Patent Publication Number: US-2022219838-A1

Title: Model resetting in a turbine engine

Description:
FIELD OF THE INVENTION 
     The present invention relates to updating predictive models in the context of a turbine engine. 
     As a preliminary point, several definitions are given. As illustrated in  FIG. 1 , it is considered within the framework of a turbine engine  1  comprising two successive compressors  2 ,  3  (low pressure  2  and high pressure  3  compressor) followed by a combustion chamber  4 . These definitions are applicable for the entire application. 
     Ps 3  is the static pressure measured or calculated in the plane upstream of the combustion chamber. 
     Xn 12 R is the speed of the low pressure compressor  2 , reduced on the temperature of said compressor T 12  (to avoid temperature variations), expressed in revolutions per minute. 
     PCN 12 R (or N 1  in the case of a direct drive) is the speed of the low pressure compressor  2 , reduced on T 12  (to avoid temperature variations), expressed in percentage of maximum low pressure speed. 
     Xn 25 R is the speed of the high pressure compressor  3 , reduced on T 25  (to avoid temperature variations), expressed in revolutions per minute. 
     PCN 25 R (or N 2 ) is the speed of the high pressure compressor  3 , reduced on the temperature of said compressor T 25  (to avoid temperature variations), expressed in percentage of maximum high pressure speed. 
     PT 2  is the total external pressure (supplied by the aircraft). 
     P 25  is the modeled static pressure in the high pressure compressor. 
     A model is a mathematical law describing the evolution of a physical quantity (parameter) as a function of one or more physical variables. 
     STATE OF THE ART 
     During operation, turbine engines sometimes undergo false pumping detections (stalling of the blades of one of the two compressors) during cruising phase. These events have a strong operational impact (engine endoscopy) and are dangerous. 
     In these two cases, a deviation failure between the two channels Ps 3 , that is to say between the two channels for acquiring the static pressure upstream of the combustion chamber was observed when the events took place. 
     The impact of false pumping detections has a significant operational impact in the sense that the aircraft is immobilized until the engine has been endoscoped to check for damage. 
     The acquisition line Ps 3  sometimes consists of a pipe which takes the pressure upstream of the combustion chamber  4  and two pressure sensors located directly in the aircraft calculator (FADEC, for full authority digital engine control). 
     The measurement of Ps 3  is carried out using two independent sensors. In order to consolidate the information from the two sensors, a selection logic between the two sensors has been implemented. It is assumed here that the sensors are taking valid measurements (no power failure and the measurement is within a physically plausible range of measurements), and that the two sensors are taking measurements that are deviated from each other. This configuration causes a deviation failure, but it is impossible to vote for either measurement, not knowing which is closest to the actual value Ps 3 . 
     To overcome this problem, a Ps 3  model based on thermodynamic laws is calculated. This model theoretically allows to remove the doubt by providing a third quantity (analytical redundancy), independent of the measurements of Ps 3 , which will allow to vote for one or the other of the readings via the selection logic.  FIG. 2  illustrates this principle, with the two acquisition channels V 10 , V 20 , the model mod_Ps 3  and the switch which occurs when the channel V 10  again becomes closer to the model mod_Ps 3  than the channel V 20  which had diverged from the channel V 10  previously. The switch causes the calculator to observe a significant pressure variation ΔPs 3 . 
     However, in practice, model values that are quite remote from the real value of Ps 3  are observed. This can lead to erroneous channel arbitration. The applicant noticed, after studies, that the false detection of pumping was due to a sudden change in selection of Ps 3 : as the two measurements of Ps 3  were deviated, the selected channel went from the strongest measurement in Ps 3  to the lowest measurement in one calculation pitch since the model was initially closer to the most significant Ps 3  and then closer to the weakest Ps 3 . It is this false jump ΔPs 3  of at least 15% relative value that can trigger a false pumping detection when the pressure has not actually dropped. 
     There is therefore a need to guard against this type of event, in particular by improving the management of arbitration, in particular concerning the pressure Ps 3 , but for any other parameter. 
     More generally, there is a need to better process thermodynamic models, so that they better reflect reality, whether for Ps 3  or other parameters. 
     In addition, various improvements or uses of the thermodynamic model could be made to improve the speed, efficiency and relevance of thermodynamic models. 
     The patent application references US 2014/326213 A1, EP 2 434 127 A2, US 2019/080523 A1 and US 2017/218854 A1 are also known. 
     DESCRIPTION OF THE INVENTION 
     A purpose of the invention is to provide solutions to the mentioned problems. 
     To this end, a method is proposed for resetting the static pressure model upstream of the combustion chamber, called “Ps 3  model”, in a turbine engine comprising a compressor, the Ps 3  model being used to arbitrate between two acquisition channels of the static pressure upstream of the combustion chamber, called “pressure Ps 3 ”, the two acquisition channels involving two sensors, 
     the method using a Ps 3  model stored in a memory, the model expressing the pressure Ps 3  as a function at least of the speed of the compressor, called “speed PCN 25 R” and comprising the following steps: 
     E 1 : measuring a pressure value Ps 3 , by one of the two sensors. 
     E 2 : resetting the Ps 3  model using the measurement of the Ps 3  value. 
     In one embodiment, the Ps 3  model is a Ps 3  model on the compressor pressure, called “pressure P 25 ”, the model being called “model PS 3 /P 25 ”. 
     In one embodiment, the model Ps 3 /P 25  is expressed as a function of the compressor speed, reduced on its temperature, called “temperature T 25 ”, called “speed PCN 25 R” or “speed Xn 25 R”. 
     In one embodiment, the resetting is performed on the Ps 3 /P 25  model as a function of the speed PCN 25 R. 
     In one embodiment, the compressor is a high-pressure compressor, when the turbine engine further comprises a low-pressure compressor upstream of the high-pressure compressor. 
     In one embodiment, the model Ps 3  is defined by segments according to and the resetting step consists in resetting each segment. 
     In one embodiment, in each segment the model PS 3  is linear. 
     In one embodiment, the step of resetting by segment is carried out using a corrector, for example an integral corrector. 
     In one embodiment, the model PS 3  is further expressed as a function of the low-pressure compressor speed, reduced on its temperature, called “temperature T 12 ”, called “speed PCN 12 R”. 
     In one embodiment, the model PS 3  is further expressed as a function of the total external pressure, called “pressure T 2 ”. 
     In one embodiment, the model PS 3  is defined by plane and the resetting step consists in resetting each plane. 
     In one embodiment, the PS 3  model to be reset is selected based on the level of aircraft air bleed in the compressors and the memory stores a plurality of models PS 3  expressed as a function of the aircraft air bleed. 
     A method for arbitrating between two acquisition channels is also proposed, said method comprising the following steps:
         A 1 : implementing a resetting method as described above,   A 2 : selecting the acquisition path closest to the reset model.       

     A method for analyzing the aging of a turbine engine is also proposed, the method consisting in implementing the following steps:
         F 1 : Implementing a resetting method as described above,   F 2 : Saving the reset model in a non-volatile memory,       

     steps F 1  and F 2  being repeated at least twice, and preferably more,
         F 3 : Comparing the different reset models to deduce an evolution of the state of the turbine engine therefrom.       

     To this end, a method is proposed for resetting a model of the operating parameter of a turbine engine or of an aircraft, 
     the model being defined as a law by segment indicating the value of said parameter as a function of a variable, or being defined as a law by plane indicating the value of said parameter as a function of two operating variables, 
     said law being affine on each segment or being affine on each plane, the parameter model being stored in a memory. 
     The operating parameters and variables are for example related to a temperature or a pressure, or else to a compressor speed (typically the speeds Xn 12  and Xn 25  of the low pressure body and of the high pressure body. More generally, they can be any operating parameter for which there is a measurement and a model allowing analytical redundancy. 
     The resetting method comprises the following steps:
         obtaining a value of the parameter,   calculating an error by comparing said value of the parameter with the corresponding value of the model, said value of the model belonging to one of the segments or planes of the model,   applying a corrector by minimizing said error to determine a correction,   resetting the segment of the model or the plane of the model using the correction, to reposition said segment or plane and thus obtain a reset model of the physical parameter.       

     In one embodiment, the step of obtaining the value of the parameter is performed by:
         a direct measurement of said parameter using a sensor, or   a measurement of a third-party parameter on which said parameter depends, or   a simulation.       

     In one embodiment, the corrector is a PID corrector or an integral corrector. 
     In one embodiment, when the model is a law by segment, the resetting is done by freezing a point of the segment and by moving another point of the segment using the correction, the two points preferably being the ends of the segment. 
     In one embodiment, when the model is a law by segment, the resetting is done by not keeping any point of the segment fixed, for example by moving the two ends of the segment using the correction. 
     In one embodiment, the movement of the ends of the segment is done depending on their respective distance from said corresponding value of the model. 
     In one embodiment, the distribution of the correction to be applied to one end of the segment is equal to the ratio of the distance of the corresponding value of the model to the other end of the segment, over the length of the segment. 
     In one embodiment, the step of resetting the segment of the model comprises a linear interpolation between two reset points. 
     In one embodiment, when the model is a law by plane, the plane has the shape of a rectangle which is cut into triangles, and the resetting is done by freezing one or two vertices of the triangle and moving the last two vertices or the last vertex of the triangle using the correction. 
     In one embodiment, when the model is a law by plane, the plane is cut into triangles, and the resetting is done by moving the three vertices of the triangle. 
     In one embodiment, the movement of each vertex of the triangle is done depending on the area of the sub-triangle defined by the other two vertices and said corresponding value of the model. 
     In one embodiment, the distribution of the correction to be applied to a vertex of the triangle is equal to the ratio of the area of the sub-triangle defined by the other vertices and said corresponding value of the model, to the area of the triangle. 
     In one embodiment, the step of resetting the triangle comprises a linear interpolation from the reset points. 
     In one embodiment, the parameter is the pressure Ps 3  or the pressure Ps 3  divided by the pressure P 25  and wherein:
         the variable is, when the model is a law by segment, the speed PCN 25 R and   the variables are, when the model is a law by plane, the PCN 25 R and the PCN 12 R, or the PCN 25 R and the PT 2 .       

     In one embodiment, the model to be reset is selected according to a variable, the memory stores a plurality of models expressed as a function of the aircraft air bleed, the variable possibly being the level of aircraft air bleed in the compressors. 
     In one embodiment, the corrector gains are different for different segments or planes of the model. 
     A method for analyzing the aging of a turbine engine is also proposed, the method consisting in implementing the following steps:
         F 1 : Implementing a resetting method as described above,   F 2 : Saving the reset model in a non-volatile memory,       

     steps F 1  and F 2  being repeated at least twice, and preferably more,
         F 3 : Comparing the different reset models to deduce an evolution of the state of the turbine engine therefrom.       

    
    
     
       DESCRIPTION OF THE FIGURES 
       Other features, purposes and advantages of the invention will emerge from the following description, which is purely illustrative and not limiting, and which should be read with reference to the appended drawings wherein: 
         FIG. 1  schematically illustrates a turbine engine. 
         FIG. 2  illustrates a method for arbitrating between two acquisition channels using a thermodynamic model. 
         FIG. 3  graphically illustrates a method for resetting the pressure Ps 3 . 
         FIG. 4  illustrates a block diagram of a method for resetting a parameter model, such as the pressure Ps 3 . 
         FIG. 5  illustrates a corrector. 
         FIGS. 6 a  and 6 b    illustrate methods for resetting a 2D model by segment. 
         FIG. 7 a    illustrates, for a segment, a method for resetting a 2D model into a segment by weighting. 
         FIG. 7 b    illustrates, for several segments, a method for resetting a 2D model into a segment by weighting. 
         FIG. 8  illustrates a 3D model by plane. 
         FIG. 9  illustrates a block diagram of a method for resetting a 3D model of a parameter, such as the pressure Ps 3 , as a function of the pressures PCN 12 R and PCN 25 R. 
         FIG. 10 a    illustrates, for a plan, a method for resetting a 3D model in segment by weighting. 
         FIG. 10 b    illustrates the choice of a triangle among the rectangle forming a plane of the 3D model. 
         FIG. 10 c    illustrates the choice of the weighting for a triangle among the rectangle forming a plane of the 3D model. 
         FIG. 11  illustrates by a block diagram a model selection as a function of a variable, prior to the resetting of the model. 
         FIG. 12  illustrates a method for analyzing the turbine engine aging. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The context and definitions given in the introduction are repeated here. 
     First of all, a method for resetting the static pressure model upstream of the combustion chamber will be described. This pressure will be called pressure Ps 3  and this model will be called “Ps 3  model” and referenced mod_Ps 3 . This is a thermodynamic model. 
     The final purpose of the Ps 3  model is in particular to allow to arbitrate between two redundant acquisition channels V 10 , V 20 , the function of which is to measure the pressure Ps 3 . Each acquisition channel V 10 , V 20  comprises a sensor  10 ,  20 . The sensor  10 ,  20  is standard and will not be described here. 
     A method for arbitrating between the two acquisition channels V 10 , V 20  will be described below. 
     A calculation unit  100  is provided, which comprises a processor  110  and a memory  120 . The calculation unit  100  can be a FADEC (“full authority digital engine control”) or else be a separate component, positioned as close as possible to the acquisition channels V 10 , V 20  for more responsiveness. 
     The memory  120  stores a model mod_Ps 3 , which allows to obtain the value of the pressure PS 3  as a function at least of one variable Var, which is the speed PCN 25 R (high pressure compressor speed): the model mod_Ps 3  is then written under the form mod_Ps 3 (PCN 25 R). In practice, the model mod_Ps 3  involves several sub-models, such as in particular the Ps 3  model on the pressure of the high-pressure compressor P 25  (this model is called mod_Ps 3 /P 25 ) and the model mod_Ps 3 /P 25  is in turn expressed as a function of the speed of the high-pressure compressor PCN 25 R reduced on its temperature T 25 . This model is then written in the form mod_Ps 3 /P 25 (PCN 25 R/T 25 ). 
     Then it is sufficient to multiply the value of Ps 3 /P 25  by P 25  to get the value of Ps 3 . 
     Rather than directly resetting the model mod_Ps 3 , it is thus preferable to reset the model mod_Ps 3 /P 25 . The denomination of “Ps 3  model”, in the form mod_Ps 3 , includes models which do not directly express pressure Ps 3  but allow it to be obtained subsequently, such as the model mod_Ps 3 /P 25 . 
     In a first step E 1 , one of the two acquisition channels V 10 , V 20 , using its sensor  10 ,  20 , measures a value Val_Ps 3  of the pressure Ps 3  on the turbine engine (for a real value of the physical quantity which is used as a variable, that is to say PCN 25 R). At this stage, it is assumed that the two acquisition channels V 10 , V 20  are sound and that the two sensors  10 ,  20  give a correct measurement. In other words, there is no failure of sensors  10 ,  20  or deviation beyond a predetermined threshold between the two measurements. 
     This measurement of a value Val_Ps 3  of the pressure Ps 3  is then sent to the calculation unit  100 . 
     A step E 2  of conversion or of processing data can be implemented: for example, Val_Ps 3  is a value of static pressure Ps 3 , while the model mod_Ps 3 /P 25  uses the pressure Ps 3  reduced on the P 25 : it is therefore necessary to divide the value of the static pressure by P 25  to obtain the value Val_Ps 3 /P 25 . 
     Then, in a step E 3 , the calculation unit  100  resets the Ps 3  model stored in its memory  120  using said measurement of the value of the pressure Ps 3 . Resetting means that there exists at least one point of the model mod_Ps 3  (in practice a plurality, or even an infinity, if the model is continuous) whose ordinate has been shifted (therefore with constant abscissa). The reset model is noted Rmod_PS 3 /P 25 . Subsequently, the writing will be simplified by keeping mod_PS 3 /P 25  which designates a model before and after resetting. 
     In this case, there is at least one point P of the curve mod_Ps 3 (Var) whose value Val_mod_Ps 3 (Var) has changed before and after the resetting, for a value of the given variable. In the preferred embodiment, mod_Ps 3 /P 25 (PCN 25 R) and Val_mod_Ps 3 /P 25 (PCN 25 R) are used. 
     Finally, a step E 4  of storing the reset Ps 3  model in memory  120  is defined. In one embodiment, the reset model mod_Ps 3  (in this case mod_Ps 3 /P 25 ) replaces by deleting the previous model in the memory  120 . In another embodiment, it does not delete it. 
     Preferably, the steps E 1 , E 2  and E 3  are repeated at regular intervals, of the type at each calculation pitch. The calculation pitch corresponds to approximately 0.015 s. During a calculation pitch, the two steps E 1  and E 3  can be implemented or else a step E 1  and in parallel the step E 3  using the data from step E 1  of the previous pitch are implemented. 
     As the model mod_Ps 3  is updated at regular intervals, the arbitration can be done more quickly and therefore more correctly, avoiding the jumps ΔPs 3  related to the untimely channel V 10 , V 20  change. 
     The resetting is advantageously carried out using a corrector  112  which is integrated in a loop of the control chain. This corrector will be described in detail below. 
     A method for arbitrating between two acquisition channels V 10 , V 20  is also defined, the arbitration method comprising a step A 1  of implementing a resetting method as described above and a step A 2  of selecting the acquisition channel V 10 , V 20 , during which the processor selects a channel V 10 , V 20  among the two channels V 10 , V 20 . The choice is made according to the acquisition channel V 10 , V 20  which is closest to the reset model. The step A 2  is conventional and will not be described here. 
     Secondly, a specific method for resetting a model mod_PARAM of turbine engine or aircraft parameter (for example temperature, pressure, in absolute or in relative terms) will be described, with reference to the general representation of  FIG. 4 . “Parameter of interest” will be discussed. The model is again a thermodynamic model. The model describes the change in the parameter as a function of one or more variables Var which are also in reality turbine engine or aircraft parameters (for example temperature, pressure, in absolute or relative terms). It is stored in the memory  120  of the calculation unit  100 . 
     This method is fully applicable to the method for resetting the pressure Ps 3  described above. The pressure Ps 3  will also be used as an example of parameter PARAM and the pressure PCN 25 R as variable Var but the method can be applied to any physical parameter PARAM of an aircraft and any variable Var (for example pressure PT 2 ): for example mod_Ps 3 /P 25 (PCN 25 R), mod_Ps 3 /P 25 (PCN 25 R, PCN 12 R), mod_Ps 3 /P 25 (PCN 25 R, PT 2 ), mod_T 25 (PCN 12 R, PT 2 ), mod_Xn 25 (PCN 12 R, PT 2 ) where Mach is the speed of the aircraft, mod_T 3  (T 25 ), etc. 
     A model is defined here as a law by segments (in a configuration called 2D configuration) or by plane (in a configuration called 3D configuration) indicating the value of said parameter of interest as a function respectively of a variable Var (2D) or of two variables Var 1 , Var 2  (3D). The law is linear respectively on each segment (or in other words, piecewise affine: that is to say that its equation is in the generic form z=ax+c) or on each plane (equation in the generic form z=ax+by+c). 
     The interest of a model defined as a law by segment (2D) or by plane (3D) is the application of the principles of linear automation. For example, the model mod_Ps 3 /P 25 (Xn 25 r) or mod_Ps 3 /P 25 (PCN 25 R) is nonlinear in its entirety. 
     The same framework as before is considered, with the two acquisition channels V 10 , V 20 . 
     In a step E 1 , a value Val_PARAM of the parameter of interest PARAM is obtained. This can be obtained in the context of step E 1  described above, by measuring a sensor  10 ,  20  of one or more acquisition channels V 10 , V 20 , in particular with the acquisition of a third-party parameter and said parameter of interest is deduced therefrom. 
     Alternatively or in addition, the parameter of interest PARAM can be obtained using a simulation. 
     The following steps and sub-steps are implemented by the processor  110  and the memory  120  of the calculation unit  100 . 
     A data conversion step E 2  can be implemented when the measured parameter does not correspond to the model parameter: for example, as explained previously, Val_Ps 3  is a static pressure value Ps 3 , while the model mod_Ps 3 /P 25  uses pressure Ps 3  reduced on P 25 . In the case of a third-party parameter, said calculation unit  100  calculates a value of the parameter of interest Val_PARAM from the value of the third-party parameter. 
     Then, the resetting step E 3  is implemented. This resetting step E 3  comprises several sub-steps. 
     In a sub-step E 31 , the processor  110  recovers the value Val_mod_PARAM from the model mod_PARAM which corresponds to the value of the parameter of interest Val_PARAM obtained in step E 1 . 
     The value of the model Val_mod_PARAM is thus on one of the segments or planes of the model mod_PARAM. This correspondence can be done via the value of the variable Var of the model mod_PARAM: the value of the model Val_mod_PARAM whose abscissa corresponds to that of the value Val_PARAM of the parameter of interest is taken. For this purpose, it may be necessary to actually perform two measurements: one on the parameter PARAM and one on the variable Var, to have a pair of data. 
     In the case of the pressure Ps 3 , it is thus possible to have a measurement of the PCN 25 R at the same time as the measurement of the Ps 3 . 
     With the two values Val_mod_PARAM and Val_PARAM, the sub-step E 31  comprises the calculation of an error ε, typically by subtraction: ε=Val_mod_PARAM-Val_PARAM. This error ε is illustrated in  FIG. 5 . 
     In a sub-step E 32 , this error ε is processed by a corrector  122 , the role of which is to minimize said error ε. The corrector  122  allows to calculate a correction corr which is a deviation to be applied to the coordinates of the points of the corrected law, obtained via the corrector PID, from the error (deviation between the measurement and the model) and which must be brought to the model mod_PARAM. Due to the segmentation (segment or plane) of the model m_PARAM, the corrector is implemented only on the segment or plane considered during the implementation of step E 3 . 
     A particular corrector will be described below. 
     Finally, in a sub-step E 33 , the correction corr is used to reset the segment or the plane of the model mod_PARAM. This step consists in recalculating a segment or a plane, from the preceding model mod_PARAM and the correction corr calculated in the sub-step E 32 . In particular, the resetting consists in moving a minimum number of points of the model mod_PARAM in a sub-step E 331  and in interpolating the rest of the model between these points in a sub-step E 332 : two points for the model by segments and three points for the model by plane. 
     Several embodiments of the resetting will be described below. 
     It is further noted, for example in  FIG. 3 , that the resetting of a segment will also influence the adjacent segments in the case where the end of the reset segment is moved. A step of interpolating the adjacent segments can further be implemented. 
     The corrector selected is a PID (proportional integral derivative) corrector, illustrated in  FIG. 5 , where Gp, Gd and Gi are respectively the gain of the proportional corrector, of the derivative corrector and of the integral corrector, S being the variable in the frequency domain (Laplace variable). 
     The integral corrector (the I of the PID) allows to introduce a certain inertia to the looped system, which allows to avoid hypersensitivity to disturbances and idle points, compared to an all or nothing corrector. The integral corrector also allows to control the resetting speed, and to avoid an instantaneous drift of the model m(param) towards the average between the two channels V 10 , V 20  in the event of a drift of one of the sensors  10 ,  20 . 
     A proportional corrector (the P of the PID) and a derivative corrector (the D of the PID) are implemented to more finely adjust the corrector  122  if necessary but are not used (the empirical approach has shown that their contribution is marginal compared to that of the integrator which naturally transcribes the desired behavior much better for the resetting). Gp=Gd=0 can thus be obtained. 
     The corrector is adjusted so that the model mod_PARAM is reset quickly enough to account for reconfigurations of the turbine engine (for example a change in the levels of air bleeds from the high pressure compressor). 
     Model by Segment (2D) 
     One places oneself here on the segment of the model mod_PARAM which is concerned by the measurement Val_PARAM carried out in step E 1 . This segment has two end points, on the left and on the right, noted A and B. 
     Point-by-Point Resetting 
     The first solution, illustrated in  FIGS. 6 a    and  6   b,  consists in reporting the correction by modifying the coordinates of a single point of the segment, for example one of the end points A or B, while the other is frozen. 
     In this case, the output of the corrector  122  directly impacts point B (respectively point A), and point A (respectively point B) remains frozen. This solution however constrains to freeze at least one of the points of the model mod_PARAM to serve as a reference, from which the other segments of the model mod_PARAM will be impacted. Thus, during the resetting step E 2  and more specifically during the sub-step E 231 , only one of the two end points is moved. Then, the interpolation step E 232  is implemented. 
     This solution is the simplest and fastest to calculate. 
     Weighted Resetting of the Two Points of the Segment 
     The second solution, illustrated in  FIGS. 7 a    and  7   b,  consists in distributing the correction in a weighted manner to allow the selected segment to be reset in a more representative and more efficient manner. In an advantageous embodiment, the weighting is performed according to the distance between the value Val_PARAM, here Val_Ps 3 /P 25 , and the points A and B of the segment. 
       FIGS. 7 a  and 7 b    illustrate the resetting over an interval and a calculation pitch:
         step E 1 : the measured value Val_PARAM is obtained by one or two acquisition channels V 10 , V 20 ; in the example, this is Val_Ps 3 ,   step E 2  (image (a) of  FIG. 7 b   ): the measured value Val_PARAM is converted to be homogeneous with the model mod_PARAM; by simplification, the same reference Val_PARAM is kept,   step E 31  (image (b) of  FIG. 7 b   ): ε which is the deviation between the measured value Val_PARAM and the value of the model Val_mod_PARAM is measured; in the example with the pressure Ps 3 : Val_PARAM=Val_PS 3 /P 25 , that is to say the measured pressure Ps 3  divided by the pressure P 25  model and Val_mod_PARAM=Val_mod_Ps 3 /P 25 , the pressure Ps 3  of the reset model (by previous iterations) which is divided by P 25  model,   step E 32  (image (b) of  FIG. 7 b   ): the error ε is minimized via the corrector  122 , by integrating it, to calculate a correction corr,   step E 331  ( FIG. 7 a   ): the distance from the point Val_mod_PARAM, here Val_mod (Ps 3 /P 25 ), to the point A, which constitutes the lower limit of the interval of the variable Var (here PCN 25 R), and which is a function of the linearization of the selected model, is then measured (or before step E 31 ) relative to the distance between points A and B. Finally, the correction is distributed on the ordinate of points A (to give A′) and B (to give B′),   step E 332  (image (c) of  FIG. 7 b   ): a new segment is interpolated between the two reset points A′ and B′.       
     The operating principle is to distribute the correction corr of the corrector  122  of an interval on the ordinates of the points A and B according to the same principle as previously: in one embodiment, X % of the correction is distributed on the ordinate of the point B, with X the ratio between the distance from point Val_mod_PARAM to point A on the distance from point A to point B. 100-X % of the correction is distributed on the ordinate of point A (30% and 70% on the  FIG. 7 a   ). 
     Once the two points A′ and B′ have been replaced, it suffices in step E 232  to interpolate the model between these two points. Since the law is defined by segment, the linear (or affine) interpolation is simple. 
     Alternatively, any other (distinct) points of the segment can be moved by the correction: it suffices to select two points and the linear (or affine) interpolation allows to complete the rest of the considered segment. 
     This method thus allows an efficient and fast resetting to obtain a reset model mod_PARAM. However, since this model mod_PARAM depends only on one variable Var (PCN 25 R in the case of mod_Ps 3 ), it may be insufficient for certain flight situations, in particular when the parameter of interest PARAM depends on several variables Var 1 , Var 2 . 
     Model by Plane (3D) 
     In this regard, to take into account several variables, the model mod_PARAM can be a function of two variables (mod_PARAM(Var 1 , Var 2 )) and be expressed in the form of a law defined by planes, the law being linear on each plane as shown in  FIG. 8 . 
       FIG. 9  illustrates the implementation of a resetting method in the case of a model by plane. 
     For example, in the case of the pressure Ps 3 , when activating an air bleed level, the model mod_Ps 3 /P 25 (PCN 25 R) (that is to say the model Ps 3  reduced on P 25  as a function of PCN 25 R) is modified because part of the air compressed by the high pressure compressor is sent to the aircraft air system). The corrector  122  of the 2D model by segment optionally allows to adapt to this reconfiguration if the gains of the corrector  122  are adjusted so that the resetting of the model is fast, but this can pose other difficulties. 
     The air bleed is performed from the primary flow. The air bled can be used by the aircraft (for example to pressurize the cabin . . . ). It can also be rejected in the secondary flow (VBV for Variable Bleed Valve, TBV for Turbine Bypass Valve), the purpose then being to reduce the pressure downstream of the compressor to avoid pumping. Depending on the volume of air required by the aircraft and the volume released into the secondary flow for engine regulation reasons, it is then possible to define air bleed levels. These bleed levels have an impact on the speed/Ps 3  correlation since depending on the level of air bled, different pressures can be obtained for the same engine speed. Then it becomes difficult to define a model for regulating Ps 3  according to the speed. The solution developed in the various embodiments to respond to this problem is to define several models, each model corresponding to a given level of air bleed. The corrector is then asked to change the model depending on the level of active air bleed at the given instant. 
     Still in the example of the pressure Ps 3 , to overcome the problem of air bleeds, a Ps 3 /P 25  model which no longer depends only on PCN 25 R, but also on PCN 12 R, is then implemented: mod_Ps 3 /P 25 (PCN 25 R, PCN 12 R) is then defined. When activating direct bleeds, the law linking PCN 25 R and PCN 12 R is changed, which allows to take the reconfiguration of the system into account. The resetting of this law therefore requires a new “3D” corrector. 
     Point-to-Point Resetting 
     The first solution, not illustrated, consists in taking into account the correction by fixing the coordinates of a single point of the rectangle, for example one of the vertices A, B, C or D of the rectangle and by modifying the coordinates of two points of the rectangle, for example two of the vertices A, B, C or D. Alternatively, one can fix two points fixing the coordinates of two points of the rectangle, for example two of the vertices A, B, C or D of the rectangle and modifying the coordinates of a point of the rectangle, for example two of the vertices A, B, C or D. 
     The concerned points are moved during sub-step E 331  then the interpolation step E 332  over the entire rectangle is implemented. Since we are working on three points each time, the existence of the interpolated rectangle is ensured. 
     Weighted Resetting 
     To allow a weighted resetting, for which no point is fixed, the model mod_PARAM is linearized by cutting the rectangle ABCD into triangles ABC, ABD, typically two complementary triangles ( FIG. 8 ). Indeed, three points A, B, C are always coplanar, before and after resetting, which ensures the existence of the interpolation of the triangle reset in the interpolation sub-step E 332 , once the sub-step E 331  of resetting the three points is carried out. The three new points resulting from the correction can thus be used to describe the Cartesian equation of a plane, thus allowing to linearly interpolate the model mod_PARAM. 
     Indeed, if a correction weighted on three points of the surface were applied on four points, for example the four vertices ABCD of the rectangle, there would be a deformation of the rectangle if the four points of the rectangle were no longer coplanar (impossible to interpolate the coordinates of the parameter PARAM using the Cartesian equation of a plane). 
     In sub-step E 331 , it is a matter of first selecting the triangle to be reset according to the value of Val_PARAM (called point X) obtained by steps E 1  and E 2 . For this purpose, a difference in slope between the segment AC which divides the rectangle in two and the segment AX ( FIG. 10 b   ). Any vertex B, C or D can be used. 
     Indeed, with the four points A, B, C, D forming a rectangle and the point X corresponding to the measured point Val_PARAM, it is necessary to determine if X belongs to the triangle ABC or to the triangle ACD (it is recalled that these triangles were selected arbitrarily compared to ABD and DBC). 
     For this purpose, a comparison of the values of the variation rates ΔAC, ΔAX of the straight lines (AC) and (AX) is carried out during the sub-step E 331 . Indeed, if ΔAX&gt;ΔAC then ACD is selected and if ΔAX≤ΔAC then ABC is selected. Then it is about distributing the correction. 
     Unlike the 2D model with segments, the distances between point X and the points of triangle ABC do not take into account the distribution of the correction to be applied. The distribution is therefore made in proportion to the areas of triangles XAB, XAC and XBC ( FIG. 10   c,  where xis the area of XBC, y is the area of AXC and z is the area of XAB). 
     The ratios corr_A, corr_B, corr_C by corr_a=x/(x+y+z), corr_b=y/(x+y+z), and corr_z=z/(x+y+z) are defined. 
     The ratio corr_A is applied to the resetting of point A, corr_B to that of point B and corr_C to that of point D. 
     Finally, the interpolation sub-step E 332  is implemented from the three points reset by a simple plane Cartesian equation, to interpolate the entire triangle. 
     Matrix (2D) Segment Model 
     It was said that the 2D segment model has limitations, in particular when another variable could have a strong influence on the model mod_PARAM. 
     Illustrated in  FIG. 11 , another solution to take into account another variable consists in storing in the memory  120  a matrix M of 2D model mod_PARAM. Instead of having a model in the form mod_PARAM(Var 1 , Var 2 ), there is a model in the form mod_PARAM_Var 2 (Var 1 ), where mod_PARAM_Var 2  designates an applicable model for a given value (or a set of given values) of the variable Var 2 . 
       FIG. 11  illustrates mod_Ps 3 _PCN 12 R(PCN 25 R). Here, PCN 12 R does not necessarily symbolize an exact value of the variable but a level, which can be an interval or be discrete. 
     In the case of the pressure Ps 3  where the parameter PARAM is Ps 3 /P 25  and where the variable Var 1  is PCN 25 R, the memory  120  can store a plurality of models mod_Ps 3  according to the bleeds, that is to say PCN 12 R. 
     In this embodiment, there is a limited number of stored models. Consequently, the values of PCN 12 R can be expressed by a number of levels of aircraft air bleed. 
     Consequently, before step E 31  described above, the model mod_PARAM_Var 2  is selected in a step E 30 , as a function of the value of the variable Var 2 , then the model mod_PARAM_Var 2  is reset as a 2D model during steps E 31 , E 32  and E 33 . Along with step E 1 , there is a step of measuring or acquiring the variable Var 2  which determines the choice of the model mod_PARAM_Var 2   
     Adjusting the Dynamics of the Correctors 
     The adjustment of the dynamics of the 2D corrector is carried out by taking into account two conflicting needs:
         the dynamics must be slow enough so that the known cases of drifts of one of the acquisition channels V 10 , V 20  do not cause the model to drift by following the average of the channels V 10 , V 20  (so that one can vote for one of the two channels when the deviation failure clears),   the dynamics must be fast enough so that the concerned speed ranges are nonetheless reset (in particular the speeds traveled up to the take-off speed, during take-off).       

     As there is one corrector  122  per 2D model segment or per 3D model plane, it is possible to adjust the correctors (mainly the integrating corrector) independently of each other:
         rapid dynamics will then be applied to the speed ranges covered quickly during a classic mission. This allows to respond to the constraint of resetting these speed ranges in a very short time,   slow dynamics will be applied to the speed ranges over which the reset time is not a strong constraint (examples: ground idle, cruising, climb). In the case of pressure Ps 3 , this allows to best guard against the risks of resetting on the average of the channels Ps 3  in the event of a drift of one of the two over these speed ranges.       

     Thirdly, a method for analyzing the aging of a turbine engine will be described, as illustrated in  FIG. 12 . The example with the pressure Ps 3  and the previous recalibration models will be taken, but the principle is applicable in the same way to any resetting method allowing to generate a reset model Rmod_PARAM. 
     At each resetting, step E 3  is implemented and a “reset” model mod_PARAM (mod_Ps 3 , mod_Ps 3 /P 25 , etc.) is generated. When the purpose of this resetting is to allow a more efficient arbitration, the reset model mod_Ps 3 /P 25  replaces the model mod_Ps 3 /P 25  previously which becomes in fact obsolete. In this regard, an overwrite can be performed in the memory  120 . 
     However, as each model mod_Ps 3 /P 25  differs from the previous model (on a few segments or a few planes, at a minimum), it is possible to observe, step by step, the overall evolution of the model mod_Ps 3 /P 25  by comparing all (or a certain number) of the reset models. 
     Thus, the various resetting methods described above are advantageously implemented in a method for measuring the aging of a turbine engine. 
     The turbine engine analysis method thus comprises a step F 1  of implementing a resetting method comprising steps E 1 , E 2 , E 3 , E 4  and a step F 2  of storing the model mod_PARAM reset in a memory, which may be the memory  120 . Unlike step E 4 , which may involve deleting the previous model, step F 2  involves a definitive saving (that is to say a non-transitory saving) of the model mod_PARAM. 
     Steps F 1  and F 2  are repeated at least twice and preferably a large number of times. 
     It should be noted in particular that the behavior of a compressor can be degraded in different ways depending on its environment (cold, sand, etc) or unforeseen events (ingestion of a bird causing pumping or slight damage to the blades). The resetting allows the model to “age” with its engine. It must therefore be able to reset on one or two missions, but not be sensitive to variations in Ps 3  over a few seconds. 
     As it is a matter of analyzing the turbine engine, that is to say of seeing its evolution over time, it is preferable that the memory  120  stores corrected models mod_PARAM generated at time intervals greater than the day, or even the last one month or trimester or semester. 
     Once all these data were acquired, a comparison step F 3  is implemented by the processor  110  to compare the different reset models mod_PARAM. This comparison allows to deduce the state of the turbine engine. 
     In the case of pressure Ps 3  for example, at the same PCN 25 R, a “young” compressor HP will have a higher Ps 3  than an “old” compressor HP. The degradation of the compression ratio therefore results in the lowering of the Ps 3  at a given PCN 25 R. The comparison of the models therefore allows to deduce a change in the condition of the engine. 
     Step F 3  can be performed by the calculation unit  100  directly, so that the state of the turbine engine or of the aircraft is known as soon as an operator so requires. Alternatively, this step F 3  is done in the design office, after data recovery. Likewise, step F 2  can be carried out using the memory  120  of the calculation unit, but the reset models Rmod_PARAM can also be transmitted to a memory external to the aircraft or to the turbine engine, in particular in a design office, to then implement the state F 3 . 
     For example, an analysis of the aging of the high-pressure compressor can be established thanks to the evolution of the model mod_Ps 3 /P 25 (PCNR 25 R). As the compressor efficiency decreases over time, monitoring the models mod_Ps 3 /P 25 (PCNR 25 R) provides continuous information reflecting the current compressor.