Gas turbine engine fleet performance deterioration

A method for determining gas turbine engine fleet performance deterioration includes receiving data indicative of deterioration parameter values for a plurality of gas turbine engines. The method also includes determining an average deterioration parameter value for each gas turbine engine at a plurality of intervals, and further determining an individual engine slope between the average deterioration parameter value at each adjacent interval for each gas turbine engine. The method also includes determining a fleet average slope between each adjacent interval based on the individual engine slopes between each adjacent interval, the determined fleet average slopes being usable to determine a performance deterioration of a gas turbine engine in the fleet.

FIELD

The present subject matter relates generally to a system and method for determining performance deterioration of a fleet of gas turbine engines.

BACKGROUND

A gas turbine engine generally includes, in serial flow order, a compressor section, a combustion section, and a turbine section. In operation, ambient air is provided to an inlet of the compressor section where one or more axial compressors progressively compress the air until it reaches the combustion section. Fuel is mixed with the compressed air and burned within the combustion section to provide combustion gases. The combustion gases are routed from the combustion section to the turbine section. The flow of combustion gasses through the turbine section drives the turbine section.

The longer a gas turbine engine is in operation, however, a performance or efficiency of the gas turbine engine degrades. For example, performance deterioration may result in a loss in efficiency, increased emissions, etc. Performance deterioration may be a result of components within a flowpath of the engine becoming covered with foreign particles, eroded, corroded, rusted, damaged, etc. After a certain amount of performance deterioration, the gas turbine engine must go in for service, repair, or overhaul. When the gas turbine engine is one of a fleet of similar gas turbine engines (e.g., one of a plurality of the same make and model gas turbine engines), information may be collected from the fleet of gas turbine engines to determine a performance deterioration model for the gas turbine engines. The performance deterioration model may be used to predict a gas turbine engine's performance deterioration based on, e.g., an amount of time the particular gas turbine engine in the fleet has been operating.

However, current data collection and analysis techniques can result in performance deterioration models with room for improvement in accuracy. Accordingly, a more accurate method for determining performance deterioration of the gas turbine engine in a fleet of gas turbine engines would be useful. With increased accuracy, the gas turbine engines may operate for longer periods of time prior to being taken off wing for service, repair, or overhaul.

BRIEF DESCRIPTION

In one exemplary aspect of the present disclosure, a computer-implemented method for determining gas turbine engine fleet performance deterioration is provided. The method includes receiving, by one or more computing devices, data indicative of deterioration parameter values for a plurality of gas turbine engines. The method also includes determining, by the one or more computing devices, an average deterioration parameter value for each gas turbine engine at a plurality of intervals. The method also includes determining, by the one or more computing devices, an individual engine slope between the average deterioration parameter value at each adjacent interval for each gas turbine engine. The method also includes determining, by the one or more computing devices, a fleet average slope between each adjacent interval based on the determined individual engine slopes between each adjacent interval. Additionally, the method includes providing, by one or more computing devices, a signal to a graphical user interface indicative of a gas turbine engine deterioration based at least in part on the determined fleet average slopes.

In an exemplary embodiment of the present disclosure, a control system for determining gas turbine engine fleet performance deterioration is provided. The control system includes one or more memory devices, and one or more processors. The one or more memory devices store instructions that when executed by the one or more processors cause the one or more processors to perform operations. The operations include receiving data indicative of deterioration parameter values for a plurality of gas turbine engines. The operations also include determining an average deterioration parameter value for each gas turbine engine at a plurality of intervals. The operations also include determining an individual engine slope between the average deterioration parameter value at each adjacent interval for each gas turbine engine. The operations also include determining a fleet average slope between each adjacent interval based on the determined individual engine slopes between each adjacent interval. The operations also include providing a signal to a graphical user interface indicative of a gas turbine engine deterioration based at least in part on the determined fleet average slopes.

In another exemplary embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium has stored thereon executable computer code comprising a set of instructions that, when executed by a computer, causes the computer to perform operations. The operations include receiving data indicative of deterioration parameter values for a plurality of gas turbine engines. The operations further include determining an average deterioration parameter value for each gas turbine engine at a plurality of intervals, and determining an individual engine slope between the average deterioration parameter value at each adjacent interval for each gas turbine engine. The operations further include determining a fleet average slope between each adjacent interval based on the determined individual engine slopes between each adjacent interval. The operations further include providing a signal to a graphical user interface indicative of a gas turbine engine deterioration based at least in part on the determined fleet average slopes.

DETAILED DESCRIPTION

The present disclosure is generally related to a system and method for determining performance deterioration of a fleet of gas turbine engines. The method includes receiving data indicative of deterioration parameter values for plurality of gas turbine engines. The data indicative of deterioration parameter values may be data indicative of, e.g., exhaust gas temperature values of the plurality of gas turbine engines. The method also includes determining an average deterioration parameter value for each gas turbine engine at a plurality of intervals, and subsequently determining an individual engine slope between the average deterioration parameter values at each adjacent interval for each gas turbine engine. The individual engine slopes are then used to determine fleet average slopes between each adjacent interval, which may then be used to determine a deterioration model for the fleet of gas turbine engines. Determining the individual engine slopes between adjacent intervals first, and subsequently determining the fleet average slopes between adjacent intervals, has the technical effect of determining a more accurate deterioration model for the fleet of gas turbine engines. Having a more accurate deterioration model for the fleet of gas turbine engines may allow for the fleet of gas turbine engines to operate for a longer period of time between services, overhauls, etc.

Referring now to the drawings, wherein identical numerals indicate the same elements throughout the figures,FIG. 1provides a schematic cross-sectional view of a propulsion engine in accordance with an exemplary embodiment of the present disclosure. In certain exemplary embodiments, the propulsion engine may be configured a high-bypass turbofan jet engine100, herein referred to as “turbofan100.” As shown inFIG. 1, the turbofan100defines an axial direction A (extending parallel to a longitudinal centerline101provided for reference), a radial direction R, and a circumferential direction C (extending about the axial direction A; not shown). In general, the turbofan100includes a fan section102and a core turbine engine104disposed downstream from the fan section102.

The exemplary core turbine engine104depicted generally includes a substantially tubular outer casing106that defines an annular inlet108. The outer casing106encases, in serial flow relationship, a compressor section including a second, booster or low pressure (LP) compressor110and a first, high pressure (HP) compressor112; a combustion section114; a turbine section including a first, high pressure (HP) turbine116and a second, low pressure (LP) turbine118; and a jet exhaust nozzle section120. The compressor section, combustion section114, and turbine section together define a core air flowpath121extending from the annular inlet108through the LP compressor110, HP compressor112, combustion section114, HP turbine section116, LP turbine section118and jet nozzle exhaust section120. A first, high pressure (HP) shaft or spool122drivingly connects the HP turbine116to the HP compressor112. A second, low pressure (LP) shaft or spool124drivingly connects the LP turbine118to the LP compressor110.

For the embodiment depicted, the fan section102includes a variable pitch fan126having a plurality of fan blades128coupled to a disk130in a spaced apart manner. As depicted, the fan blades128extend outwardly from disk130generally along the radial direction R. Each fan blade128is rotatable relative to the disk130about a pitch axis P by virtue of the fan blades128being operatively coupled to a suitable actuation member132configured to collectively vary the pitch of the fan blades128, e.g., in unison. The fan blades128, disk130, and actuation member132are together rotatable about the longitudinal axis12by LP shaft124across a power gear box134. The power gear box134includes a plurality of gears for stepping down the rotational speed of the LP shaft124to a more efficient rotational fan speed.

Referring still to the exemplary embodiment ofFIG. 1, the disk130is covered by rotatable front hub136aerodynamically contoured to promote an airflow through the plurality of fan blades128. Additionally, the exemplary fan section102includes an annular fan casing or outer nacelle138that circumferentially surrounds the fan126and/or at least a portion of the core turbine engine104. The nacelle138is mechanically coupled to the core turbine engine104by a plurality of circumferentially-spaced outlet guide vanes140. A downstream section142of the nacelle138extends over an outer portion of the core turbine engine104so as to define a bypass airflow passage144therebetween.

Additionally, the exemplary turbofan100depicted includes a plurality of sensors146for collecting data indicative of various operating parameters of the turbofan100. Specifically, the turbofan100includes a sensor146positioned within, adjacent to, or proximate, the exhaust120, such that the sensor146may collect data indicative of an exhaust gas temperature of the turbofan100. Although not depicted, the turbofan100may further include sensors for determining a core speed (i.e., a rotational speed of the HP spool122), a fuel flow, and/or temperatures along the core air flowpath121.

Further, the turbofan100includes a computing device148, depicted schematically, which may be utilized to control certain operations of the turbofan100. For example, the computing device148may be utilized to control a fuel flow rate to a combustor of the combustion section114during operation. Additionally, the computing device148may be operably connected to the sensors146, such that the computing device148may receive data indicative of the operating parameters collected by the sensors146.

It should be appreciated, however, that the exemplary turbofan engine100depicted inFIG. 1is provided by way of example only, and that in other exemplary embodiments, the turbofan engine100may have any other suitable configuration. For example, in other exemplary embodiments, the turbofan engine100may instead include a reduction gear system or configuration, may instead be configured as a direct drive turbofan engine, may include any other suitable number of compressors, turbines, and/or spools, etc. Furthermore, in other exemplary embodiments, the gas turbine engine may not be configured as a turbofan engine and instead may be configured as a turboprop engine, a turbojet engine, a turboshaft engine, or any other suitable aeronautical gas turbine engine. Furthermore, still, in other exemplary embodiments, the gas turbine engine may not be configured as an aeronautical gas turbine engine, and instead may be configured as a land-based gas turbine engine, e.g., for power generation, or an aeroderivative gas turbine engine, such as a nautical gas turbine engine.

Referring now toFIG. 2, the turbofan engine100ofFIG. 1may include or be operably connected to a control system150. As shown, the control system150can include one or more computing device(s)152. Notably, the computing device148depicted inFIG. 1may be one of the one or more computing device(s)152of the exemplary control system150depicted inFIG. 2. The computing device(s)152may be configured to execute one or more methods in accordance with exemplary aspects of the present disclosure (such as method200described below with reference toFIG. 3). The computing device(s)152can include one or more processor(s)154and one or more memory device(s)156. The one or more processor(s)154can include any suitable processing device, such as a microprocessor, microcontroller, integrated circuit, logic device, or other suitable processing device. The one or more memory device(s)156can include one or more computer-readable media, including, but not limited to, non-transitory computer-readable media, RAM, ROM, hard drives, flash drives, or other memory devices.

The one or more memory device(s)156can store information accessible by the one or more processor(s)154, including computer-readable instructions158that can be executed by the one or more processor(s)154. The instructions158can be any set of instructions that when executed by the one or more processor(s)154, cause the one or more processor(s)154to perform operations. The instructions158can be software written in any suitable programming language or can be implemented in hardware. In some embodiments, the instructions158can be executed by the one or more processor(s)154to cause the one or more processor(s)154to perform operations, such as the operations for regulating fuel flow, as described herein, and/or any other operations or functions of the one or more computing device(s)152. Additionally, and/or alternatively, the instructions158can be executed in logically and/or virtually separate threads on processor154. The memory device(s)156can further store data160that can be accessed by the processors154.

The computing device(s)152can also include a communications interface162used to communicate, for example, with the components of turbofan engine100and/or other computing device(s)152. The communications interface162can include any suitable components for interfacing with one more communications network(s), including for example, transmitters, receivers, ports, controllers, antennas, or other suitable components. Control system150may also be in communication (e.g., via communications interface162) with the various sensors, such as sensors146described above, and may selectively operate turbofan engine100in response to user input and feedback from these sensors.

The technology discussed herein makes reference to computer-based systems and actions taken by and information sent to and from computer-based systems. It should be appreciated that the inherent flexibility of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. For instance, processes discussed herein can be implemented using a single computing device or multiple computing devices working in combination. Databases, memory, instructions, and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel.

In certain exemplary embodiments, the control system150may be configured to receive information indicative of a fleet of gas turbine engines each including or operable with a separate computing device152. For example, the control system150may be operably connected to computing devices152of the fleet of gas turbine engines. In certain exemplary embodiments, the fleet may include at least ten gas turbine engines, at least twenty gas turbine engines, or more. Each of the gas turbine engines in the fleet may be the same make and model gas turbine engine, and may in certain embodiments be configured in the same or similar manner as the turbofan100ofFIG. 1. However, in other exemplary embodiments, the gas turbine engines in the fleet may alternatively be configured in any other suitable manner, or as any other suitable type of gas turbine engine. Such a configuration may allow for the control system to determine information regarding the fleet of gas turbine engines, such as performance deterioration of the gas turbine engines in the fleet, as described below.

Referring now toFIG. 3, a method200for determining gas turbine engine fleet performance deterioration is provided. The method200may be a computer-implemented method implemented using the control system150ofFIG. 2. The method200ofFIG. 3may allow for more accurate determinations of expected performance deterioration of gas turbine engines in a fleet of gas turbine engines.

The method200includes at (202) receiving, by the one or more computing devices, data indicative of deterioration parameter values for a plurality of gas turbine engines. For instance, the one or more computing devices152may receive data indicative of deterioration parameter values for the plurality of gas turbine engines. For example, in certain exemplary aspects, the deterioration parameter values may be exhaust gas temperature values, and receiving data indicative of deterioration parameter values for a plurality of gas turbine engines at (202) may include receiving data from one or more sensors, such as from one or more exhaust gas temperature sensors146, of the respective gas turbine engines. It should be appreciated, however, that in other exemplary aspects, the deterioration parameter value may instead be any other gas turbine engine parameter which correlates to a performance deterioration of the gas turbine engine. For example, the deterioration parameter value may instead be core speed values, fuel flow values (e.g., cruise fuel flow rate values), and/or stall margin values.

Furthermore, for the exemplary method200ofFIG. 3, receiving data indicative of the deterioration parameter values for the plurality of gas turbine engines at (202) further includes at (204) removing outliers in the data received at (202). For instance, the one or more computing devices152may remove outliers in the data received indicative of the deterioration parameter values for the plurality of gas turbine engines. More specifically, for the exemplary aspect depicted inFIG. 3, removing outliers in the data at (204) may include removing outliers following a generalized extreme studentized deviate test procedure. It should be appreciated, however, that in other exemplary aspects, any other suitable procedures or methods may be utilized for removing outliers in the data at (204). Accordingly, the method200may at (204) remove data points that are likely incorrectly measured data or other data points that would negatively affect an accuracy of the method200.

Referring still toFIG. 3, the exemplary method200further includes at (206) determining an average deterioration parameter value for each gas turbine engine at a plurality of intervals, and at (208) determining an individual engine slope between the average deterioration parameter value at each adjacent interval for each gas turbine engine. For instance, the one or more computing devices152may determine the average deterioration parameter value for each gas turbine engine at the plurality of intervals, and further may determine an individual engine slope between the average deterioration parameter value at each adjacent interval for each gas turbine engine.

The plurality of intervals are for the exemplary aspect depicted a plurality of preset intervals, the intervals constant for each of the plurality of gas turbine engines. In certain exemplary aspects, the plurality of intervals may be a plurality of time intervals indicative of a time on wing for each of the respective gas turbine engines. In other exemplary aspects, however, the plurality of intervals may be a plurality of numerical intervals indicative of a number of engine cycles of the respective gas turbine engines.

By way of example, the above aspects of the exemplary method200will now be described with reference toFIG. 4.FIG. 4provides a graph300plotting deterioration parameter values for a plurality of gas turbine engines illustrating certain exemplary aspects of the exemplary method200ofFIG. 3. Specifically, the exemplary graph300ofFIG. 4plots deterioration parameter values (Y-axis,302) for three gas turbine engines across various times on wing (X-axis,304). Of course, in other exemplary aspects, the method200may apply to a fleet of any other suitable number of gas turbine engines, and with any other suitable number of deterioration parameter values for each of such gas turbine engines across any suitable number of intervals.

Each of the deterioration parameter values306,308,310for each of the gas turbine engines may be received by the one or more computing devices at (202). Similarly, the average deterioration parameter values312,314,316for each of the gas turbine engines may be determined by the one or more computing devices at (206).

It will be appreciated that determining the average deterioration parameter values for each of the gas turbine engines at (206) may include determining an average of the deterioration parameter values closest to each respective interval, for so long as the values are available. For example, in the exemplary aspect depicted in the graph300ofFIG. 4, each interval is a preset time on wing, fixed for each of the gas turbine engines. Further, each interval may be indicative of a certain time interval. For example, each of the intervals may be representative of a multiple of two hundred (200) hours on wing. For example, a first time interval, t0, may be indicative of one hundred (100) hours on wing; a second interval, t1, may be indicative of three hundred (300) hours on wing; a third interval, t2, may be indicative of five hundred (500) hours on wing; etc. Further, with the exemplary aspect depicted in the graph300ofFIG. 4, determining the average deterioration parameter values312,314,316for each of the gas turbine engines at (206) may include determining the average deterioration parameter values312,314,316at the first time interval, t0, for each of the gas turbine engines by averaging the deterioration parameter values306,308,310for hours zero (0) through two hundred (200); determining the average deterioration parameter values312,314,316at the second interval, t1, for each of the gas turbine engines by averaging the deterioration parameter values306,308,310for hours two hundred (200) through four hundred (400); determining the average deterioration parameter values312,314,316at the third interval, t3, for each of the gas turbine engines by averaging the deterioration parameter values306,308,310for hours four hundred (400) to six hundred (600), if available; etc.

Moreover, for the exemplary aspect depicted in the graph300ofFIG. 4, the first gas turbine engine has been operating for the longest period of time, having the most deterioration parameter values306across the longest span of time on wing. The second gas turbine engine has been operating for less amount of time than the first gas turbine engine, having less deterioration parameter values308across a smaller span of time on wing. Further, the third gas turbine engine has been operating for less amount of time than the second gas turbine engine, having still less deterioration parameter values310across a still smaller span of time on wing.

Referring still also to the exemplary method200depicted inFIG. 3, it will be appreciated that in certain exemplary aspects, more information may be available for certain gas turbine engines as compared to others within the fleet. Accordingly, determining the average deterioration parameter values for each of the gas turbine engines at (206) of the exemplary method200, may include determining average deterioration parameter values for certain gas turbine engines at a larger number of intervals as compared to other gas turbine engines within the fleet. For example, for the exemplary aspect depicted in the graph300ofFIG. 4, determining the average deterioration parameter value for each of the gas turbine engines at (206) includes: determining the average deterioration parameter value312for the first gas turbine engine at a first number of intervals, n1; determining the average deterioration parameter value314for the second gas turbine engine at a second number of intervals, n2; and determining the average deterioration parameter value316for the third gas turbine engine at a third number of intervals, n3. For the exemplary aspect depicted in the graph300ofFIG. 4, the third number of intervals, n3(i.e., two intervals), is less than the second number of intervals, n2(i.e., three intervals), and the second number of intervals, n2, is less than the first number of intervals, n3(i.e., four intervals).

As is discussed above, the exemplary method200ofFIG. 3additionally includes at (208) determining an individual engine slope between the average deterioration parameter values at each adjacent interval for each gas turbine engine. Referring again specifically to the exemplary aspect depicted in the graph300ofFIG. 4, the determination at (208) with respect to the first gas turbine engine includes determining a first slope318between the average deterioration parameter value312at the second time interval, t1, and first time interval, t0; determining a second slope320between the average deterioration parameter values312at the third time interval, t2and the second time interval, t1; and determining a third slope322between the average deterioration parameter values312at the fourth time interval, t3and the third time interval, t2. The determination at (208) with respect to the second gas turbine engine includes determining a first slope324between the average deterioration parameter values314at the second time interval, t1and first time interval, t0; and determining a second slope326between the average deterioration parameter values314at the third time interval, t2and the second time interval, t1. Similarly, the determination at (208) with respect to the third gas turbine engine includes determining a first slope328between the average deterioration parameter values316at the second time interval, t1and first time interval, t0.

Of course, no slopes are determined for the gas turbine engines between intervals were no deterioration parameter values, and thus no average deterioration parameter values, are available. Accordingly, as there are no deterioration parameter values308for the second gas turbine engine proximate the fourth time interval, t3, or deterioration parameter values310for the third gas turbine engine proximate the third time interval, t2or fourth time interval, t3, no individual engine slopes are determined at (208) for these gas turbine engines between intervals adjacent to these time intervals.

Referring now back toFIG. 3, it should be appreciated that in certain exemplary aspects, one or more of the gas turbine engines may not include sufficient data proximate a first interval to accurately determine a value, or an initialization point, at the first interval. Accordingly, for the exemplary aspect depicted, the method200further includes at (210) determining, by one or more computing devices, an initialization point at a first interval for an individual gas turbine engine of the plurality of gas turbine engines. More particularly, for the exemplary aspect ofFIG. 3, determining the initialization point at (210) further includes at (212) determining a plurality of data points indicative of the engine deterioration parameter for the individual gas turbine engine; defining at (214) a linear fit line of the plurality of data points for the individual gas turbine engine; and determining at (216) the initialization point at the first interval based on a value of the linear fit line for the individual gas turbine engine at the first interval.

For instance, the one or more computing devices152may determine the initialization at the first interval for an individual gas turbine engine, and more particularly, the one or more computing devices152may: determine a plurality of data points indicative of the engine deterioration parameter for the individual gas turbine engine; define a linear fit line of the plurality of data points for the individual gas turbine engine; and determine the initialization point at the first interval based on the value of the linear fit line for the individual gas turbine engine at the first interval.

By way of example, certain of the above aspects of the exemplary method200will now be described with reference toFIG. 5.FIG. 5provides a graph350plotting data indicative of an engine deterioration parameter for an individual gas turbine engine. Specifically, the graph350depicts a plurality of data points352indicative of the deterioration parameter (Y-axis,354) for the individual gas turbine engine at various times on wing (X-axis,356). Notably, in at least certain exemplary embodiments, each of the data points352is indicative of the engine deterioration parameter may be a rolling average of a plurality of engine deterioration parameter values. For example, in at least certain exemplary embodiments, each of the data points352indicative of the engine deterioration parameter may be a rolling average of the least five (5) engine deterioration parameter values, or of at least ten (10) engine deterioration parameter values. The graph350further depicts a linear fit line358of the plurality of data points352. The linear fit line358may be determined in any suitable manner, such as, for example, using a least mean square criterion or any other suitable manner. Regardless, an initialization point360, i.e., a deterioration parameter value at the first time interval, t0, may be determined by locating a value/location at which the linear fit line358intersects the first time interval, t0.

Notably, for the embodiment depicted, each of the data points352indicative of the engine deterioration parameter are located past the first interval, or rather, for the exemplary aspect depicted, at a time past the first time interval, t0. Such may be due to the information being lost, the gas turbine engine not being online during initial operation, or any other reason. Accordingly, determining the initialization point360for the exemplary gas turbine engine plotted in the graph350ofFIG. 5includes projecting the linear fit line358across the first time interval, t0, or rather, back in time across the first time interval, t0.

Referring again back toFIG. 3, the exemplary method200additionally includes at (218) determining a fleet average slope between each adjacent interval based on the determined individual engine slopes between each adjacent interval. More specifically, determining the fleet average slope at (218) may include averaging each of the individual engine slopes determined at (208) available between adjacent intervals. For instance, the one or more computing devices152may determine the fleet average slope between each adjacent interval based on the determined individual engine slopes between each adjacent interval, and more particularly, the one or more computing devices152may determine the fleet average slope by averaging each of the individual engine slopes available between adjacent intervals.

Further, the exemplary method200includes at (220) determining a fleet deterioration model based at least in part on the fleet average slopes determined between each adjacent interval at (218). For instance, the one or more computing devices152may determine the fleet deterioration model based at least in part on the fleet average slopes determined between each adjacent interval. As will be appreciated, determining the fleet deterioration model at (220) may include, in certain exemplary aspects, combining the fleet average slopes determined between each adjacent interval to determine a fleet deterioration line. The fleet deterioration line may simply be a combination of each of the fleet average slopes determined between each adjacent interval, or alternatively, may be, e.g., a polynomial fit line for each of the fleet average slopes determined between each adjacent interval. Furthermore, determining the fleet deterioration model at (220) may further include defining an initialization point based on the average initialization points for each of the gas turbine engines.

By way of example, certain of the above aspects of the exemplary method200will now be described with reference toFIG. 6.FIG. 6provides a graph380plotting the fleet average slopes between adjacent intervals for a plurality of gas turbine engines and a resulting fleet average model. More specifically, the graph380ofFIG. 6plots the fleet average slopes based on the data of the plurality of gas turbine engines ofFIG. 4. As is depicted, the fleet average slopes between the adjacent intervals are an average of each of the individual engine slopes available between the respective adjacent intervals. More specifically, for the exemplary aspect depicted inFIG. 6, a first fleet average slope382is provided between the second time interval, t1and the first time interval, t0; a second fleet average slope384is provided between the third time interval, t2and the second time interval, t1; and a third fleet average slope386is provided between the fourth time interval, t3and the third time interval, t2. The first fleet average slope382is an average of the first slope318of the first engine, the first slope324of the second engine, and the first slope328of the third engine. The second fleet average slope384is an average of the second slope320of the first engine, and the second slope326of the second engine. Additionally, the third fleet average slope386is equal to the third slope322of the first engine (i.e., the only available slope between the fourth time interval, t3and the third time interval, t2). Accordingly, when the exemplary method200toFIG. 3is applied to the exemplary aspect depicted in the graphs300,380ofFIGS. 4 and 6, determining the fleet average slopes (i.e., slopes326,328) between the intervals subsequent to the second number of intervals, n2, includes determining the fleet average slopes without use of average deterioration parameter values314,316for the second and third gas turbine engine, and between the intervals subsequent to the third number of intervals, n3, without use of an average deterioration parameter value316of the third gas turbine engine.

Referring again toFIG. 3, the method200further includes at (222) sending a signal to a graphical user interface device indicative of a performance deterioration of one or more of the gas turbine engines of the fleet of gas turbine engines based at least in part on the determined fleet average slopes, and more particularly, based on the determined fleet deterioration model. For instance, the one or more computing devices152may send the signal to the graphical user interface device indicative of the performance deterioration of one or more of the gas turbine engines of the fleet of gas turbine engines based at least in part on the determined fleet average slopes, and more particularly, based on the determined fleet deterioration model.

It should be appreciated, however, that in other exemplary aspects, the method200may in additionally to sending the signal at (222), or in the alternative, take any other suitable action based at least in part on the determined fleet average slopes, and more particularly, based on the determined fleet deterioration model. For example, the method200may additionally or alternatively include scheduling a repair of a gas turbine engine of the plurality of gas turbine engines using the determined fleet average slope, and more particularly, based at least in part on the fleet deterioration model determined at (220). For instance, the one or more control devices152may schedule a repair of a gas turbine engine of the plurality of gas turbine engines based at least in part on the determined fleet average slopes, and more particularly, based at least in part on the fleet deterioration model determined at (220).

Additionally, or alternatively, still, the method200may further include providing a recommendation to a user (such as an owner of the gas turbine engine) to modify wash practices of one or more gas turbine engines in the fleet of gas turbine engines based at least in part on the fleet average slopes, and more particularly, based at least in part on the fleet deterioration model. For instance, the one or more control devices152may provide a recommendation to a user to modify wash practices of one or more gas turbine engines in the fleet of gas turbine engines based at least in part on the fleet average slopes, and more particularly, based at least in part on the fleet deterioration model.

Additionally, or alternatively still, the method200may further include sending an alert to maintenance personnel (such as a maintenance team, e.g., via a graphical user interface) indicating a particular engine needs to be taken off wing for repair or maintenance based at least in part on the fleet average slopes, and more particularly, based at least in part on the fleet deterioration model. For instance, the one or more control devices152may send an alert to maintenance personnel indicating a particular engine needs to be taken off wing for repair or maintenance based at least in part on the fleet average slopes, and more particularly, based at least in part on the fleet deterioration model

Further, the method200may additionally or alternatively include taking a gas turbine engine (or a plurality of gas turbine engines in the fleet of gas turbine engines) out of service, e.g., for repair, servicing, or overhaul, and/or adjusting a planned overhaul workscope for the gas turbine engine (or a plurality of gas turbine engines in the fleet of gas turbine engines) based at least in part on the fleet average slopes, and more particularly, based at least in part on the fleet deterioration model. For instance, the one or more control devices152may take a gas turbine engine (or a plurality of gas turbine engines in the fleet of gas turbine engines) out of service, e.g., for repair, servicing, or overhaul, and/or adjust a planned overhaul workscope for the gas turbine engine (or a plurality of gas turbine engines in the fleet of gas turbine engines) based at least in part on the fleet average slopes, and more particularly, based at least in part on the fleet deterioration model.

Notably, the fleet average slopes determined at (218), and more particularly, the fleet deterioration model determined at (220) may further facilitate a discussion with owners and/or operators of the gas turbine engines within the fleet of how to change flight operations (including, e.g., routes, derate, climb path, rating, etc.) to reduce a performance deterioration of the gas turbine engines.

It will be appreciated, that utilizing a method in accordance with one or more exemplary aspects of the present disclosure to determine gas turbine engine fleet performance deterioration has the technical advantage of providing a more accurate deterioration model for a particular make and model of a gas turbine engine. More specifically, determining a fleet average slope between each adjacent interval using only the available individual engine slopes, and subsequent stitching these fleet average slopes together, has the technical advantage of providing for a more accurate overall deterioration model, which may provide for a more accurate determination of when, e.g., a particular engine within the fleet needs to be grounded or sent for repairs. Further, the method described herein may further allow for forecasting when a particular engine may need to be grounded and/or sent in for repairs based at least in part on the determined fleet average slopes and/or the determined fleet deterioration model, allowing for more efficient planning and determination of logistical issues associated therewith.