Patent Description:
Gas turbine engines, such as those utilized in commercial and military aircraft, include a compressor section that compresses air, a combustor section in which the compressed air is mixed with a fuel and ignited, and a turbine section across which the resultant combustion products are expanded. The expansion of the combustion products drives the turbine section to rotate. As the turbine section is connected to the compressor section via one or more shaft, the rotation of the turbine section further drives the compressor section to rotate. In some examples, a fan is also connected to the shaft and is driven to rotate via rotation of the turbine as well.

Any given gas turbine engine is constructed of a significant number of individually manufactured components. Among the individually manufactured components can be blades, vanes, panels, outer air seals, and the like. In some cases, such as with a compressor rotor or a fan, multiple substantially identical components can be utilized in a single engine assembly.

Engine operations within varied regions can have substantial impacts on component life cycles due to the engine operations and service conditions in extreme regions. By way of example, sand ingestion in hot climates can result in faster damage to individual airfoils due to pitting. In an alternate example, an extremely cold operation zone may result in minimal pitting, but damage could be localized to supply lines that are subjected to substantially more freeze/thaw cycles than in the hot environment. <CIT> discloses method for determining schedule maintenance of gas turbine engine components. <CIT> relates to automated predictive maintenance options particularly for gas turbine engines.

According to an aspect of the invention, there is provided a method for maintaining a gas turbine engine component as recited in claim <NUM>.

The invention is as defined in independent claim <NUM>.

Further, optional, features are recited in each of claims <NUM> to <NUM>.

<FIG> schematically illustrates a gas turbine engine <NUM> including a compressor section <NUM>, a combustor section <NUM>, and a turbine section <NUM>. Positioned fore of the compressor section <NUM> is a fan <NUM>. The compressor section <NUM> includes a low pressure compressor <NUM> and a high pressure compressor <NUM>. Similarly, the turbine section <NUM> includes a high pressure turbine <NUM> and a low pressure turbine <NUM>. The high pressure turbine <NUM> is connected to the high pressure compressor <NUM> via a first shaft <NUM> and rotation of the high pressure turbine <NUM> drives rotation of the high pressure compressor <NUM>. Similarly, the low pressure compressor <NUM> is connected to the low pressure turbine <NUM> via a second shaft <NUM> and the rotation of the low pressure turbine <NUM> drives rotation of the low pressure compressor <NUM>. In the example engine <NUM> of <FIG>, the fan <NUM> is connected to, and driven by, the first shaft <NUM> via a gear system <NUM>.

One of skill in the art will appreciate that in alternative examples, an alternative number of turbines <NUM>, <NUM> and compressors <NUM>, <NUM> can be utilized and still achieve similar results. Similarly, the fan <NUM> can be driven via a direct connection to the shaft <NUM> instead of the geared system <NUM>, or driven in any other known manner.

Each of the fan <NUM>, the compressors <NUM>, <NUM> and the turbines <NUM>, <NUM> are constructed from multiple substantially identical components which can include rotor blades, vanes, blade outer air seals, and the like. Each component is constructed according to a set of multiple design parameters. Each of those design parameters is given a range of acceptable values to account for manufacturing variations, as well as tolerances with the engine structure.

Existing component qualification systems determine the as-manufactured dimensions of each manufactured component, compare the measured dimensions of the manufactured component to the design dimensions, including tolerances, and determine that the component is "acceptable" when every parameter falls within the as designed specification. The type of manufacturing process used to make the part, and the relationship between each measured parameter and each other measured parameter is not included within the existing analysis. In some examples, such as those where the manufacture of each component is particularly expensive, unqualified components are manually reviewed to determine if the component may still be acceptable for use within an engine despite including one or more parameter that is outside of the as designed tolerances. In alternative examples, the unqualified component can be scrapped or reworked to meet tolerances.

One such component in the example of <FIG> is the fan <NUM>. Referring to <FIG>, the fan <NUM> includes a rotor <NUM> having an array or row <NUM> of airfoils or blades <NUM> that extend circumferentially around, and are supported by, the fan hub <NUM>. Any suitable number of fan blades <NUM> may be used in a given application. The hub <NUM> is rotatable about the engine axis A. The array <NUM> of fan blades <NUM> are positioned about the axis A in a circumferential or tangential direction Y. Each of the blades <NUM> includes an airfoil body that extends in a radial span direction R from the hub <NUM> between a root <NUM> and a tip <NUM>, in a chord direction H (axially and circumferentially) between a leading edge <NUM> and a trailing edge <NUM> and in a thickness direction T between a pressure side P and a suction side S.

Each blade <NUM> has an exterior surface <NUM> providing a contour that extends from the leading edge <NUM> aftward in a chord-wise direction H to the trailing edge <NUM>. The exterior surface <NUM> of the fan blade <NUM> generates lift based upon its geometry and directs flow along the core flow path and bypass flow path. The fan blade <NUM> may be constructed from a composite material, or an aluminum alloy or titanium alloy, or a combination of one or more of these. Abrasion-resistant coatings or other protective coatings may be applied to the fan blade <NUM>.

A chord, represented by chord dimension (CD), is a straight line that extends between the leading edge <NUM> and the trailing edge <NUM> of the blade <NUM>. The chord dimension (CD) may vary along the span of the blade <NUM>. The row <NUM> of blades <NUM> also defines a circumferential pitch (CP) that is equivalent to the arc distance between the leading edges <NUM> or trailing edges <NUM> of neighboring blades <NUM> for a corresponding span position. The root <NUM> is received in a correspondingly shaped slot in the hub <NUM>. The blade <NUM> extends radially outward of a platform <NUM>, which provides the inner flow path. The platform <NUM> may be integral with the blade <NUM> or separately secured to the hub <NUM>, for example. A spinner <NUM> is supported relative to the hub <NUM> to provide an aerodynamic inner flow path into the fan section <NUM>.

Referring to <FIG>, span positions are schematically illustrated from <NUM>% to <NUM>% in <NUM>% increments to define a plurality of sections <NUM>. Each section at a given span position is provided by a conical cut that corresponds to the shape of segments the bypass flowpath or the core flow path, as shown by the large dashed lines (shown in <FIG>). In the case of a fan blade <NUM> with an integral platform, the <NUM>% span position corresponds to the radially innermost location where the airfoil meets the fillet joining the airfoil to the platform <NUM>. In the case of a fan blade <NUM> without an integral platform, the <NUM>% span position corresponds to the radially innermost location where the discrete platform <NUM> meets the exterior surface of the airfoil (shown in <FIG>). A <NUM>% span position corresponds to a section of the blade <NUM> at the tip <NUM>.

In some examples, each of the blades <NUM> defines a non-linear stacking axis <NUM> (shown in <FIG>) in the radial direction R between the tip <NUM> and the inner flow path location or platform <NUM>. For the purposes of this disclosure, "stacking axis" refers to a line connecting the centers of gravity of airfoil sections <NUM>. In some examples, each fan blade <NUM> is specifically twisted about a spanwise axis in the radial direction R with a corresponding stagger angle at each span position and is defined with specific sweep and/or dihedral angles along the airfoil <NUM>. Airfoil geometric shapes, stacking offsets, chord profiles, stagger angles, sweep, dihedral angles, and surface shape in an X, Y, Z coordinate system, among other associated features, can be incorporated individually or collectively to improve characteristics such as aerodynamic efficiency, structural integrity, and vibration mitigation, for example.

In some examples, the airfoil <NUM> defines an aerodynamic dihedral angle D (simply referred to as "dihedral") as schematically illustrated in <FIG>. An axisymmetric stream surface S passes through the airfoil <NUM> at a location that corresponds to a span location (<FIG>). For the sake of simplicity, the dihedral D relates to the angle at which a line L along the leading or trailing edge tilts with respect to the stream surface S. A plane P is normal to the line L and forms an angle with the tangential direction Y, providing the dihedral D. A positive dihedral D corresponds to the line tilting toward the suction side (suction side-leaning), and a negative dihedral D corresponds to the line tilting toward the pressure side (pressure side-leaning).

As can be seen, each individual fan blade <NUM> defines multiple parameters such as chord dimension, radial span length, thickness, contour, circumferential pitch, stacking axis, stagger angle, sweep angle, and dihedral angle. Further, many of the example parameters as well as additional parameters can be required to meet tolerances at each of multiple span positions resulting in a substantial number of parameters, any one of which can disqualify the fan blade <NUM> if it is out of tolerance range under existing manufacturing processes. The design of a part such as the blade determines the manufacturing requirements that should be adhered to by a vendor constructing the blade. The design blue print dimensions are a design mean µd that the vendor must match when producing the part. The standard deviation for the design is σd = <NUM>, for a certain nominal design durability or nominal part life with the nominal design durability or the nominal part life being determined via any conventional process.

Manufacturing variations typically adhere to a normal distribution (N(µ, σ), about the mean µ=µd. When a part is exposed to an operational environment, such as the single route environments discussed herein, the part can change shape due to any number of factors including foreign object damage (FOD), material internal flaws (MF), or environmental and temperature induced reactions. The exposure to operational conditions causes a deviation from the nominal manufactured shape and leads to a degraded performance. The particular deviations for some components can be highly dependent on the operational environment and similar components can require substantially different maintenance depending on the actual usage of a specific component.

While described above with regards to the fan <NUM>, and individual fan blades <NUM>, it should be understood that similar parameters exist for any given blade and/or vane utilized through the engine <NUM>, including those within the compressor section <NUM>, and the turbine section <NUM>. Further, any number of other engine components can have similar numbers of parameters, all of which must be within tolerance, even if the parameters of the given component are not the same as the airfoil parameters described above. It is further recognized that normal wear, or wear as the result of damaging events, can impact the parameters of a fan blade that has been put in service in an engine. As a result of the wear, the fan blade can become disqualified, and a repair operation may be necessary to repair the blade. Further, when an engine including a given component is exposed to a specific wear condition (e.g. a specific aircraft route) repeatedly, the component will experience similar wear as other substantially identical components exposed to that wear condition.

With regards to any given specific component the cumulative performance criteria of the component drives part acceptance rather than adherence to any one particular parameter tolerance or set of parameter tolerances. The cumulative performance is driven by individual shapes and construction, as well as the relationships between individual shapes and constructions.

Under current manufacturing or repair processes, if any of the above described parameters, or any similar parameters that may be necessary for a given component, are out of tolerance at any single point the component will fail inspection (be disqualified) and either be scrapped or provided to an engineering team for manual review. Further it should be understood that the above described parameters are merely exemplary parameters of a fan blade <NUM>, and practical components can include more and different parameters that are subjected to the same level of analysis when qualifying the component.

With reference to each of <FIG>, disclosed herein is an improved system and process for maintaining manufactured parts based on the totality of the part configuration and the expected wear due to expected usage of the manufactured parts, rather than individually based on each parameter at each location on the component. In some examples, one or more parameter may be out of tolerance either due to manufacturing variance or due to wear, but when the component is considered as a whole the part is still in an acceptable configuration. Further exacerbating this is the fact that different manufacturing techniques for any given component (e.g. additive manufacturing vs. casting) can result in different acceptable configurations, or different wear patterns, that may include one or more parameter outside of the as designed tolerances. Similarly, different usages of a component (e.g. long-haul cross transoceanic flights vs short haul intracontinental flights) can result in distinct wear patterns with varying maintenance requirements. Under existing systems, the maintenance schedule is determined according to a worst case wear scenario, without specific regards to the type of wear associated with the specific usage.

In order to ensure overall engine performance meets minimum requirements, a customized repair of the parts is done periodically during scheduled maintenance of the engine. The customized repair procedures ensure that the part and design shape conform to the dimensional requirements described above. However, given the extreme conditions single route parts may be exposed to, the timing required between customized repairs, and the types of customized repairs required can vary dramatically from route to route. The repair mechanisms and schedules should be designed to accommodate individual repair needs and service intervals of a given specific single route component or single environment aircraft.

With continued reference to <FIG>, <FIG> schematically illustrates an exemplary system <NUM> for analyzing a batch of single route parts in order to determine an optimized maintenance schedule. Initially a set of single route parts that were manufactured using a single manufacturing process or uniform combination of processes is identified in an identify single route parts step <NUM>. As used herein, single route parts refers to a set of substantially identical components that are used on a single repeated flight route or set of flight routes. In some examples, the single repeated flight route can be a repeatedly run distance, in other examples the single repeated flight route can be a repeatedly run region, and in other examples the single repeated flight route can be a combination of the two. Once a sufficient number of the single route parts have been identified, each parameter of each of the single route parts is measured in an inspect parts step <NUM> after having been operated for a predetermined amount of flight hours. The inspect parts step <NUM> can be performed over a substantial period of time, as components are removed from aircraft engines during standard maintenance.

The output of the inspect parts step <NUM> is a data set that includes a measurement of each parameter of each single route part in the identified set. The single route data set is then provided to a computer system and is used by the computer system to train a part analysis in a "train analysis system" step <NUM> within the train analysis step <NUM>. The computer system develops a variance model that models the variations of an average, or exemplary single route part, for the specific manufacturing process or processes and the specific wear case of the single route for the identified single route part set, based on the set of measured single route parts. Contemporaneously with the variance model, the computer system develops a predictive model, that can predict the change to the behavioral characteristics, such as efficiency, bending, vibration, etc. of a given component based on the specific single route that that component is utilized in. In some examples, the predictive model can be predetermined by performing a similar process on the as-manufactured parts prior to inclusion of the parts within an operating engine. This change is a deviation from the design dimensions and the design intent of the part and increases over time.

The variation model is a dimension reducing model, and describes a large number of observable variables' values using a smaller number of independent, latent variables. A latent variable is a variable whose value depends on our understanding of the latent structure inside the observed data. The latent structure of the data can only be determined from correlation analysis of the observed variables, and the correlation analysis requires observations of multiple as-manufactured parts. The usage of the single route measurements of the set of single route components to create the variance model and the predictive model can be referred to as a principal component analysis (PCA), and provides an accurate model of the actual wear patterns expected from a single specific route. In some examples, the predictive model is a Gaussian Process (GP) model.

Based on the predictive model, and the variance model, the computer system then creates a maintenance schedule in a "create maintenance schedule" step <NUM>. The maintenance schedule compares a function, or set of functions, that defines an acceptable component based on all of its parameters to the expected wear pattern for the single route, and determines the longest on-wing time for the single route component before maintenance is required due to the expected wear pattern. By applying the maintenance schedule to an individual single route part, the computer system can automatically determine a maintenance timing, and expected maintenance operation, in order to maintain the single route part within qualification parameters. Due to the number of parameters (in some cases the number of parameters can exceed <NUM>), and the number of positions on the part where each parameter is to be measured, the functions determined by the computer system are high order functions, and determining whether an individual component meets the functions would be prohibitively time consuming if performed manually.

Parts analyzed using the process described herein adhere to the design intent mean and accepted tolerances that would allow efficient assembly of the component. Typically each part is required to adhere to its design intent, with some variations being acceptable as described above. By way of example, the acceptable variations should be within a 3σ criteria and thus are expected to fall within an exemplary distribution illustrated in <FIG>. Typically, the parts should fall within the <NUM>% region of the normal distribution as per the design intent in order to ensure that part life and component performance metrics are met. As the part or component is introduced into the field, the operational environment effects lead to a deterioration of the component which manifests as a further deviation from the norm.

The analysis described above allows an operator to analyze the deterioration (derivation from normal) of multiple parts over time, and correlate that deterioration with given operational environments. Based on the determined correlations between the deterioration and the single route, a maintenance schedule can be determined corresponding to a given specific single route with the maintenance schedule allowing for each part to be analyzed for compliance and reworked if necessary at the correct intervals.

With continued reference to <FIG>, <FIG> schematically illustrates the "train analysis" step <NUM> of <FIG>. Initially, during the train analysis step <NUM>, the computer system receives all of the measured parameters of each component in the set of single route components in a receive measured parameters step <NUM>. In some examples, the computer system can further determine one or more derived parameter based on a combination or manipulation of one or more of the measured parameters. As described above, each of the single route components in the data set is measured at the same, or similar, number of flight hours.

The full data set is passed to a generate variation model operation <NUM> and a run simulation on components operation <NUM>. During the generate variation model step <NUM>, the computer system determines a single variation model representative of the possible and/or expected variations of the single route component resulting from the particular wear case of the set of single parts. The variation model is representative of the general wear of all of the single route parts in the entirety of the batch. The variation model further represents the expected wear pattern of all other substantially identical components that have approximately the same number of flight hours and have been utilized on the same repeated route. The variation model can include an average figure, standard deviations, tolerances, and the like and be determined using any known process.

During the run simulation on components process <NUM>, the computer system iteratively runs a simulation where each of the single route components is incorporated into a simulated engine, and a computer simulation is run to determine how the single route component is expected to operate within a mathematical model of the engine. The results of the simulation for each single route component are compared and the variations in the parameters between each single route component and each other single route component are correlated with variations in the operation of the corresponding simulation results.

Once the simulation results are completed, the process moves to a combine to predictive model step <NUM>. The correlated variations from the simulation results are applied to the variation model generated in the generate variation model step <NUM> in order to determine a predictive model. The generated predictive model provides a prediction of how a component will wear over time if it is used along the single route, including the wear at fewer flight cycles and greater flight cycles than the number of flight cycles of the training set. In some examples, the predictive model can include a Gaussian process. Once generated, the predictive model is output to the computer system in an output predictive model step <NUM>.

Once the predictive model has been output to the computer system, the computer system uses the predictive model to create a maintenance schedule which can be applied to any substantially identical component created using the same manufacturing process, and exposed to the same single route, as the set of single route components used to train the analysis. As used herein a "maintenance schedule" for a component refers to the frequency that the component is removed for maintenance. In some examples, the maintenance schedule can also include specifically scheduled operations, such as a blending operation, based on the expected wear pattern that the single route component will be exposed to. The maintenance schedule is determined by one or more mathematical functions, each of which relates multiple parameters of the single route component to each other and generates a corresponding output value. In a typical example each of the functions within the maintenance schedule is determined at least partially by a higher order function.

As described herein, the train analysis step <NUM> utilizes a set of single route components, all of which are manufactured using the same manufacturing process and operated on the same, or substantially similar, routes for the same or substantially similar amounts of flight hours. In some examples, the set of single route components includes at least <NUM> analyzed single route components. In further examples, such as ones where the system may need a greater level of accuracy in the maintenance schedule, at least <NUM> analyzed single route components can be utilized. In yet further examples, where additional training of the analysis is desired, an initial set of single route components can be supplemented at a later date with another set of single route components manufactured using the same process, and operated on the single route for the same number of flight hours, so as to increase the level of accuracy of the maintenance schedule.

With continued reference to <FIG>, <FIG> illustrates a process for maintaining an aircraft component utilizing the maintenance schedule determined in the create maintenance schedule step. Initially, a substantially identical component to the previously analyzed components is analyzed and installed in an engine in an install component step <NUM>. The analysis includes determining the as-manufactured parameters of the installed component. Once installed, the as-manufactured parameters of the installed component are applied to the predictive model output by the computer system at the output predictive model step <NUM> of <FIG> in an apply predictive model step <NUM>.

The predictive model applies the expected wear pattern to the as-manufactured components and determines an expected amount of flight hours that the component can be operated before a repair operation is required, provided the component is utilized in the single route. The number of flight hours is output in an output maintenance schedule step <NUM>. In some examples, the maintenance schedule output in step <NUM> can further include an expected repair type (e.g. blending). In yet further examples, the expected repair operation can include a specific operation of the expected repair type.

Once the maintenance schedule has been output, the engine is operated for the predetermined number of flight hours along the single flight route in an operate engine step <NUM>. Once the predetermined number of flight hours, or other engine cycles, has elapsed, the component is removed and subjected to maintenance in a repair part step <NUM>. In some examples, the part is repaired according to the suggested repair procedure or operation without further analyzing the part. Such examples can be utilized when the set of single route components used to train the analysis is sufficient to provide a high accuracy. By way of example, the set could include at least <NUM> data points. In other examples, the component is removed at the determined time and is analyzed using an as-run analysis system to determine what specific repair operations are necessary to bring the part within qualifications. Once repaired, the part is returned to the aircraft engine, and the engine is returned to operation in a reinstall part step <NUM>.

Claim 1:
A method for maintaining a gas turbine engine component comprising:
training, by a computer system, a maintenance schedule module via:
receiving a set of measured parameters for each gas turbine engine component in a set of single route gas turbine engine components, wherein each single gas turbine engine component has been exposed to a substantially identical route for a substantially identical duration and was manufactured using one of a single manufacturing process and a uniform combination of processes,
generating a variation model of the set of single route gas turbine engine components, wherein the variation model models variations of single route gas turbine engine components for the specific manufacturing process or processes and a specific wear case of the single route for the set of substantially identical gas turbine engine components, based on the measured set of substantially identical gas turbine engine components,
iteratively running a simulation where each of the single route components is incorporated into a simulated engine, and a computer simulation is run to determine how each single route gas turbine engine component is expected to operate within a mathematical model of the simulated engine, and
correlating variations in the set of parameters between each of the single route gas turbine engine components and each other single route component with variations in the operation of the corresponding simulation results ,
generating a predictive model based on the variations where the correlated variations from the simulation results are applied to the variation model, wherein the predictive model provides a prediction of an expected wear pattern of a component if the component is used along the single route; and
wherein the method further comprises:
generating a maintenance schedule, by the computer system, for a second gas turbine engine component, substantially identical to each gas turbine engine component in the set of single route gas turbine engine components by the predictive model applying the expected wear pattern to the as-manufactured second gas turbine engine component and determining an expected amount of flight hours that the component can be operated before a repair operation is required, provided the component is utilized in the single route.