Abstract:
A method of estimating quality parameters for a plurality of engine components of a gas turbine engine is provided. The engine components have at least one sensor responsive to the engine component operation. The method includes providing an engine model having virtual sensor values and quality parameters corresponding to the plurality of sensors of the engine components; comparing the virtual sensor values to actual sensor values of the plurality of sensors of the engine components to determine the difference between the actual and virtual sensor values; amplifying the difference by a predetermined gain; generating a plurality of quality parameter deltas in response to the sensed difference; iteratively updating the embedded engine model by inputting a predetermined portion of the generated quality parameter deltas into the embedded engine model; adjusting the embedded engine model for engine operating conditions; and recalculating the virtual sensor values.

Description:
FIELD OF THE INVENTION  
       [0001]     The present invention relates generally to gas turbine engines, and more particularly to a method of tracking and estimating engine component qualities.  
       BACKGROUND OF THE INVENTION  
       [0002]     As gas turbine engines operate, the engines may become less efficient due to a combination of factors including wear and damage. Because the rate at which engines deteriorate depends on several operational factors, the rate is difficult to predict, and as such, engine components are typically scheduled for maintenance based on a pre-selected number of hours or cycles. The pre-selected number is typically conservatively selected based on a number of factors including past component experience and past engine performance estimates. If a component fails, a predetermined diagnosis routine is followed to identify and replace the failed component.  
         [0003]     To estimate engine performance and to find engine sensor faults, selected engine parameters are sensed and monitored to estimate an overall loss in the engine performance. Typically, rotor speeds, exhaust gas temperatures, and fuel flows are corrected or normalized for variations in operating conditions, and these normalized parameters are trended, i.e., their changes over short and long periods of time are plotted, and used to forecast when engine refurbishment is required. Additionally, immediate engine repairs may be scheduled if comparing current trending values to prior trending values illustrates abrupt changes, or step changes.  
         [0004]     While currently the overall engine performance and quality is inferred by assessing engine sensor values during operation and long-term trending, the quality level of individual gas turbine components, such as fan, compressor or turbines, is not estimated and continuously updated during operation.  
         [0005]     What is needed is the ability to forecast the remaining engine life before an engine overhaul is required, through the estimation and tracking of engine component quality. What is also needed is the ability to define the scope of the required engine overhaul, and to detect and isolate engine component damage and faults.  
       SUMMARY OF THE INVENTION  
       [0006]     The present invention is directed to a method of estimating quality parameters for a plurality of engine components of a gas turbine engine. The engine components have at least one sensor responsive to the engine component operation. The method includes providing an engine model having virtual sensor values and quality parameters corresponding to the plurality of sensors of the engine components; comparing the virtual sensor values to actual sensor values of the plurality of sensors of the engine components to determine the difference between the actual and virtual sensor values; amplifying the difference by a predetermined gain; generating a plurality of quality parameter deltas in response to the sensed difference; iteratively updating the embedded engine model by inputting a predetermined portion of the generated quality parameter deltas into the embedded engine model; adjusting the embedded engine model for engine operating conditions; and recalculating the virtual sensor values; and repeating the steps of comparing, amplifying, generating, and updating the embedded model for a predetermined percentage of the generated quality delta parameters.  
         [0007]     In another aspect, the present invention is directed to a diagnostic system for estimating quality parameters for a plurality of engine components of a gas turbine engine. The system includes an engine model for computing virtual sensor values and quality parameters corresponding to the plurality of sensors of the engine components. A comparator is connected to the engine model output for comparing the virtual sensor values to actual sensor values of a plurality of sensors of the engine components to determine the difference between the actual and virtual sensor values. The differences are multiplied by an amplifier having a predetermined gain. A processor unit generates a plurality of quality parameter deltas in response to the determined difference in actual versus virtual sensor values. Automatic switching means iteratively updates the engine model by switching a predetermined portion of the generated quality parameter deltas into the embedded engine model. The model also has inputs for sensing engine operating conditions for adjusting the virtual sensor values for feedback to the comparator.  
         [0008]     One advantage of the present invention is the ability to track and to estimate engine component quality for forecasting useful life, for determining the scope of a maintenance overhaul, and for detecting and isolating component damage or faults.  
         [0009]     Another advantage of the present invention is the ability to track quality parameters for each individual component of an engine.  
         [0010]     Other features and advantages of the present invention will be apparent from the following more detailed description of the preferred embodiment, taken in conjunction with the accompanying drawings which illustrate, by way of example, the principles of the invention. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0011]      FIG. 1  is a schematic diagram of a physics-based embedded component level model.  
         [0012]      FIG. 2  is a diagram of the quality estimation method.  
         [0013]      FIG. 3  is a diagram of a quality optimization process using the quality estimation method of the present invention.  
         [0014]      FIG. 4  is a graphical representation of the changes in quality deltas during an exemplary take off. 
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0015]     Referring first to  FIG. 1 , a gas turbine engine includes a plurality of sensors (not shown), which monitor engine operation and input real-time actual engine sensor data during engine operation to engine model  10 . In one embodiment, the sensors monitor engine rotor speeds, engine temperatures, and engine pressures. Ambient flight condition data is also input to engine model  10 . Ambient flight condition data input includes, but is not limited to, ambient temperature, ambient pressure, aircraft mach number, and engine power setting parameters such as fan speed or engine pressure ratio. Any suitable technique for collecting ambient flight condition data and actual engine sensor data can be used.  
         [0016]     Engine model  10  is used to estimate sensed parameters, such as rotor speeds, temperatures, and pressures, as well as computed parameters such as thrust, airflows, stall margins, and turbine inlet temperature. The computed parameters are based on environmental conditions, power setting parameters, and actuator positions input into engine model  10 . In the exemplary embodiment, engine model  10  is a physics-based aerothermodynamic model. In another embodiment, engine model  10  is a regression-fit model. In a further embodiment, engine model  10  is a neural-net model.  
         [0017]     A physics-based engine model  10  includes a core engine  38  including in serial, axial flow relationship, a high pressure compressor  14 , a combustor or burner  24 , and a high pressure turbine  16 . Core engine  38  is downstream from an inlet  22  and fan  12  and booster  20 . Fan  12  and booster  20  are in serial, axial flow relationship with core engine  38  and a bypass duct  30  and a bypass nozzle  32 . Fan  12 , booster  20 , and low pressure turbine  18  are coupled by a first shaft  52 , and compressor  14  and high pressure turbine  16  are coupled with a second shaft  54 . A portion of airflow  58  entering inlet  22  is channeled through bypass duct  30  and is exhausted through bypass nozzle  32 , and the remainder of airflow  58  passes through core engine  38  and is exhausted through a core engine nozzle  26 .  
         [0018]     Engine model  10  is known as a Component Level Model (CLM) because each component,  12 ,  14 ,  16 ,  18 ,  20 , and  24  within engine model  10  is individually modeled and then assembled into a specific engine model, such as physics-based engine model  10 . Engine model  10  is programmed to represent a fast-running transient engine cycle that accounts for flight conditions, control variable inputs, and high-pressure compressor bleed. Further, engine model  10  includes parameters such as engine component efficiencies and flows which may be adjusted or tuned. These parameters can be modified using a parameter estimation algorithm, thereby modifying the model of a nominal or average engine to the model of a specific engine.  
         [0019]     The CLM  10  is designed with realistic sensitivities to flight conditions, control variable inputs and high-pressure compressor bleed. The quality parameters for the CLM  10  comprise flow and efficiency modifiers for each major rotating component. Each of the fan  12 , compressor  14 , HP turbine  16 , LP turbine  18 , and in some cases, the booster  20 , have a flow modifier and an efficiency modifier, resulting in eight quality parameters for the CLM  10 , and possibly ten quality parameters if the booster  20  is included. The quality parameters are based on the sensed engine component parameters described above. These quality parameters can be adjusted or perturbed from their nominal values, thereby affecting the model calculations. Proper manipulation of these quality parameters permits the model to simulate the behavior of a particular engine more precisely, to take into account the effects of manufacturing variations between engines, engine deterioration, or damaged engine parts. Perturbation of the quality parameters allows for a better match of model-computed sensor values to actual engine sensor values.  
         [0020]     Additionally, the physics based model  10  includes components and senses parameters associated with the air inlet  22 , the burner  24 , the core nozzle  26 , the bypass duct  30 , and the bypass nozzle  32 .  
         [0021]     When properly tracked, and given accurate sensor values, the model quality parameters reflect actual engine component quality levels, and these levels can be used to diagnose problems in the engine. For example, a “large” bird strike on the fan results in a “large” negative shift in the flow and efficiency of the fan in the model, resulting from the attempts of the tracking filter to match the model outputs to the engine sensors. If the damage caused by the bird striking the fan propagates to the compressor, a negative shift in the compressor quality parameters would also be seen.  
         [0022]     Referring next to  FIG. 2 , a diagram  100  of the parametric quality estimation method is illustrated. A logic unit (not shown) for executing the method of the invention may include a processor, the processor being implemented through a microprocessor and associated components such as RAM, I/O devices, and other computer components. The engine sensor values at intermediate rated power (IRP)  46  are input to a subtractor circuit  60 . Virtual sensor values  50  are subtracted from the engine sensor values  46  and the difference (or delta) signal  64  is input to compute quality adjustments in filterblock  66 , in which the differences between the virtual and engine sensor values  64  are multiplied by predetermined gain levels to yield computed quality adjustments  69 . A predetermined percentage of the computed quality adjustments are input into block  72  for updating the embedded model  56  via an iterative process  68 . The iterative process  68  with time delay controls the sampling rate indicated as switch  70 , which is switched at a sufficiently high rate. Preferably the time delay is about 250 milliseconds (ms).  
         [0023]     The engine model  56  is updated every delay period at step  72  with the computed quality adjustments  69 . Operating conditions  76  at IRP are input to the adjusted engine model  56 . The adjusted engine model  56  generates an optimum set of component quality adjustments  78 , and also generates an updated set of virtual sensor values  50  to close the feedback loop to the subtractor  60 , which are subtracted from the IRP engine sensor values  46  again in the next iteration. The iterative process performs continuous sampling and adjustments during the flight.  
         [0024]     The iterative process of the present invention allows the embedded model of the engine  56  to migrate to a state where the virtual sensors closely match the actual engine sensors. The resulting modified component qualities computed in the embedded model  56  are optimized for the operating state. The quality estimation process is invoked at steady state engine operating conditions. Component quality adjustments are computed and the embedded model is iteratively adjusted. The component quality parameters can then be tracked throughout a flight whenever steady state operating conditions are met.  
         [0025]     The method for computing gains for estimating the component quality adjustments is as follows:  
               Y   ⁡     (   Q   )       =     (             y   1     ⁡     (       q   1     ,   …   ⁢           ,     q   m       )                   y   2     ⁡     (       q   1     ,   …   ⁢           ,     q   m       )               ⋮               y   n     ⁡     (       q   1     ,   …   ⁢           ,     q   m       )             )             [   1   ]             
        where n is the number of computed sensor values;     m is the number of quality parameters;     y i  corresponds to a sensor value, (e.g., y 1  is fan speed, y 2  is core speed, etc.); and     q i  are the component quality parameters (e.g., q 1 , is the fan flow modifier,     q 2  is the fan efficiency, etc.)        
 
         [0031]     Y is also a function of the states of the model, as well as other model values and variables, but quality parameters are the only parameters with which the system is concerned in this analysis. The first step for obtaining the set of gains for the iterative quality estimation algorithm is computing the Jacobian of Y(Q). The Jacobian of Y(Q) is defined as  
               J   Y     =     (             ∂     y   1         ∂     q   1             ⋯           ∂     y   1         ∂     q   m                 ⋮       ⋰       ⋮               ∂     y   n         ∂     q   1             ⋯           ∂     y   n         ∂     q   m               )             [   2   ]             
 
         [0032]     Given actual engine sensor values s 1 , s 2 , . . . , s n , and the model computed sensor values y 1 , y 2 , . . . , y n , the error vector E is computed as follows:
 
 E =( e   1    . . . e   n )=( s   1 −y 1    . . . s   n   −y   n )  [3]
 
         [0033]     A delta change to the quality parameters is computed as the product of a design constant, or gain value h, the row vector E, and the matrix J Y , or:
 
ΔQ=(Δq 1  . . . Δq m )=hEJ Y   [4]
 
         [0034]     At each iteration of the algorithm, ΔQ is computed based on the current values of E, and the quality parameter vector Q is updated according to the following equation:
 
[ Q]   k+1   =[Q]   k   +ΔQ   [5]
        where [Q] k  is the quality parameter vector at iteration step k.        
 
         [0036]     In another aspect of the invention, the iterative process described above is modified to provide a fault detection and isolation process in  FIG. 3 . The embedded engine model  56  outputs a set of component quality adjustments  80 . The component quality adjustments  80  are compared to previous missions once per flight, as indicated at step  82 . Thereafter, the component quality adjustments  80  are compared to the current take off parameters at step  84 . The output of both steps  82  &amp;  84  is connected to a fault detection and isolation classifier  86 . The output from the fault detection and isolation classifier  86  is then transmitted for diagnosis  88 .  
         [0037]     Referring next to  FIG. 4 , a graphical representation shows the changes in quality deltas during an exemplary take off, for HPT and LPT flow and efficiency parameters. The graph  200  is a comparison of F414 engine data from an ASMET test engine in which the engine cycle was driven from a low power level  202 , then a power burst to maximum after-burner (A/B)  204  followed by a power reduction to stable Intermediate Rated Power (IRP)  206 . A tracking filter was employed to match the component level model (CLM) to the ASMET engine test data. The results plotted by the graph indicate the percentage change in the quality deltas or differences during the match up engine run.  
         [0038]     HPT flow quality delta  210  in the stable IRP range increased between about 1.25 percent and 2.25 percent. LPT flow quality delta  212  was approximately even in the stable IRP range at about plus 1.1 percent. HPT efficiency  214  in the stable IRP range was steady at about minus 2.9 percent, and LPT efficiency  216  in the stable IRP range varied between about minus 2.7 percent and minus 3.5 percent.  
         [0039]     While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims.