizing the conventional system C_CASM; however, notably, some operability margin is lost as represented by the spacing between the stall margin remaining for the C_CASM and the stall line. 
     For the ML_CASM control scheme, a machine learning control scheme in accordance with the present disclosure is utilized to prevent stall during the transient event and to drive the stall margin remaining as close as possible to the Stall line, e.g., to increase the operability margin of the compressor element, and more broadly, the engine. Particularly, the controller  120  ( FIG. 3 ) can set a target stall margin much closer to Stall, denoted by ML_TSM. As illustrated, the stall margin remaining for the compressor element initially decreases rapidly and then smoothly settles on the target stall margin ML_CASM during the transient event. Thus, for this example, stall is prevented utilizing the machine learning system ML_CASM during the transient event and the operability margin of the compressor element is improved over the C_CASM and NO_CASM system control schemes, as highlighted by the proximity of the stall margin remaining relative to Stall achieved with the ML_CASM system compared to the proximity of the stall margin remaining relative to Stall achieved by the conventional C_CASM and NO_CASM systems. 
     As will be explained below, the stall margin remaining of the compressor element can be determined or generated by the controller  120  by the machine-learned model  126  ( FIG. 3 ) based at least in part on received high frequency sensor data, the controller  120  can monitor the stall margin remaining and actively drive the stall margin remaining  132  to about the stall line or at some margin or target from stall, e.g., the ML_TSM. For instance, the controller  120  can cause adjustment of the one or more engine systems  95  ( FIG. 3 ) based at least in part on the determined stall margin remaining. For example, the controller  120  can cause adjustment of one or more variable geometries of the engine to track the set target stall margin. Additionally or alternatively, fuel flow to the engine can be changed, e.g., reduced. Additionally or alternatively, other methods of changing the mass flow through the compressor can be utilized. 
       FIG. 6  provides a block diagram of an example control loop for active stability management of a compressor element in accordance with an example embodiment of the present subject matter. As shown in  FIG. 6 , as the engine operates, one or more of the sensors  110 ,  112 ,  114  can sense an operating characteristic associated with the compressor element of the engine. For instance, the operating characteristic can be the pressure at the compressor element as previously noted. The high bandwidth, high frequency raw data  130  indicative of the operating characteristic of the compressor element can be routed to the machine-learned model  126  of the controller  120 . The machine-learned model  126  can convert the raw high frequency pressure data  130  into the stall margin remaining signal  132  as described above. That is, the machine-learned model  126  can predict the stall margin remaining  132  based at least in part on the received data  130 . The stall margin remaining signal  132  is then routed to a summation block  140 . At the summation block  140 , the controller  120  can determine a stall margin error  138  based at least in part on the determined stall margin remaining  132  and the target stall margin  134  set by the controller  120 , e.g., automatically or via a user input. For instance, the stall margin error  138  can be calculated as the difference between the determined stall margin remaining  132  and the target stall margin  134 . The stall margin error  138  can be representative of the margin between the stall margin remaining signal and the target stall margin. For instance, with reference to  FIG. 5 , the stall margin error can be representative of the margin or difference between the stall margin remaining of the ML_CASM line and the target stall margin ML_TSM. 
     The determined stall margin error  138  can then be input into the control logic  124 . The control logic  124  can output one or more control commands  136  based at least in part on the determined stall margin error  138 . For instance, the output control commands  136  can provide instructions for causing adjustment of the one or more engine systems  95  such that the stall margin error  138  is driven to zero (0), or as closely as possible to zero ( 0 ). In this way, the stall margin remaining signal  132  can be driven to the target stall margin  134 , e.g., during a transient event as shown in  FIG. 5 . Thus, the one or more processing devices of the controller  120  are configured to cause adjustment of the one or more engine systems  95  based at least in part on the determined stall margin error  138 , which ultimately changes the mass flow through the compressor element and consequently drives the stall margin remaining signal  132  to the target stall margin  134 . 
     In some embodiments, in determining, by the machine-learned model  126 , the stall margin remaining  132  of the compressor element based at least in part on the received data  130 , the one or more processing devices of the controller  120  are configured to splice the received data  130  into data subsets indicative of the operating characteristic of the compressor element over one or more revolutions of the engine. In such embodiments, the stall margin remaining  132  of the compressor element is determined based at least in part on the data subsets indicative of the operating characteristic of the compressor element over one or more revolutions of the engine. 
     By way of example,  FIG. 7  graphically depicts raw data signal  130  spliced into data subsets by revolution of the engine as well as the stall margin remaining signal  132  determined based at least in part on the spliced data signal. As depicted in  FIG. 7 , the one or more processing devices of the controller  120  can splice the received data  130  into data subsets by revolution of the engine. For instance, the data  130  can be spliced into a first data subset DS 1   REV  for a first revolution of the engine, a second data subset DS 2   REV  for a subsequent second revolution of the engine, and a third data subset DS 3   REV  for a subsequent third revolution of the engine. That is, the data is spliced into subsets based on the time it takes the engine to revolve around its axis of rotation. In this way, the revolutions of the engine and data splicing are synchronous. The revolution of the engine can be taken as the core speed (N 2 ) of the engine if the compressor element is operatively coupled with the HP shaft  34  ( FIG. 2 ) or can be taken as the fan speed (N 1 ) of the engine if the compressor element is operatively coupled with the LP shaft  36  or fan  38 . 
     For each data subset DS 1   REV , DS 2   REV , DS 3   REV  associated with a revolution of the engine, a corresponding stall margin remaining data point is determined by the machine-learned model  126 . For this example, a stall margin remaining SM 1  can be determined based at least in part on the spliced first data subset DS 1   REV  associated with the first revolution, a stall margin remaining SM 2  can be determined based at least in part on the spliced second data subset DS 2   REV  associated with the second revolution, and a stall margin remaining SM 3  can be determined based at least in part on the spliced third data subset DS 3   REV  associated with the third revolution. The data points SM 1 , SM 2 , and SM 3  can be plotted over time. The determined stall margin remaining data points SM 1 , SM 2 , and SM 3  can be routed to the control logic  124  ( FIG. 3 ) as they are determined such that the stall margin remaining can actively be driven to the stall margin remaining target  134  (e.g., so that the stall margin error  138  can be driven to zero (0)). 
     In yet other embodiments, in determining, by the machine-learned model  126 , the stall margin remaining  132  of the compressor element based at least in part on the received data  130 , the one or more processing devices of the controller  120  are configured to splice the received data  130  into data subsets indicative of the operating characteristic of the compressor element over an update interval that is synchronized with an update rate of the one or more computing devices, or in this example, the controller  120  and/or FADEC system of which the controller  120  is the control device thereof. For instance, if the FADEC has an update rate of X milliseconds, the raw data  130  can be spliced at intervals of X milliseconds. In such embodiments, the stall margin remaining  132  of the compressor element is determined based at least in part on the data subsets indicative of the operating characteristic of the compressor element over an update interval that is synchronized with an update rate of the one or more computing devices. 
     By way of example,  FIG. 8  graphically depicts a raw data signal  130  spliced by an update interval that is synchronized with an update rate of the one or more computing devices as a function of time as well as the stall margin remaining signal  132  determined based at least in part on the spliced data signal. As depicted in  FIG. 8 , the one or more computing devices can splice the received data  130  into data subsets by an update interval that is synchronized with an update rate (e.g., time step) of the one or more computing devices. For instance, the data  130  can be spliced into a first data subset DS 1   T  for a first update interval, a second data subset DS 2   T  for a subsequent second update interval (which can be the same or a different amount of time as the first update interval), and a third data subset DS 3   T  for a subsequent third update interval (which can be the same or a different amount of time as the first update interval and/or the second update interval). That is, the data  130  is spliced into subsets based on the time it takes the one or more computing devices to refresh or update. The update rate of the one or more computing devices can be fixed or variable, and consequently, the update interval can also be fixed or variable depending on the update rate. 
     As shown in  FIG. 8 , for each data subset DS 1   T , DS 2   T , DS 3   T  associated with an update interval, a corresponding stall margin remaining data point is determined by the machine-learned model  126 . For this example, a stall margin remaining SM 1  can be determined based at least in part on the spliced first data subset DS 1   T  associated with the first update interval or time period, a stall margin remaining SM 2  can be determined based at least in part on the spliced second data subset DS 2   T  associated with the second update interval, and a stall margin remaining SM 3  can be determined based at least in part on the spliced third data subset DS 3   T  associated with the third update interval. The data points SM 1 , SM 2 , and SM 3  can be plotted as a function of time. The determined stall margin remaining data points SM 1 , SM 2 , and SM 3  can be routed to the control logic  124  ( FIG. 3 ) as they are determined such that the stall margin remaining can actively be driven to the stall margin remaining target  134  (e.g., so that the stall margin error  138  can be driven to zero (0)). There are certain advantages in splicing the data by update intervals as previously noted. 
     Advantageously, in embodiments in which the one or more processing devices of the controller  120  are configured to splice the received data  130  into data subsets indicative of the operating characteristic of the compressor element over an update interval that is synchronized with an update rate of the one or more computing devices, synchronizing the splicing of the data  130  into data subsets with the update rate provides for efficient computing and predictable timing of the spliced data, which can provide for easier and more effective training of the machine-learned model  126 . Further, in embodiments in which the one or more processing devices of the controller  120  are configured to splice the received data  130  into data subsets indicative of the operating characteristic of the compressor element over one or more revolutions of the engine, determining the stall margin remaining based on the data subsets can require more intense processing resources (e.g., than the synchronized method noted above) as the revolutions of the engine may or may not synch with the update rate of the controller  120  and/or FADEC system of which the controller  120  is the control device thereof, but can provide more accurate results (i.e., less signal variance). 
     Further, in some embodiments, the machine-learned model  126  is a multi-input machine-learned model. In such embodiments, as shown in  FIG. 3 , the one or more processing devices of the controller  120  are configured to determine, by the data consolidation model  128 , a correlation value  134  based at least in part on the received data  130 . Thereafter, the machine-learned model  126  then receives the correlation value  134  as shown. In such embodiments, the stall margin remaining  132  of the compressor element is determined, by the machine-learned model  126 , based at least in part on the correlation value  134 . For instance, as shown in  FIG. 3 , the machine-learned model  126  can apply a machine-learned algorithm to the raw data  130  received from one or more sensors (e.g., sensors  110 ,  112 ,  114 ) and the correlation value  134  received from the data consolidation model  128 . As noted previously, the correlation value  134  can provide insight or information pertaining to the physics of the compressor element (that would not otherwise be available if the stall margin remaining was determined solely on the raw sensor data  130 ). 
     In some embodiments, the correlation value  134  is descriptive of a correlation between a first signal indicative of the operating characteristic of the compressor element over one or more first revolutions of the engine and a second signal indicative of the operating characteristic of the compressor element over one or more second revolutions of the engine. By way of example,  FIG. 9  provides a graph depicting a first signal (Rev  1 ) and a second signal (Rev  2 ) overlaying the first signal Rev  1  over time. For this example, the first signal (Rev  1 ) is indicative of the pressure of the compressor element over a first revolution of the engine and the second signal (Rev  2 ) is indicative of the pressure of the compressor element over a subsequent second revolution of the engine. As noted above, the two signals overlay one another over time. The correlation value  134  is a calculated mathematical value that represents how similar two arrays of data are, e.g., how similar the pattern of the first signal (Rev  1 ) and second signal (Rev  2 ) are to one another. The data consolidation model  128  can use one or more Fourier Transforms to determine various characteristics of the first signal (Rev  1 ) and the second signal (Rev  2 ) signals so that the patterns of the signals can be compared more easily and accurately. As noted previously, the more comparable the patterns of the signals, the healthier the compressor element; in contrast, the less comparable the patterns of the signals, the less healthier the compressor element. The insight into the physics of the compressor element can enhance the machine-learned model&#39;s ability to quickly and accurately process the stall margin remaining  132 , among other benefits and advantages. 
     In yet other embodiments, the correlation value  134  is descriptive of a correlation between a first signal indicative of the operating characteristic of the compressor element over a first time interval that is synchronized with an update rate of the one or more computing devices and a second signal indicative of the operating characteristic of the compressor element over a second time interval that is synchronized with an update rate of the one or more computing devices, or in this example, the controller  120  and/or FADEC system of which the controller  120  is the control device thereof. In such embodiments, the second time interval is later in time than the first time interval. A correlation value computed by the data consolidation model  128  in such a manner provides similar advantages as noted above. 
     By utilizing a machine-learned model to determine the stall margin remaining of a compressor element based on data from a high frequency sensor, significant improvements in signal accuracy and variation can be achieved compared to conventional methods for determining stall margin remaining. For instance,  FIG. 10  provides a graph depicting the stall margin remaining output by the machine-learned model of the present disclosure versus the actual stall margin remaining of the compressor element for a given engine power setting and also depicts the stall margin remaining output by a conventional control system versus the actual stall margin remaining of the compressor element for the same given engine power setting. 
     As shown in  FIG. 10 , a mean of the stall margin remaining derived using the machine learning signal processing control scheme described herein, denoted as ML_Mean, is shown relative to the ideal stall margin remaining, denoted as Ideal. Further, as depicted, a plus 2 Sigma standard deviation, denoted as ML_+2σ, and a negative 2 Sigma standard deviation, denoted as ML_2σ, are shown relative to the mean ML_Mean. Notably, the mean ML_Mean (e.g., the stall margin remaining output from the machine-learned model  126 ) nearly overlays the ideal stall margin remaining Ideal. Indeed, the machine-learned model  126  can generate or output a very accurate stall margin remaining signal. Moreover, the machine-learn model  126  can output the stall margin remaining with less signal variation compared to conventional methods. For instance, as shown in  FIG. 10 , the plus 2 Sigma standard deviation ML_+2σ and negative 2 Sigma standard deviation ML_−2σ nearly overlay the mean ML_Mean, indicating that there is little variance in the stall margin remaining signal output by the machine-learned model  126 . 
     In comparison, as further shown in  FIG. 10 , conventional control methods have not produced stall margin remaining signals with the same level of accuracy for active compressor stability management and also require too much processing time. In  FIG. 10 , a mean associated with the stall margin remaining calculated or predicted by conventional methods is denoted as C_Mean and is shown relative to the ideal stall margin remaining Ideal. Further, as depicted in  FIG. 10 , a plus 2 Sigma standard deviation, denoted as C_+2σ, and a negative 2 Sigma standard deviation, denoted as C_−2σ, are shown relative to the mean C_Mean. Notably, the mean C_Mean of the predicted/calculated stall margin remaining (i.e., the stall margin remaining output from a conventional processing scheme) is offset from the ideal stall margin remaining Ideal by a considerable margin. Further, as shown in  FIG. 10 , the plus 2 Sigma standard deviation C_+2σ and negative 2 Sigma standard deviation C_−2σ are in some instances nearly ±10 pts from the mean C_Mean of the predicted stall margin remaining signal, indicating that there is significant variance in the predicted stall margin remaining signal output by the conventional processing scheme. As will be appreciated by the graph of  FIG. 10 , significant improvements in signal accuracy and variation can be achieved by processing high frequency sensor data to determine a stall margin remaining utilizing the machine-learned model described herein compared to conventional control schemes. 
       FIG. 11  provides a flow diagram of an exemplary method ( 400 ) for controlling a turbine engine in accordance with an embodiment of the present subject matter. For instance, the method ( 400 ) can be implemented to control the turbofan  10  of  FIGS. 1 and 2 . However, the method ( 400 ) can be implemented to control other engines having a compressor element as well. Some or all of the method ( 400 ) can be implemented by the control system  100  described herein. In addition, it will be appreciated that exemplary method ( 400 ) can be modified, adapted, expanded, rearranged and/or omitted in various ways without deviating from the scope of the present subject matter. 
     At ( 402 ), the method ( 400 ) includes operating an engine having a compressor element. For instance, the engine can be a gas turbine engine, such as e.g., the turbofan  10  of  FIGS. 1 and 2 . Moreover, the gas turbine engine can be another suitable type of gas turbine engine, such as, e.g., a turboprop, a turboshaft, a jet engine, an aeroderivative gas turbine engine, a marine gas turbine engine, etc. In yet other embodiments, the engine can be a different type of turbine engine, such as e.g., a steam turbine engine. The compressor element can be any suitable rotatable compressor. For instance, the compressor element can be the booster  22 , the HP compressor  24 , and/or the fan  38  of the turbofan  10  of  FIGS. 1 and 2 . In some implementations, the compressor element can be an annular array of compressor blades or stator vanes. In yet other embodiments, the compressor element can be a single blade or vane of an annular array. 
     At ( 404 ), the method ( 400 ) includes receiving, by one or more computing devices, data indicative of an operating characteristic associated with the compressor element of the engine. For instance, the one or more computing devices can be the engine controller  120  described herein. Moreover, the data indicative of the operating characteristics associated with the compressor element of the engine can be at least one of a pressure at the compressor element and a temperature at the compressor element. The data can be routed from one or more sensors positioned at or proximate the compressor element. For instance, the one or more sensors can be high bandwidth pressure sensors. For example, with reference to  FIG. 2 , the sensors can include the sensor  110  positioned along the booster  22  of the turbofan  10 , the sensor  112  positioned along the HP compressor  24 , and/or the sensor  114  positioned along the fan  38 . The data can be received as an analog signal, e.g., as shown in  FIG. 4 . The frequency at which the sensors read the operating characteristic associated with the compressor element (e.g., the pressure fluctuations at the compressor element) can be on the order of two thousand times (2,000×) the update rate of the one or more computing devices, e.g., the controller  120  of the FADEC. 
     At ( 406 ), the method ( 400 ) includes determining, by a machine-learned model of the one or more computing devices, a stall margin remaining of the compressor element based at least in part on the received data. For instance, the machine-learned model  126  of the one or more computing devices can convert the raw sensor data indicative of the operating characteristic associated with the compressor element into the stall margin remaining of the compressor element, e.g., as shown by the ML_CASM line in  FIG. 5 . A machine-learned algorithm of the machine-learned model  126  can determine a highly accurate and high bandwidth stall margin remaining signal  132  compared to conventional processing schemes, e.g., as shown in  FIG. 10 . 
     In some implementations, the machine-learned model  126  is a neural network. For instance, in some implementations, the machined-learned model  126  is one of a recurrent neural network (RNN) and a convolutional neural network (CNN). In some implementations, the machine-learned model  126  is trained to recognize one or more characteristics of the AC component of the received raw data  130 . For instance, the machine-learned model  126  can recognize frequency characteristics, amplitude characteristics, and/or other pattern or characteristics of the AC component of the raw data sensor signal. The machine-learned model can be trained to associate the frequency characteristics, amplitude characteristics, and/or other pattern or characteristics of the AC component of the raw data sensor signal with a stall margin remaining of the compressor element. Additionally, in yet other implementations, the machine-learned model  126  is trained to recognize one or more characteristics of the DC component of the received raw data signal  130  to enhance the accuracy of the determined stall margin remaining signal  132 . The determined stall margin remaining signal  132  can be output by the model with equal to or greater than about ten times (10×) improvement in signal variation over conventional methods for determining stall margin remaining. 
     In some implementations, the method ( 400 ) includes splicing the received data into data subsets each indicative of the operating characteristic of the compressor element over a revolution of the engine. In such implementations, the stall margin remaining of the compressor element is determined, by the machine-learned model of the one or more computing devices, based at least in part on the data subsets each indicative of the operating characteristic of the compressor element over a revolution of the engine. As depicted in  FIG. 7 , the one or more computing devices can splice the received data  130  into data subsets by revolution of the engine. For instance, the data  130  can be spliced into a first data subset DS 1   REV  for a first revolution of the engine, a second data subset DS 2   REV  for a subsequent second revolution of the engine, and a third data subset DS 3   REV  for a subsequent third revolution of the engine. That is, the data can be spliced into subsets based on the time it takes the engine to revolve around its axis of rotation. Thus, in some implementations, the splicing of the data is synchronous with the revolutions of the engine. 
     As noted previously, for each data subset DS 1   REV , DS 2   REV , DS 3   REV  associated with a revolution of the engine, a corresponding stall margin remaining data point is determined by the machine-learned model  126 . For this example, a stall margin remaining SM 1  can be determined based at least in part on the spliced first data subset DS 1   REV  associated with the first revolution, a stall margin remaining SM 2  can be determined based at least in part on the spliced second data subset DS 2   REV  associated with the second revolution, and a stall margin remaining SM 3  can be determined based at least in part on the spliced third data subset DS 3   REV  associated with the third revolution. The data points SM 1 , SM 2 , and SM 3  can be plotted as a function of time. The determined stall margin remaining data points SM 1 , SM 2 , and SM 3  can be routed to the control logic  124  ( FIG. 3 ) as they are determined such that the stall margin remaining can actively be driven to the stall margin remaining target  134 . As explained previously, the revolution of the engine can be based on the core speed (N 2 ) of the engine if the compressor element is operatively coupled with the HP shaft  34  ( FIG. 2 ) or can be based on the fan speed (N 1 ) of the engine if the compressor element is operatively coupled with the LP shaft  36  or fan  38 . There are certain advantages in splicing the data by revolution of the engine as previously noted. 
     In yet other implementations, the method ( 400 ) includes splicing the received data into data subsets each indicative of the operating characteristic of the compressor element over an update interval that is synchronized with an update rate of the one or more computing devices. In such implementations, the stall margin remaining of the compressor element is determined, by the machine-learned model of the one or more computing devices, based at least in part on the data subsets each indicative of the operating characteristic of the compressor element over an update interval that is synchronized with an update rate of the one or more computing devices. As depicted in  FIG. 8 , the one or more computing devices can splice the received data  130  into data subsets by an update interval that is synchronized with an update rate of the one or more computing devices. For instance, the data  130  can be spliced into a first data subset DS 1   T  for a first update interval, a second data subset DS 2   T  for a subsequent second update interval (which can be the same or a different amount of time as the first update interval), and a third data subset DS 3   T  for a subsequent third update interval (which can be the same or a different amount of time as the first update interval and/or the second update interval). The update rate of the one or more computing devices can be fixed or variable, and consequently, the update interval can also be fixed or variable depending on the update rate. 
     As shown in  FIG. 8 , for each data subset DS 1   T , DS 2   T , DS 3   T  associated with an update interval, a corresponding stall margin remaining data point is determined by the machine-learned model  126 . For this example, a stall margin remaining SM 1  can be determined based at least in part on the spliced first data subset DS 1   T  associated with the first update interval, a stall margin remaining SM 2  can be determined based at least in part on the spliced second data subset DS 2   T  associated with the second update interval, and a stall margin remaining SM 3  can be determined based at least in part on the spliced third data subset DS 3   T  associated with the third update interval. The data points SM 1 , SM 2 , and SM 3  can be plotted as a function of time. The determined stall margin remaining data points SM 1 , SM 2 , and SM 3  can be routed to the control logic  124  ( FIG. 3 ) as they are determined such that the stall margin remaining can actively be driven to the stall margin remaining target  134 . There are certain advantages in splicing the data by update intervals as previously noted. 
     In yet further implementations, the method ( 400 ) includes determining, by a data consolidation model of the one or more computing devices, a correlation value based at least in part on the received data, wherein the correlation value is descriptive of a correlation between a first signal indicative of the operating characteristic of the compressor element over a first time period and a second signal indicative of the operating characteristic of the compressor element over a second time period, the second time period being later in time than the first time period. The method ( 400 ) can also include receiving, by the machine-learned model of the one or more computing devices, the correlation value. In such implementations, the stall margin remaining of the compressor element is determined, by the machine-learned model, based at least in part on the correlation value. 
     For instance, in some implementations, the machine-learned model  126  is a multi-input machine-learned model. In such implementations, as shown in  FIG. 3 , the one or more processing devices of the controller  120  are configured to determine, by the data consolidation model  128 , a correlation value  134  based at least in part on the received data  130 . Thereafter, the machine-learned model  126  can receive the correlation value  134 . In such implementations, the stall margin remaining  132  of the compressor element is determined, by the machine-learned model  126 , based at least in part on the correlation value  134 . For instance, as shown in  FIG. 3 , the machine-learned model  126  can apply a machine-learned algorithm to the raw data  130  received from one or more sensors (e.g., sensors  110 ,  112 ,  114 ) and the correlation value  134  received from the data consolidation model  128 . The correlation value  134  can provide insight or information pertaining to the physics of the compressor element. 
     In some implementations, the correlation value  134  is descriptive of a correlation between a first signal indicative of the operating characteristic of the compressor element over a first time period and a second signal indicative of the operating characteristic of the compressor element over a second time period, the second time period being later in time than the first time period. For instance, in some implementations, the first time period can be associated with a first revolution of the engine and the second time period can be associated with a second revolution of the engine. The second revolution can be a subsequent revolution of the first revolution. Thus, in such implementations, the first signal can be indicative of the operating characteristic of the compressor element over a first revolution of the engine and the second signal can be indicative of the operating characteristic of the compressor element over a subsequent second revolution of the engine. 
     In some implementations, the correlation value is representative of how similar two arrays of data are, e.g., how similar the pattern of the first signal and second signal are to one another. For instance, with reference to  FIG. 9 , a first signal (Rev  1 ) and a second signal (Rev  2 ) are shown overlaying one another over time. For this example, the first signal (Rev  1 ) is indicative of the pressure of the compressor element over a first revolution of the engine and the second signal (Rev  2 ) is indicative of the pressure of the compressor element over a subsequent second revolution of the engine. The data consolidation model  128  can use one or more Fourier Transforms to determine various characteristics of the first signal (Rev  1 ) and the second signal (Rev  2 ) so that the patterns or characteristics of the signals can be compared more easily and accurately. The more comparable the patterns of the signals, the healthier the compressor element; in contrast, the less comparable the patterns of the signals, the less healthier the compressor element. The insight into the physics of the compressor element can enhance the machine-learned model&#39;s ability to quickly and accurately process the stall margin remaining  132 , among other benefits and advantages. 
     In some other implementations, the first time period can be associated with a first fixed interval and the second time period can be associated with a second fixed interval. The second fixed interval can be a subsequent interval of the first fixed interval. Thus, in such implementations, the first signal can be indicative of the operating characteristic of the compressor element over a first fixed interval and the second signal can be indicative of the operating characteristic of the compressor element over a second fixed interval. 
     At ( 408 ), the method ( 400 ) includes causing, by the one or more computing devices, adjustment of one or more engine systems of the engine based at least in part on the determined stall margin remaining. For instance, the one or more engine systems can be adjusted such that the mass flow through or across the compressor element can be changed. In this way, compressor stall can be avoided. 
     For instance, in some implementations, the one or more engine systems can include one or more variable geometry components. Thus, in such implementations, causing, by the one or more computing devices, adjustment of one or more engine systems of the engine based at least in part on the determined stall margin remaining at ( 408 ) includes causing, by the one or more computing devices, adjustment of one or more variable geometry components based at least in part on the determined stall margin remaining. For instance, the variable geometry components can include the variable geometry components  86  depicted in  FIG. 2 . 
     In other implementations, the one or more engine systems can include a fuel system, and more particularly, a fuel metering device of a fuel system. Thus, in such implementations, causing, by the one or more computing devices, adjustment of one or more engine systems of the engine based at least in part on the determined stall margin remaining at ( 408 ) includes causing, by the one or more computing devices, adjustment of a fuel metering device based at least in part on the determined stall margin remaining. For instance, the fuel metering device can include the fuel metering device  92  depicted in  FIG. 2 . When the fuel metering device is adjusted, the mass flow of fuel to the combustor  26  can be changed for a particular power setting. 
     In yet other implementations, the one or more engine systems can include a fan of the engine or a propeller operatively coupled thereto. In such implementations, causing, by the one or more computing devices, adjustment of one or more engine systems of the engine based at least in part on the determined stall margin remaining at ( 408 ) includes causing, by the one or more computing devices, adjustment of a fan of the engine based at least in part on the determined stall margin remaining. For instance, the fan can be the fan  38  depicted in  FIG. 2 . In other implementations, the fan can be a propeller operatively coupled with the engine. In some implementations, adjustment of the fan can be an adjustment of the fan speed for a given power setting. Additionally or alternatively, in some implementations, adjustment of the fan or propeller can be an adjustment of the pitch of the fan or propeller blades about their respective axes. 
     In some further implementations, the one or more engine systems can include one or more bleed valves, e.g., positioned along the core air flowpath of the engine. In such implementations, causing, by the one or more computing devices, adjustment of one or more engine systems of the engine based at least in part on the determined stall margin remaining at ( 408 ) includes causing, by the one or more computing devices, adjustment of a bleed valve of the engine based at least in part on the determined stall margin remaining. For instance, the bleed valve can be the bleed valve  88  depicted in  FIG. 2 . Adjustment of the bleed valve can change the mass flow through the core air flowpath of the engine, and consequently, the compressor element. 
     In some implementations, the method ( 400 ) includes setting, by the one or more computing devices, a target stall margin remaining associated with the compressor element. The controller  120  can set the stall margin target automatically or based on user input, e.g., from a pilot. The stall margin target is set at a higher margin than and is offset from stall so that there is sufficient margin between the operating line of the engine and stall. For instance, as shown in  FIG. 5 , the controller  120  can set the target stall margin ML_TSM such that it is offset from Stall by a desired margin. 
     Further, in some implementations, the method ( 400 ) includes determining a stall margin error based at least in part on the determined stall margin remaining and the target stall margin remaining. In such implementations, the one or more computing devices cause adjustment of the one or more engine systems based at least in part on the determined stall margin error. For instance, as shown in  FIG. 6 , the machine-learned model  126  can output a stall margin remaining  132 . At the summation block  140 , a difference between the output stall margin remaining  132  and the target stall margin can be determined. This difference is the stall margin error  138 . The stall margin error  138  can be routed to the control logic  124  so that instructions for adjusting one or more engine systems  95  can be determined based at least in part on the determined stall margin error  138 . The control logic  124  can generate one or more commands indicative of instructions for adjusting the one or more engine systems  95  based at least in part on the stall margin error  138 . The control commands  136  can be routed to the engine systems  95 , e.g., so that the engine system  95  can change the mass flow through the compressor element so that the stall margin error  138  is ultimately driven to zero (0). Stated differently, the engine systems  95  are adjusted based on the control commands  136  so that the stall margin remaining is driven to the target stall margin  134 . 
       FIG. 12  provides a flow diagram of an exemplary method ( 450 ) for controlling a turbine engine in accordance with an embodiment of the present subject matter. For instance, the method ( 450 ) can be implemented to control the turbofan  10  of  FIGS. 1 and 2 . However, the method ( 450 ) can be implemented to control other types and configurations of turbine engines as well. Some or all of the method ( 450 ) can be implemented by the control system  100  described herein. In addition, it will be appreciated that exemplary method ( 450 ) can be modified, adapted, expanded, rearranged and/or omitted in various ways without deviating from the scope of the present subject matter. 
     At ( 452 ), the method ( 450 ) includes operating the turbine engine. For instance, the engine can be a gas turbine engine, such as e.g., the turbofan  10  of  FIGS. 1 and 2 . Moreover, the gas turbine engine can be another suitable type of gas turbine engine, such as, e.g., a turboprop, a turboshaft, a jet engine, an aeroderivative gas turbine engine, a marine gas turbine engine, etc. In yet other embodiments, the engine can be a different type of turbine engine, such as e.g., a steam turbine engine. 
     At ( 454 ), the method ( 450 ) includes setting, by one or more computing devices, a target signal associated with the turbine engine. For instance, the target signal can be a target stall margin, e.g., as shown in  FIG. 5 . In other implementations, the target signal can be any other target, threshold, or operating line of the engine that is sought, e.g., to improve engine performance, efficiency, thrust, etc. 
     At ( 456 ), the method ( 450 ) includes receiving, by the one or more computing devices, data indicative of an operating characteristic associated with the turbine engine. For instance, the data can be high frequency data received from a high bandwidth, high frequency sensor. The operating characteristic can be any suitable characteristic indicative of the operating conditions of the turbine engine, e.g., pressure, temperature, etc. The data can be received as an analog signal, e.g., as shown in  FIG. 4 . The frequency at which the sensors read the operating characteristic associated with the turbine engine can be on the order of two thousand times (2,000×) the update rate of the one or more computing devices, e.g., the controller  120  of the FADEC system. 
     At ( 458 ), the method ( 450 ) includes predicting, by a machine-learned model of the one or more computing devices, a signal based at least in part on the received data. For instance, the machine-learned model can predict a signal (e.g., stall margin remaining) that can be used for controlling the engine. The machine-learned model can predict the signal based on any of the techniques or methods described herein. Further, machine-learned models running onboard a control processor (e.g., on one or more computing devices of a FADEC system) can be trained to interpret sensor data in real time to predict, convert, or otherwise determine signals relating to, without limitation, component operability or stall margin (as described above), component health (e.g., deterioration, burst pipes, line leaks, etc.), vibration signal processing, and/or sensor fault detection. 
     At ( 460 ), the method ( 450 ) includes determining, by the one or more computing devices, a signal error based at least in part on the predicted signal and the target signal. For instance, the signal error can be calculated as the difference between the predicted signal and the target signal. 
     At ( 462 ), the method ( 450 ) includes causing, by the one or more computing devices, adjustment of one or more engine systems of the turbine engine based at least in part on the determined signal error. The one or more computing devices can cause adjustment of the engine systems to drive the signal error to zero ( 0 ), or stated differently, the one or more computing devices can cause adjustment of the engine systems to drive the predicted signal to the target signal. In this way, the desired performance, efficiency, etc. of the engine can be achieved. In yet other implementations, such as implementations in which the target signal and predicted signals relate to component health, at ( 462 ) the one or more computing devices can cause adjustment of the engine systems to use or operate a particular component (e.g., an unhealthy component) in a different way, e.g., to extend the time-on-wing of the engine). In yet further implementations, such as implementations in which the target signal and predicted signals relate to vibration signal processing, at ( 462 ) the one or more computing devices can cause adjustment of the engine systems, e.g., to reduce the vibration of the engine generally or the particular engine component experiencing an undesirable amount vibration. 
       FIG. 13  provides an example computing system  500  according to example embodiments of the present disclosure. The controller  120  described herein can include various components and perform various functions of the computing system  500  described below, for example. 
     As shown in  FIG. 13 , the computing system  500  can include one or more computing device(s)  510 . The computing device(s)  510  can include one or more processor(s)  510 A and one or more memory device(s)  510 B. The one or more processor(s)  510 A can include any suitable processing device, such as a microprocessor, microcontroller, integrated circuit, logic device, and/or other suitable processing device. The one or more memory device(s)  510 B can include one or more computer-readable media, including, but not limited to, non-transitory computer-readable media, RAM, ROM, hard drives, flash drives, and/or other memory devices. 
     The one or more memory device(s)  510 B can store information accessible by the one or more processor(s)  510 A, including computer-readable instructions  510 C that can be executed by the one or more processor(s)  510 A. The instructions  510 C can be any set of instructions that when executed by the one or more processor(s)  510 A, cause the one or more processor(s)  510 A to perform operations. In some embodiments, the instructions  510 C can be executed by the one or more processor(s)  510 A to cause the one or more processor(s)  510 A to perform operations, such as any of the operations and functions for which the computing system  500  and/or the computing device(s)  510  are configured, such as e.g., operations for controlling an engine as described herein. For instance, the methods ( 400 ), ( 450 ) can be implemented in whole or in part by the computing system  500 . Accordingly, the methods ( 400 ), ( 450 ) can be at least partially a computer-implemented method such that at least some of the steps of the methods ( 400 ), ( 450 ) are performed by one or more computing devices, such as the exemplary computing device  510  of the computing system  500 . The instructions  510 C can be software written in any suitable programming language or can be implemented in hardware. Additionally, and/or alternatively, the instructions  510 C can be executed in logically and/or virtually separate threads on processor(s)  510 A. The memory device(s)  510 B can further store data  510 D that can be accessed by the processor(s)  510 A. For example, the data  510 D can include models, databases, etc. 
     The computing device(s)  510  can also include a network interface  510 E used to communicate, for example, with the other components of system  500  (e.g., via a network). The network interface  510 E can include any suitable components for interfacing with one or more network(s), including for example, transmitters, receivers, ports, controllers, antennas, and/or other suitable components. One or more external devices, such as remote computing system  200  ( FIG. 2 ), can be configured to receive one or more commands or data from the computing device(s)  510  or provide one or more commands or data to the computing device(s)  510 . 
     The technology discussed herein makes reference to computer-based systems and actions taken by and information sent to and from computer-based systems. One of ordinary skill in the art will recognize that the inherent flexibility of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. For instance, processes discussed herein can be implemented using a single computing device or multiple computing devices working in combination. Databases, memory, instructions, and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel. 
     Although specific features of various embodiments may be shown in some drawings and not in others, this is for convenience only. In accordance with the principles of the present disclosure, any feature of a drawing may be referenced and/or claimed in combination with any feature of any other drawing. 
     This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they include structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.