A method, system, and apparatus for life extension of a high pressure turbine blade

Abstract

The application relates to the technical field of aircraft part life analysis, and discloses a high-pressure turbine blade life extension method, system and equipment. The method comprises the following steps: acquiring specified dimension feature data of a to-be-detected engine in multiple specified dimensions, performing chart similarity analysis and data distribution difference analysis on the specified dimension feature data, obtaining current reliability data of a high-pressure turbine blade in each specified dimension, and realizing health state evaluation of the high-pressure turbine blade. Further, life extension control measures suitable for the high-pressure turbine blade are determined based on the current reliability data, so that subsequent flight schemes of a target aircraft can be guided, and the technical effect of extending the life of the high-pressure turbine blade is achieved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of life analysis of aircraft parts, in particular to a life extension method, system and equipment of a high-pressure turbine blade. BACKGROUND

[0002] A civil aviation engine is composed of a fan, a low-pressure compressor, a high-pressure compressor, a combustion chamber, a high-pressure turbine and a low-pressure turbine, wherein the high-pressure turbine blade of the high-pressure turbine is a key power source of the engine, and once the high-pressure turbine blade is damaged, the high-pressure compressor cannot work, and the engine will lose all power, causing an in-flight shutdown.

[0003] The high-pressure turbine blade is a time-life part, and has a mandatory replacement requirement in the continuous airworthiness file of an aircraft, an engine or a propeller, so as to ensure flight safety. The time interval of the time-life part is provided with a recommended time interval by a manufacturer, and a minimum time interval standard is issued by a relevant department with reference to the recommended interval of the manufacturer. In order to ensure absolute safety, most airlines usually shorten the replacement interval on this basis. Moreover, the working conditions encountered in actual flight will affect the actual service life of the high-pressure turbine blade, and therefore a life extension method of the high-pressure turbine blade is urgently needed. SUMMARY

[0004] The application provides a life extension method, system and equipment of a high-pressure turbine blade, which can perform reliability detection on the high-pressure turbine blade in a wing state, so as to guide a subsequent flight scheme or a subsequent troubleshooting scheme of the aircraft through corresponding life extension control measures to extend the service life of the high-pressure turbine blade.

[0005] In order to achieve the above-mentioned purpose, the main technical scheme adopted by the application comprises:

[0006] In a first aspect, the application provides a life extension method of a high-pressure turbine blade, which comprises:

[0007] obtaining specified dimension feature data of a to-be-detected engine in a plurality of specified dimensions; wherein the plurality of specified dimensions are feature dimensions capable of causing a specified type of damage to the high-pressure turbine blade; and each specified dimension corresponds to historical health feature data and historical unhealthy feature data;

[0008] For each specified dimension, performing chart similarity analysis according to the specified dimension feature data, the historical health feature data and the historical unhealthy feature data to obtain a current chart similarity result in the each specified dimension;

[0009] for each specified dimension, performing data distribution difference analysis according to the specified dimension feature data, the historical health feature data and the historical unhealthy feature data, to obtain current health assessment data on the each specified dimension;

[0010] generating current reliability data of the high-pressure turbine blade on the each specified dimension according to the current chart similarity result and the current health assessment data; wherein the current reliability data is used to describe whether a preset safety type crack exists on a pressure surface area of the high-pressure turbine blade;

[0011] determining a life extension control measure suitable for the high-pressure turbine blade according to the current reliability data; wherein the life extension control measure is used to guide a subsequent flight scheme and / or a subsequent troubleshooting scheme of a target aircraft, the target aircraft being an aircraft configured with the to-be-detected engine.

[0012] The life extension method of the high-pressure turbine blade provided by the embodiments of the present application can obtain specified dimension feature data of a to-be-detected engine on multiple specified dimensions, perform chart similarity analysis and data distribution difference analysis on the specified dimension feature data, obtain current reliability data of the high-pressure turbine blade on the each specified dimension, realize health state assessment of the high-pressure turbine blade, further determine a life extension control measure suitable for the high-pressure turbine blade based on the current reliability data, thereby being able to guide a subsequent flight scheme of a target aircraft, achieve the technical effect of extending the life of the high-pressure turbine blade, and not only improve the safety of the aircraft during flight, but also make the best use of the high-pressure turbine blade, and reduce the economic cost caused by replacing parts such as the high-pressure turbine blade.

[0013] In a second aspect, the embodiments of the present application provide a life extension system of a high-pressure turbine blade, and the system comprises:

[0014] a data acquisition module, configured to acquire specified dimension feature data of a to-be-detected engine on multiple specified dimensions; wherein the multiple specified dimensions are feature dimensions capable of causing specified type damage to the high-pressure turbine blade; and each specified dimension corresponds to historical health feature data and historical unhealthy feature data;

[0015] a chart analysis module, configured to, for each specified dimension, perform chart similarity analysis according to the specified dimension feature data, the historical health feature data and the historical unhealthy feature data, to obtain a current chart similarity result on the each specified dimension;

[0016] a distribution analysis module configured to perform a data distribution difference analysis according to the specified dimension feature data, the historical health feature data and the historical unhealthy feature data for each of the specified dimensions, to obtain current health assessment data on each of the specified dimensions;

[0017] a reliability analysis module configured to generate current reliability data of the high-pressure turbine blade on each of the specified dimensions according to the current chart similarity result and the current health assessment data, wherein the current reliability data is used to describe whether a preset safety type crack exists on a pressure surface area of the high-pressure turbine blade;

[0018] a measure determination module configured to determine a life extension control measure suitable for the high-pressure turbine blade according to the current reliability data, wherein the life extension control measure is used to guide a subsequent flight scheme and / or a subsequent troubleshooting scheme of a target aircraft, the target aircraft being an aircraft configured with the to-be-detected engine.

[0019] In a third aspect, an embodiment of the present application provides a computer device, including: a memory and a processor, which are in communication connection with each other, and the memory stores computer instructions, and the processor executes the computer instructions to perform the method in any of the above embodiments.

[0020] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to make a computer execute the method in any of the above embodiments.

[0021] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes computer instructions, and the computer instructions are used to make a computer execute the method in any of the above embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the description of the embodiments or the prior art will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0023] Figure 1a a schematic diagram of the high-pressure turbine blade in the embodiment of the present application;

[0024] Figure 1b a schematic diagram of the high-pressure turbine blade in the embodiment of the present application;

[0025] Figure 2 a step chart for obtaining the current chart similarity result on each specified dimension in the embodiments of the present application;

[0026] Figure 3a a target box plot on any specified dimension in the embodiments of the present application;

[0027] Figure 3b a histogram of the specified dimension feature data on any specified dimension in the embodiments of the present application;

[0028] Figure 3c a histogram of the historical health feature data on any specified dimension in the embodiments of the present application;

[0029] Figure 3d a histogram of the historical unhealthy feature data on any specified dimension in the embodiments of the present application;

[0030] Figure 3e a normal distribution chart on any specified dimension in the embodiments of the present application;

[0031] Figure 4 a step chart for obtaining the current health assessment data on each specified dimension in the embodiments of the present application;

[0032] Figure 5 a cross-sectional schematic view of a high-pressure turbine blade in the embodiments of the present application;

[0033] Figure 6 a module diagram of a life extension system of a high-pressure turbine blade provided by the embodiments of the present application;

[0034] Figure 7 a structural schematic diagram of a computer device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0035] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0036] A civil aviation engine includes a fan, a low-pressure compressor, a high-pressure compressor, a combustion chamber, a high-pressure turbine and a low-pressure turbine. Among them, after the gas is mixed and expanded by the combustion chamber, the gas is first passed through the high-pressure turbine blade, the front high-pressure compressor is driven by the high-pressure turbine blade, and then the low-pressure turbine is passed through, the low-pressure compressor and the fan are driven by the low-pressure turbine. Therefore, the high-pressure turbine blade of the high-pressure turbine is the key power source of the engine. If the high-pressure turbine blade is damaged, the high-pressure compressor will not work, and the engine will lose all power, causing an in-flight shutdown.

[0037] The civil aviation time-life part refers to a component with a mandatory replacement requirement in the continuing airworthiness document of an aircraft, an engine or a propeller. The continuing airworthiness document specifies a certain time interval for this purpose. When the component reaches the specified time interval, it needs to be replaced regardless of its actual state, in order to ensure flight safety. The time interval of the time-life part is usually provided by the manufacturer with a recommended time interval, and the Civil Aviation Bureau refers to the recommended interval of the manufacturer to issue a minimum time interval standard, that is, the maximum time that the component can be used. However, in order to ensure absolute safety, most airlines shorten the replacement interval on this basis.

[0038] In related technologies, on the one hand, in the actual flight process of the aircraft, it often encounters harsh working conditions, which leads to that the actual service life of the high-pressure turbine blade is lower than the theoretical service life. At this time, only relying on time life management cannot find the problem in time, which leads to safety risks in the flight process. On the other hand, since the high-pressure turbine blade is expensive, if the time interval is blindly shortened to ensure safety, it will cause waste of the available life of the high-pressure turbine blade, leading to considerable economic losses.

[0039] In order to timely understand the health status of the high-pressure turbine blade, in related technologies, the hole-probing method is usually used to check the conventional civil aviation engine, such as extending an industrial endoscope into the inside of the engine to be detected, and displaying the picture through the camera, and checking by a professional engineer. However, the industrial endoscope cannot detect the key part (the bottom connecting tenon) of the high-pressure turbine blade component, so the damage condition on the key part of the high-pressure turbine blade cannot be checked by the hole-probing method.

[0040] Based on the above problems, the application provides a high-pressure turbine blade life extension method, system and device, wherein the method can include: first, obtaining the specified dimension feature data of the to-be-detected engine in multiple specified dimensions, wherein the multiple specified dimensions are characteristic dimensions that can cause a specified type of damage to the high-pressure turbine blade; each specified dimension corresponds to historical health feature data and historical unhealthy feature data. Second, compare the specified dimension feature data with the historical health feature data and the historical unhealthy feature data from two angles, specifically, for each specified dimension, perform a chart similarity analysis according to the specified dimension feature data, the historical health feature data and the historical unhealthy feature data to obtain a current chart similarity result on each specified dimension; perform a data distribution difference analysis according to the specified dimension feature data, the historical health feature data and the historical unhealthy feature data to obtain current health assessment data on each specified dimension. Then, generate current reliability data of the high-pressure turbine blade in each specified dimension according to the current chart similarity result and the current health assessment data. Finally, determine a life extension control measure suitable for the high-pressure turbine blade according to the current reliability data, to guide the subsequent flight plan and / or subsequent troubleshooting plan of the target aircraft.

[0041] The high-pressure turbine blade life extension method, system and device provided by the application can perform reliability detection on the high-pressure turbine blade in the wing state, so as to guide the subsequent flight plan or subsequent troubleshooting plan of the aircraft through the corresponding life extension control measure to extend the service life of the high-pressure turbine blade.

[0042] The high-pressure turbine blade life extension method provided by the present specification can be applied to evaluate the health state of the high-pressure turbine blade in a civil aviation engine, obtain current reliability data of the high-pressure turbine blade in multiple specified dimensions, and determine a life extension control measure suitable for the high-pressure turbine blade based on the current reliability data to guide the subsequent flight plan and / or subsequent troubleshooting plan of the target aircraft, thereby extending the service life of the high-pressure turbine blade. It can be understood that the method provided by the present specification can also be used to evaluate the health state of other components in a civil aviation engine after adaptive modification, and also guide the target aircraft to extend the service life of the corresponding components. Of course, in addition to civil aviation engines, other engines used in aircraft can also be the applicable object of the method provided by the present specification.

[0043] It should be noted that in the method for prolonging the service life of the high-pressure turbine blade provided in the present application, the data basis for obtaining the specified dimension feature data can be the QAR data of the target aircraft. During the operation of the aircraft, the corresponding data can be collected through sensors and recorded in a certain format. These data are referred to as QAR (Quick Access Recorder) data. After the aircraft lands, the QAR data can be automatically uploaded to the cloud. Engineers can use the QAR data to restore a series of information such as flight status, engine operating conditions, and states. The QAR data can also be used as a basis for fault diagnosis. The QAR data can have different recording frequencies. The recording frequency increases with the importance of the sensor recording the parameter, and is usually recorded once every 1 second.

[0044] According to the embodiments of the present application, a method for prolonging the service life of a high-pressure turbine blade is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0045] In the present embodiment, a method for prolonging the service life of a high-pressure turbine blade is provided, which can be used for the above-mentioned civil aviation engine and the like. Referring to Figure 1a , Figure 1a The steps of the method for prolonging the service life of a high-pressure turbine blade provided in the present application are shown in the figure. The method comprises the following steps:

[0046] S100. Obtain specified dimension feature data of a to-be-detected engine in a plurality of specified dimensions; wherein the plurality of specified dimensions are characteristic dimensions capable of causing a specified type of damage to the high-pressure turbine blade; and each specified dimension corresponds to historical health feature data and historical unhealthy feature data.

[0047] S200. For each specified dimension, perform chart similarity analysis according to the specified dimension feature data, the historical health feature data, and the historical unhealthy feature data to obtain a current chart similarity result in each specified dimension.

[0048] S300. For each specified dimension, perform data distribution difference analysis according to the specified dimension feature data, the historical health feature data, and the historical unhealthy feature data to obtain current health evaluation data in each specified dimension.

[0049] S400. Generate current reliability data of the high-pressure turbine blade in each specified dimension according to the current chart similarity result and the current health evaluation data; wherein the current reliability data is used to describe whether a preset safety type crack exists on the pressure surface area of the high-pressure turbine blade.

[0050] S500. determining a life extension control measure suitable for the high-pressure turbine blade according to the current reliability data; wherein the life extension control measure is used to guide a subsequent flight plan and / or a subsequent troubleshooting plan of the target aircraft, the target aircraft being an aircraft configured with the to-be-inspected engine.

[0051] Specifically, the data basis used to obtain the specified dimension feature data can be the QAR data of the target aircraft, which includes flight-related parameters of the target aircraft and engine performance-related parameters of the to-be-inspected engine in the target aircraft, etc. The specified dimension feature data of the to-be-inspected engine is obtained from the QAR data according to multiple specified dimensions. The multiple specified dimensions are characteristic dimensions that can cause a specified type of damage to the high-pressure turbine blade. The specified type of damage can be understood as damage to the high-pressure turbine blade when the target aircraft operates in an improper parameter range on the specified dimension.

[0052] Exemplarily, how to select the specified dimension is explained. If there is a crack at the root of the high-pressure turbine blade, it will cause crack growth when subjected to abnormal vibration, and the growth rate of the crack at the root of the high-pressure turbine blade is positively correlated with the vibration energy, that is, the higher the vibration energy, the longer the accumulation time, and the faster the growth rate of the crack at the root of the high-pressure turbine blade. The crack at the root of the high-pressure turbine blade has a great influence on the safety of the target aircraft during flight, so the vibration condition of the engine can be selected as a specified dimension. It can be understood that the specified type of damage can be the growth of the crack at the root of the high-pressure turbine blade.

[0053] Exemplarily, how to select the specified dimension is explained. For example, the engine idle running time will affect the cooling of the high-pressure turbine blade. If the cooling is insufficient, the high-pressure turbine blade and the turbine disc may expand and deform accordingly. Therefore, the engine idle running time can be selected as a specified dimension. It can be understood that the specified type of damage can be the expansion and deformation of the high-pressure turbine blade.

[0054] By obtaining the specified dimension feature data of the to-be-inspected engine on multiple specified dimensions, a data basis is provided for subsequent evaluation of the health status of the high-pressure turbine blade, thereby improving the safety of the target aircraft during flight.

[0055] Further, each specified dimension corresponds to historical health feature data and historical unhealthy feature data, and the historical health feature data and the historical unhealthy feature data are used as reference data of the specified dimension feature data. The historical health feature data is the feature data of the engine in a healthy state on each specified dimension, and its data basis can be the QAR data of the aircraft during historical flight. Correspondingly, the historical unhealthy feature data is the feature data of the engine in an unhealthy state on each specified dimension, and its data basis can also be the QAR data of the aircraft during historical flight.

[0056] Specifically, in the chart similarity analysis, the specified dimension feature data, the historical health feature data and the historical unhealthy feature data on any specified dimension are represented by a specific type of chart to reflect the statistical characteristics of the three kinds of data respectively. Exemplarily, the specific type of chart includes one or more of chart types such as scatter plot, density plot, quantile plot or violin plot, and the type of chart can be determined according to the actual use scenario.

[0057] Based on the above data, by performing chart similarity analysis between the specified dimension feature data and the historical health feature data, the similarity result of the to-be-detected engine and the engine in a healthy state on the any specified dimension can be obtained. Correspondingly, by performing chart similarity analysis between the specified dimension feature data and the historical unhealthy feature data, the similarity result of the to-be-detected engine and the engine in an unhealthy state on the any specified dimension can be obtained. The current chart similarity result on the any specified dimension is determined based on the above two similarity results.

[0058] Further, in the data distribution difference analysis, the specified dimension feature data, the historical health feature data and the historical unhealthy feature data on any specified dimension all have certain data distribution characteristics, and the data distribution characteristics can be represented by a plurality of statistical quantities in the above data. Exemplarily, the statistical quantities include one or more of types such as median, variance, skewness and correlation coefficient, and the type of statistical quantity can be determined according to the actual use scenario. It can be understood that the data distribution difference analysis is respectively performed between the specified dimension feature data, the historical health feature data and the historical unhealthy feature data on any specified dimension, to obtain the difference between the to-be-detected engine and the engine in an unhealthy state, and the difference between the to-be-detected engine and the engine in a healthy state, thereby generating the current health assessment data on the any specified dimension.

[0059] Further, according to the current chart similarity result and the current health assessment data, the current reliability data of the high-pressure turbine blade in the to-be-detected engine on each specified dimension can be obtained. For example, the current chart similarity result and the current health assessment data are input into a pre-trained classification model for classification to obtain the current reliability data of the high-pressure turbine blade on each specified dimension. The classification model can adopt one or more of types such as logistic regression, support vector machine (SVM), decision tree, random forest and neural network. Exemplarily, the current reliability data of the high-pressure turbine blade is explained. The current reliability data is used to describe whether there is a preset safety type crack on the pressure surface area of the high-pressure turbine blade. Please refer to Figure 1b As the to-be-detected engine is used, Figure 1bSome cracks can occur in the pressure surface region 100. The cracks in the pressure surface region 100 can have different lengths. The pre-set safe type cracks can be cracks that exist in the pressure surface region but have no temporary impact on the health of the engine. For example, the pre-set safe type cracks have a small length, and the pre-set safe type cracks in the pressure surface region need a certain time to grow into long cracks that have a certain impact on the health of the engine.

[0060] Exemplarily, the reliability data for describing whether the pre-set safe type cracks exist on the pressure surface region of the high-pressure turbine blade is illustrated. The reliability data can include a 0-level health state, a 1-level health state, and a 2-level health state. The 0-level health state: the high-pressure turbine blade has no cracks in the pressure surface region; the 1-level health state: the high-pressure turbine blade has micro cracks in the pressure surface region, and the length of the cracks is less than 2 mm; and the 2-level health state: the high-pressure turbine blade has cracks in the pressure surface region, and the length of the cracks is greater than 2 mm, but the cracks do not extend to the end surface.

[0061] It should be noted that the health state in the embodiment can include the 0-level health state, the 1-level health state, and the 2-level health state. The unhealthy state in the embodiment can include any of the following situations:

[0062] 1) The high-pressure turbine blade has cracks extending to the end surface along the small neck, and the length of the cracks extending to the inside of the end surface is less than 1 mm

[0063] 2) The high-pressure turbine blade has cracks extending to the end surface along the small neck, and the cracks extend to the inside of the end surface more than 1 mm, but the cracks do not extend to the third surface (such as the germination surface, the end surface, the bottom surface, or another side surface)

[0064] 3) The high-pressure turbine blade has cracks extending to the end surface along the small neck, and the cracks extend to the third surface (such as the germination surface, the end surface, the bottom surface, or another side surface)

[0065] 4) The high-pressure turbine blade is broken into two parts along the small neck, but the remaining material can still be limited by the turbine disc and will not be detached

[0066] 5) The high-pressure turbine blade is broken into at least two parts along the small neck, and the turbine disc cannot limit the part above the small neck to be separated from the turbine disc, and the broken part can completely separate from the engine (i.e., fly out)

[0067] Further, since the current reliability data can describe whether the preset safety type crack exists on the pressure surface area of the high-pressure turbine blade, a reasonable life extension control measure for the high-pressure turbine blade is output according to the current reliability data of the high-pressure turbine blade in any specified dimension. For example, if the current reliability data indicates that the preset safety type crack does not exist on the pressure surface area, the target aircraft can continue to use the current flight scheme, and the flight scheme of the target aircraft does not need to be adjusted. If the current reliability data indicates that the preset safety type crack exists on the pressure surface area, the target aircraft cannot continue to use the current flight scheme, and the flight scheme of the target aircraft needs to be adjusted to reduce the probability of the preset safety type crack continuing to grow or maintain the status of the preset safety type crack.

[0068] The life extension method of the high-pressure turbine blade provided in the embodiment can obtain the specified dimension feature data of the to-be-detected engine in multiple specified dimensions, perform chart similarity analysis and data distribution difference analysis on the specified dimension feature data, obtain the current reliability data of the high-pressure turbine blade in each specified dimension, and realize health state evaluation of the high-pressure turbine blade. Further, the life extension control measure suitable for the high-pressure turbine blade is determined based on the current reliability data, so that the subsequent flight scheme of the target aircraft can be guided, the technical effect of extending the life of the high-pressure turbine blade is achieved, and not only the safety of the aircraft during flight can be improved, but also the high-pressure turbine blade can be used to the full, and the economic cost generated by replacing parts such as the high-pressure turbine blade can be reduced.

[0069] As an embodiment of the present application, the current reliability data of the high-pressure turbine blade in each specified dimension is generated according to the current chart similarity result and the current health evaluation data, and includes: searching in the reliability relationship data corresponding to each specified dimension according to the current chart similarity result and the current health evaluation data to obtain the current reliability data. The reliability relationship data can be used to describe the corresponding relationship between the chart similarity result, the health evaluation data, and the reliability data of the high-pressure turbine blade in each specified dimension.

[0070] In the embodiment, the reliability relationship data can be obtained based on historical data, and is used to describe the corresponding relationship between the chart similarity result, the health evaluation data, and the reliability data of the high-pressure turbine blade in each specified dimension. It should be noted that the chart similarity result can include analysis results between multiple types of charts. Correspondingly, the health evaluation data can be analysis results between multiple types of data.

[0071] Specifically, the reliability relationship data is established in advance for each specified dimension. In any specified dimension, if the current chart similarity result and the current health assessment data are determined, the corresponding current reliability data is obtained by searching the current chart similarity result and the current health assessment data in the reliability relationship data corresponding to the any specified dimension, to determine whether the preset safety type crack exists on the pressure surface area of the high-pressure turbine blade.

[0072] With reference to Figure 2 , Figure 2 For the step of obtaining the current chart similarity result on each specified dimension in the embodiments of the present application, as shown in the figure, as an embodiment of the present application, the chart similarity analysis is performed according to the specified dimension characteristic data, the historical health characteristic data and the historical unhealthy characteristic data, to obtain the current chart similarity result on each specified dimension, including:

[0073] S210. In response to the box plot drawing operation, the specified dimension characteristic data, the historical health characteristic data and the historical unhealthy characteristic data are displayed in the target box plot, and the box plot similarity comparison result is generated.

[0074] S220. In response to the histogram drawing operation, the specified dimension characteristic data, the historical health characteristic data and the historical unhealthy characteristic data are respectively displayed in the respective histograms, and the histogram similarity comparison result is generated.

[0075] S230. In response to the normal distribution drawing operation, the specified dimension characteristic data, the historical health characteristic data and the historical unhealthy characteristic data are displayed in the normal distribution graph, and the normal distribution similarity comparison result is generated.

[0076] S240. The current chart similarity result is obtained by summarizing the box plot similarity comparison result, the histogram similarity comparison result and the normal distribution similarity comparison result.

[0077] Specifically, the chart type used for the chart similarity analysis can be a box plot, a histogram and a normal distribution graph. The box plot is a statistical chart used for displaying the dispersion condition of a group of data, mainly used for reflecting the characteristics of the distribution of the original data, and can also be used for comparing the distribution characteristics of multiple groups of data. The histogram is a statistical report chart, which represents the data distribution condition by a series of longitudinal stripes or line segments with different heights. The normal distribution graph is a probability distribution graph drawn according to the normally distributed data.

[0078] Further, the box plot mainly reflects the similarity of the data in the aspects of the central position, the distribution range, the variability, the symmetry and the overall distribution form. With reference to Figure 3a , Figure 3aFor the box plot of the target in any specified dimension in the embodiment of the present application, as shown in the figure, wherein self represents the distribution data of the specified dimension feature data, n represents the distribution data of the historical healthy feature data, and abn represents the distribution data of the historical unhealthy feature data. From Figure 3a It can be determined that the box plot similarity comparison result is that the similarity of the specified dimension feature data and the historical healthy feature data is higher, and the similarity of the specified dimension feature data and the historical unhealthy feature data is lower in the corresponding specified dimension.

[0079] Further, the histogram mainly reflects the similarity of the data in terms of distribution shape, central tendency, dispersion degree, multi-peak and overall frequency. Referring to Figure 3b to Figure 3d , Figure 3b For the histogram of the specified dimension feature data in any specified dimension in the embodiment of the present application, Figure 3c For the histogram of the historical healthy feature data in any specified dimension in the embodiment of the present application, Figure 3d For the histogram of the historical unhealthy feature data in any specified dimension in the embodiment of the present application, as shown in the figure, in the any specified dimension, the peak values of the above data appear at the same position, and the peak values of different data are different, in addition, the values of the above data at different positions in the histogram are also different. By comparison, the histogram similarity comparison result can be obtained that the similarity of the specified dimension feature data and the historical healthy feature data is higher, and the similarity of the specified dimension feature data and the historical unhealthy feature data is lower in the corresponding specified dimension.

[0080] Further, the normal distribution graph mainly reflects the similarity of the data in terms of mean, standard deviation, shape, tail feature, overall shape and overlap degree. Referring to Figure 3e , Figure 3e For the normal distribution graph in any specified dimension in the embodiment of the present application, as shown in the figure, the blue curve represents the specified dimension feature data, the green curve represents the historical healthy feature data, and the red curve represents the historical unhealthy feature data. It can be seen that the blue curve is closer to the green curve, and the blue curve is farther away from the red curve, so the normal distribution similarity comparison result can be obtained that the similarity of the specified dimension feature data and the historical healthy feature data is higher, and the similarity of the specified dimension feature data and the historical unhealthy feature data is lower in the corresponding specified dimension.

[0081] By using multiple chart types for chart similarity analysis, not only can the similarity between the specified dimension feature data, the historical healthy feature data and the historical unhealthy feature data be evaluated and analyzed from multiple aspects, thereby improving the comprehensiveness of the current chart similarity result, but also can effectively reduce the interference of the contingency of the specified dimension feature data, thereby improving the accuracy of the current chart similarity result.

[0082] In some embodiments, when drawing the histogram, for the historical health feature data and the historical unhealthy feature data, the above data is averaged when drawing, and the histogram is drawn based on the average data respectively to reduce the complexity of the histogram and facilitate the chart similarity analysis. In other embodiments, the box plot and the normal distribution plot of different data can also be drawn in different charts, and the histogram of different data can also be drawn in the same chart.

[0083] Referring to Figure 4 , Figure 4 The step chart for obtaining the current health assessment data on each specified dimension in the embodiments of the present application is shown in the figure. As an embodiment of the present application, the data distribution difference analysis is performed according to the specified dimension feature data, the historical health feature data and the historical unhealthy feature data to obtain the current health assessment data on each specified dimension, which includes:

[0084] S310. Determine the health data distribution feature of the historical health feature data, the unhealthy data distribution feature of the historical unhealthy feature data and the current data distribution feature of the specified dimension feature data.

[0085] S320. Determine the current health assessment data based on the first difference data between the health data distribution feature and the current data distribution feature, and the second difference data between the unhealthy data distribution feature and the current data distribution feature.

[0086] Specifically, the types of the health data distribution feature, the unhealthy data distribution feature and the current data distribution feature can be at least one of the data distribution features such as the median, the mode, the variance, the quantile and the range. According to the comparison between the health data distribution feature and the current data distribution feature, the first difference data is obtained, and according to the comparison between the unhealthy data distribution feature and the current data distribution feature, the second difference data is obtained. At this time, the current health assessment data can be determined according to the first difference data and the second difference data. Exemplarily, the first difference data and the second difference data can be input into the health assessment model to obtain the current health assessment data.

[0087] In some embodiments, the data distribution feature can be the mean and the standard deviation of the above data. The quantitative comparison between the health data distribution feature and the current data distribution feature can obtain the first difference data, which is in the form as follows:

[0088] Δμ HS,i = | μ S,i - μ H,i |

[0089] Δσ HS,i = | σ S,i - σ H,i |

[0090] where Δμ HS,i represents the mean difference between the historical health feature data and the specified dimension feature data, Δσ HS,i represents the standard deviation difference between the historical health feature data and the specified dimension feature data; μ S,i is the mean of the specified dimension feature data; σ S,i is the standard deviation of the specified dimension feature data; μ H,i is the mean of the historical health feature data; σ H,i is the standard deviation of the historical health feature data.

[0091] Correspondingly, the second difference data has the following form:

[0092] Δμ US,i = |μ S,i - μ U,i |

[0093] Δσ US,i = |σ S,i - σ U,i |

[0094] where Δμ US,i represents the mean difference between the historical unhealthy feature data and the specified dimension feature data, Δσ US,i represents the standard deviation difference between the historical unhealthy feature data and the specified dimension feature data; μ S,i is the mean of the specified dimension feature data; σ S,i is the standard deviation of the specified dimension feature data; μ U,i is the mean of the historical unhealthy feature data; σ U,i is the standard deviation of the historical unhealthy feature data.

[0095] Further, the current health assessment data is determined based on the first difference data and the second difference data, and the current health assessment data includes a health score and an unhealthy score of the specified dimension feature data, and has the following form:

[0096]

[0097] where P H represents the health score, P U represents the unhealthy score; α i is the correction factor of the i-th specified dimension.

[0098] As an embodiment of the present application, the plurality of specified dimensions include a rotation speed dimension, a vibration dimension, a time of flight dimension, an idling time dimension, and an aircraft climb related dimension. The rotation speed dimension includes a rotation speed accumulation dimension and a maximum rotation speed dimension. The vibration dimension includes a vibration accumulation dimension and a maximum vibration accumulation dimension. The aircraft climb related dimension includes an aircraft climb time dimension and an aircraft climb rate dimension.

[0099] In some cases, different characteristic dimensions are found to have different degrees of influence on the life of the engine to be detected, and in order to prolong the life of the engine to be detected, it is necessary to determine the characteristic dimension that can cause a specified type of damage to the high-pressure turbine blade in the characteristic dimension of the QAR data, i.e., the specified dimension. Specifically, the specified dimension includes a rotation speed dimension, a vibration dimension, a time of flight dimension, an idling time dimension, and an aircraft climb related dimension, and the cumulative value on each specified dimension can be obtained from the QAR data of the target aircraft.

[0100] In the present embodiment, from the physical mechanism analysis, the faster the engine rotation speed, the greater the rotational stress generated, and the greater the impact on the life of the high-pressure turbine blade. From the operation and maintenance experience analysis, the rotation speed from 90% will generate greater rotational stress on the high-pressure turbine blade, and long-term accumulation will cause the life of the high-pressure turbine blade to decrease. Based on this, the rotation speed dimension represents the influence of the rotation speed of the engine to be detected on its life. The rotation speed dimension includes a rotation speed accumulation dimension and a maximum rotation speed dimension. The cumulative value on the rotation speed accumulation dimension is obtained by counting the time when the rotation speed of the high-pressure rotor of the engine of the target aircraft exceeds 90% and is less than 115% during the climb phase. When counting, 1% is taken as the minimum interval, and 1 second is taken as the minimum cumulative value, and the corresponding cumulative value on the rotation speed accumulation dimension is obtained to determine the characteristic data on the rotation speed accumulation dimension. The cumulative value on the maximum rotation speed dimension is obtained by counting the maximum value of the rotation speed of the high-pressure rotor of the engine of the target aircraft during the climb phase. When counting, each flight process of the target aircraft is taken as a cycle, and the maximum rotation speed of the high-pressure rotor of the engine in the cycle is counted to determine the characteristic data on the maximum rotation speed dimension.

[0101] In this embodiment, theoretical analysis and experimental data show that high vibration can cause the engine rotor to be under abnormal stress, and the vibration and the blade root crack are positively correlated, that is, the higher the vibration, the longer the accumulation time, and the faster the crack growth rate. Based on this, the vibration dimension represents the influence of abnormal vibration on the life of the engine to be detected. The vibration dimension includes the vibration accumulation dimension and the maximum vibration accumulation dimension. The cumulative value on the vibration accumulation dimension is obtained by statistically analyzing the vibration duration of the engine to be detected during the flight of the target aircraft. When counting, 0.1 is taken as the minimum interval, and 1s is taken as the minimum cumulative value. The corresponding cumulative value is obtained on the vibration accumulation dimension to determine the characteristic data on the vibration accumulation dimension. The cumulative value on the maximum vibration accumulation dimension is obtained by statistically analyzing the maximum abnormal vibration of the engine to be detected during the flight of the target aircraft. When counting, each flight process of the target aircraft is taken as a cycle, and the maximum abnormal vibration of the engine to be detected in the cycle is counted to determine the characteristic data on the maximum vibration accumulation dimension.

[0102] In this embodiment, analysis and research show that the flight time of the aircraft in the air has a direct correlation with the life of the engine. Based on this, the flight time dimension represents the influence of the flight process duration of the target aircraft on the life of the engine to be detected. The cumulative value on the specified dimension is obtained by statistically analyzing the flight process duration of the target aircraft. When counting, 30 minutes is taken as the minimum flight time, 1 minute is taken as the minimum interval, and 1 second is taken as the minimum cumulative value. The corresponding cumulative value is obtained on the flight time dimension to determine the characteristic data on the flight time dimension.

[0103] In this embodiment, analysis and research show that after the aircraft lands, the time for the engine to slide at idle power will directly determine the cooling condition of the engine and has a direct correlation with the life of the high-pressure turbine blade of the engine. Based on this, the idle running time dimension represents the influence of the idle sliding time of the target aircraft on the life of the engine to be detected. The cumulative value on the specified dimension is obtained by statistically analyzing the time for the engine to slide at idle power after the target aircraft lands. When counting, 0 minutes is taken as the minimum interval, and 1 second is taken as the minimum cumulative value to obtain the corresponding cumulative value to determine the characteristic data on the idle running time dimension.

[0104] In this embodiment, after analyzing and researching, it is found that the rising time and rising speed during the process of the aircraft rising from the take-off to the cruising altitude are directly related to the service life of the high-pressure turbine blade. Based on this, the aircraft climbing related dimensions represent the influence of the action of the target aircraft in the climbing process on the service life of the to-be-detected engine. The aircraft climbing related dimensions include an aircraft climbing time dimension and an aircraft climbing rate dimension. The aircraft climbing time dimension represents the influence of the climbing time of the target aircraft on the service life of the to-be-detected engine, and the cumulative value on the aircraft climbing time dimension is obtained by counting the time from the start of the climbing of the target aircraft to the cruising altitude. When counting, 10 minutes is taken as the shortest climbing time, 1 minute is taken as the lowest interval, and 1 second is taken as the minimum cumulative value to obtain the corresponding cumulative value, so as to determine the feature data on the aircraft climbing time dimension. The aircraft climbing rate dimension represents the influence of the climbing time speed of the target aircraft on the service life of the to-be-detected engine, and the cumulative value on the aircraft climbing rate dimension is obtained according to the climbing time and the cruising altitude of the target aircraft. When counting, 5 feet / second is taken as the lowest interval, and the climbing speed of the target aircraft in each flight process is counted to obtain the corresponding cumulative value, so as to determine the feature data on the aircraft climbing rate dimension.

[0105] As an embodiment of the present application, the preset safety type crack is a crack with a length less than a preset safety threshold or a crack with a length greater than or equal to the preset safety threshold but not extending to the end surface; wherein the preset safety threshold is less than or equal to 2 millimeters.

[0106] Specifically, referring to Figure 5 , Figure 5 is a cross-sectional view of the high-pressure turbine blade in the embodiment of the present application, as shown in the figure, Figure 5 The left side is the three-dimensional appearance of the high-pressure turbine blade, and the right side is an enlarged view of the pressure surface area 100 of the high-pressure turbine blade, wherein the protrusions in the pressure surface area are used to connect with the clamping grooves in the engine, so as to fix the high-pressure turbine blade and avoid the high-pressure turbine blade from being pulled out of the engine. It can be understood that the upper surface and the lower surface of the protrusion, i.e. the stress surface of the high-pressure turbine blade, when the stress surface changes, may cause cracks in the large neck 101 and the small neck 102.

[0107] Further, by setting a preset safety type for the case of cracks appearing on the large neck 101 and the small neck 102, it is determined whether the high-pressure turbine blade has a safety risk. It can be understood that when there is no crack on the large neck 101 and the small neck 102, the high-pressure turbine blade has no safety risk. If a small crack appears on the large neck 101 or the small neck 102, but the crack length indicated by the fluorescent penetration detection is less than a preset safety threshold, it indicates that the high-pressure turbine blade is in a crack initiation period, at which time the safety risk of the high-pressure turbine blade is small, and it can be considered that the high-pressure turbine blade is in a healthy state. If a crack appears on the large neck 101 or the small neck 102, and the crack length indicated by the fluorescent penetration detection is greater than or equal to the preset safety threshold, but the crack has not extended to the end surface of the pressure surface area; at this time, the crack on the high-pressure turbine blade has grown to a certain extent, and the safety risk does not exceed the set threshold, and it can be considered that the high-pressure turbine blade is in a healthy state. It can be understood that if any non-preset safety type crack appears on the high-pressure turbine blade, the high-pressure turbine blade is in an unhealthy state at this time.

[0108] As an embodiment of the present application, the specified dimension is determined by at least one of the following ways:

[0109] According to the fitting of the damage data of the high-pressure turbine blade and the engine high-pressure rotor speed, nonlinear relationship data between the engine high-pressure rotor speed and the damage data is obtained; wherein the nonlinear relationship data is used to describe the case that the damage data of the high-pressure turbine blade increases nonlinearly with the increase of the engine high-pressure rotor speed, and indicates that the speed dimension is taken as a specified dimension.

[0110] According to the positive correlation between the engine vibration condition and the root crack growth rate of the high-pressure turbine blade, the vibration dimension is taken as a specified dimension.

[0111] According to the cooling relationship between the engine exhaust temperature and the idling running time, the idling running time dimension is taken as a specified dimension.

[0112] According to the relationship between the thermal expansion deformation condition of the high-pressure turbine blade and the stress concentration position, the aircraft climbing related dimension is taken as a specified dimension.

[0113] Specifically, the nonlinear relationship data between the engine high-pressure rotor speed and the damage data can be expressed as:

[0114] D=a1x 2 -b1x+c1

[0115] Wherein, D represents the damage degree data, x represents the engine high-pressure rotor speed; a1, b1 and c1 are coefficients. According to the nonlinear relationship data, when the engine high-pressure rotor speed increases, the damage degree will increase nonlinearly, and the higher the speed, the greater the damage degree. Among them, when the engine high-pressure rotor speed is greater than 90%, it will cause certain accumulated damage to the high-pressure turbine blade, so the range of the engine high-pressure rotor speed is set to be between 90% and 115%, the accumulated speed and the maximum speed of the flight are counted, and the accumulated damage is calculated by using the above nonlinear relationship data.

[0116] In the embodiment, the positive correlation between the engine vibration condition and the root crack growth rate of the high-pressure turbine blade can be understood as that the root crack growth rate of the high-pressure turbine blade is positively correlated with the engine vibration condition, and the greater the vibration amplitude or vibration frequency of the engine, the faster the root crack growth rate.

[0117] In the embodiment, the cooling relationship between the engine exhaust temperature and the idling running time can be expressed as:

[0118]

[0119] Wherein, T(t) represents the engine exhaust temperature, which refers to the temperature of the gas discharged from the combustion chamber and passing through the high-speed rotating turbine at the engine turbine outlet section; t represents the idling running time; T max is the highest temperature; T env is the ambient temperature; a2, b2 and c2 are coefficients. According to the cooling relationship, when the engine idling running time is sufficient, sufficient cooling can be obtained. Therefore, in the next flight of the target aircraft, by prolonging the engine idling running time, the full load time can be prolonged, that is, the time when the stress concentration position changes from the large neck 101 to the small neck 102, so as to increase the time of the large neck 101 under stress and reduce the probability of cracks in the small neck 102, thereby prolonging the service life of the high-pressure turbine blade.

[0120] In the embodiment, the relationship between the thermal expansion deformation of the high-pressure turbine blade and the stress concentration position can be understood as that, from the beginning of full-speed running of the engine, the temperature of the high-pressure turbine blade and the turbine disc begins to rise, and the high-pressure turbine blade and the turbine disc will expand and deform due to heat; when the temperature of the high-pressure turbine reaches a set threshold T1, the stress point of the high-pressure turbine blade will change, and the main stress point changes from the large neck 101 to the small neck 102, resulting in a reduction in the service life of the high-pressure turbine blade.

[0121] As an embodiment of the present application, the life extension control measures include at least one of limiting the maximum load of the aircraft, forcibly setting the minimum idling time, adjusting the flight task of the target aircraft, and executing the transmitter troubleshooting.

[0122] Specifically, since the current reliability data of the high-pressure turbine blade in each specified dimension is known, the influence of the operating parameter in each specified dimension on the high-pressure turbine blade is known. For example, if the reliability of the high-pressure turbine blade is affected in the rotation speed accumulation dimension, the maximum load of the target aircraft can be limited according to the accumulated value in the rotation speed accumulation dimension, so as to achieve the target of reducing the use power of the engine and prolong the life of the high-pressure turbine blade in the to-be-detected engine.

[0123] If the reliability of the high-pressure turbine blade is affected in the maximum rotation speed dimension, the maximum load of the target aircraft can be limited according to the accumulated value in the maximum rotation speed dimension, so as to achieve the target of limiting the maximum use power of the engine and prolong the life of the high-pressure turbine blade in the to-be-detected engine.

[0124] If the reliability of the high-pressure turbine blade is affected in the vibration accumulation dimension or the maximum vibration accumulation dimension, relevant troubleshooting work needs to be performed on the target aircraft, such as checking the vibration source in the target aircraft, and the maximum load of the target aircraft can also be limited according to the accumulated value.

[0125] If the reliability of the high-pressure turbine blade is affected in the idling time dimension, the minimum idling time of the target aircraft can be forcibly set according to the whole aircraft fleet, so as to prolong the life of the high-pressure turbine blade in the to-be-detected engine.

[0126] If the reliability of the high-pressure turbine blade is affected in the flight time dimension, the flight plan can be dynamically adjusted, in which the same aircraft is avoided to continuously perform a short-range task in the flight plan arrangement; and the aircraft that has performed a plurality of short-range flight tasks is avoided to continue to perform a short-range task as much as possible.

[0127] If the reliability of the high-pressure turbine blade is affected in the aircraft climbing time dimension or the aircraft climbing rate dimension, the accumulated value of the to-be-detected engine in the idling time dimension can also be abnormal, and the target aircraft can be arranged to perform a long-climbing-time route while avoiding to perform a short-range task.

[0128] Correspondingly, please refer to Figure 6 The embodiment of the present application provides a life prolonging system of a high-pressure turbine blade, which comprises:

[0129] The data acquisition module 201 is configured to acquire specified dimension characteristic data of a to-be-detected engine in a plurality of specified dimensions; wherein the plurality of specified dimensions are characteristic dimensions capable of causing a specified type of damage to the high-pressure turbine blade; and each specified dimension corresponds to historical health characteristic data and historical unhealthy characteristic data.

[0130] The chart analysis module 202 is configured to perform chart similarity analysis on the specified dimension feature data, the historical healthy feature data and the historical unhealthy feature data for each specified dimension, to obtain a current chart similarity result on each specified dimension.

[0131] The distribution analysis module 203 is configured to perform data distribution difference analysis on the specified dimension feature data, the historical healthy feature data and the historical unhealthy feature data for each specified dimension, to obtain a current health assessment data on each specified dimension.

[0132] The reliability analysis module 204 is configured to generate current reliability data of the high-pressure turbine blade on each specified dimension according to the current chart similarity result and the current health assessment data, wherein the current reliability data is used to describe whether a preset safety type crack exists on the pressure surface area of the high-pressure turbine blade.

[0133] The measure determination module 205 is configured to determine a life extension control measure suitable for the high-pressure turbine blade according to the current reliability data, wherein the life extension control measure is used to guide a subsequent flight plan and / or a subsequent troubleshooting plan of a target aircraft, the target aircraft being an aircraft configured with the to-be-detected engine.

[0134] In some optional embodiments, the reliability analysis module 204 includes:

[0135] The current reliability data calculation unit is configured to search in reliability relationship data corresponding to each specified dimension according to the current chart similarity result and the current health assessment data, to obtain the current reliability data, wherein the reliability relationship data is used to describe a corresponding relationship between the chart similarity result, the health assessment data and the reliability data of the high-pressure turbine blade on each specified dimension.

[0136] In some optional embodiments, the chart analysis module 202 includes:

[0137] The box plot comparison unit is configured to, in response to a box plot drawing operation, display the specified dimension feature data, the historical healthy feature data and the historical unhealthy feature data in a target box plot, and generate a box plot similarity comparison result.

[0138] The histogram comparison unit is configured to, in response to a histogram drawing operation, display the specified dimension feature data, the historical healthy feature data and the historical unhealthy feature data in respective histograms, and generate a histogram similarity comparison result.

[0139] The normal distribution plot comparison unit is configured to, in response to a normal distribution plot drawing operation, display the specified dimension feature data, the historical healthy feature data and the historical unhealthy feature data in a normal distribution plot, and generate a normal distribution similarity comparison result.

[0140] The results summary unit is used to summarize the similarity comparison results of the box plot, histogram, and normal distribution to obtain the current chart similarity results.

[0141] In some alternative implementations, the distribution analysis module 203 includes:

[0142] The current data distribution characteristic determination unit is used to determine the health data distribution characteristics of historical health characteristic data, the unhealthy data distribution characteristics of historical unhealthy characteristic data, and the current data distribution characteristics of characteristic data of a specified dimension.

[0143] The difference assessment unit is used to determine the current health assessment data based on the first difference data between the health data distribution characteristics and the current data distribution characteristics, and the second difference data between the unhealthy data distribution characteristics and the current data distribution characteristics.

[0144] In some optional implementations, the data acquisition module 201 includes multiple specified dimensions such as rotational speed dimension, vibration dimension, flight time dimension, idling time dimension, and aircraft climb-related dimension.

[0145] The speed dimension includes the speed accumulation dimension and the maximum speed dimension.

[0146] Vibration dimensions include vibration accumulation dimension and maximum vibration accumulation dimension.

[0147] The dimensions related to aircraft climb include the aircraft climb time dimension and the aircraft climb rate dimension.

[0148] In some optional implementations, the preset safety type crack in the reliability analysis module 204 is a crack with a length less than a preset safety threshold or a crack with a length greater than or equal to the preset safety threshold but not extending to the end face; wherein, the preset safety threshold is less than or equal to 2 mm.

[0149] In some optional implementations, the data acquisition module 201 includes:

[0150] The speed damage assessment unit is used to fit the damage data of the high-pressure turbine blades and the speed of the engine high-pressure rotor to obtain nonlinear relationship data between the engine high-pressure rotor speed and the damage data. The nonlinear relationship data is used to describe the nonlinear increase of the damage data of the high-pressure turbine blades with the increase of the engine high-pressure rotor speed, and to indicate that the speed dimension is a specified dimension.

[0151] The vibration damage assessment unit is used to designate the vibration dimension as a specified dimension based on the positive correlation between engine vibration and the root crack growth rate of high-pressure turbine blades.

[0152] The idling damage assessment unit is used to define the idling time dimension as a specified dimension based on the cooling relationship between engine exhaust temperature and idling time.

[0153] The climb damage assessment unit is used to define the aircraft climb-related dimensions as a specified dimension based on the relationship between the thermal expansion deformation of the high-pressure turbine blades and the location of stress concentration.

[0154] In some alternative implementations, the life extension control measures in the measures determination module 205 include at least one of limiting the maximum load of the aircraft, forcibly setting a minimum idling time, adjusting the flight mission of the target aircraft, and performing transmitter troubleshooting.

[0155] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0156] In this embodiment, the high-pressure turbine blade life extension system is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0157] Please see Figure 7 , Figure 7 This is a schematic diagram of a computer device according to an embodiment of this application. As shown in the figure, the computer device includes one or more processors 10, a memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components communicate with each other using different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take a processor 10 as an example.

[0158] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0159] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0160] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0161] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0162] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0163] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0164] This application provides a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method of any embodiment of this application.

[0165] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and all such modifications and variations fall within the scope defined by the appended claims.

[0166] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0167] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0168] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0169] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0170] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0171] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0172] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0173] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0174] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0175] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for extending the lifespan of high-pressure turbine blades, characterized in that, The method includes: Acquire characteristic data of the engine under test across multiple specified dimensions; wherein, the multiple specified dimensions are characteristic dimensions capable of causing specified types of damage to the high-pressure turbine blades; and each specified dimension corresponds to historical healthy characteristic data and historical unhealthy characteristic data; the multiple specified dimensions include speed dimension, vibration dimension, flight time dimension, idling time dimension, and aircraft climb-related dimension; the speed dimension includes speed accumulation dimension and maximum speed dimension, and the accumulated value on the speed accumulation dimension is obtained by statistically analyzing the time during the climb phase of the target aircraft when the speed of the high-pressure rotor of the engine under test exceeds 90% and is less than 115%; the vibration dimension includes vibration accumulation The dimensions include the maximum vibration accumulation dimension, where the accumulated value on the vibration accumulation dimension is obtained by statistically analyzing the duration of vibration of the engine under test during the flight of the target aircraft, and the accumulated value on the maximum vibration accumulation dimension is obtained by statistically analyzing the maximum abnormal vibration value of the engine under test during the flight of the target aircraft; the aircraft climb-related dimensions include the aircraft climb time dimension and the aircraft climb rate dimension, where the accumulated value on the aircraft climb time dimension is obtained by statistically analyzing the time from the start of climb to the cruise altitude of the target aircraft, and the accumulated value on the aircraft climb rate dimension is obtained based on the climb time and cruise altitude of the target aircraft; For each specified dimension, a chart similarity analysis is performed based on the specified dimension feature data, the historical health feature data, and the historical unhealthy feature data to obtain the current chart similarity result for each specified dimension. For each specified dimension, a data distribution difference analysis is performed based on the specified dimension feature data, the historical health feature data, and the historical unhealthy feature data to obtain the current health assessment data for each specified dimension; Based on the current chart similarity results and the current health assessment data, the current reliability data of the high-pressure turbine blade in each specified dimension is generated; wherein, the current reliability data is used to describe whether there are preset safety type cracks on the pressure surface region of the high-pressure turbine blade; Based on the current reliability data, suitable life extension management measures are determined for the high-pressure turbine blades; wherein, the life extension management measures are used to guide the subsequent flight plans and / or subsequent troubleshooting plans of the target aircraft, the target aircraft being an aircraft equipped with the engine to be tested.

2. The method according to claim 1, characterized in that, The step of generating current reliability data for the high-pressure turbine blades in each specified dimension based on the current chart similarity results and the current health assessment data includes: The current reliability data is obtained by searching the reliability relationship data corresponding to each specified dimension based on the current chart similarity results and the current health assessment data; wherein, the reliability relationship data is used to describe the correspondence between the chart similarity results, the health assessment data and the reliability data of the high-pressure turbine blade in each specified dimension.

3. The method according to claim 1, characterized in that, The step of performing chart similarity analysis based on the specified dimension feature data, the historical health feature data, and the historical unhealthy feature data to obtain the current chart similarity result on each specified dimension includes: In response to the box plot drawing operation, the specified dimension feature data, the historical health feature data, and the historical unhealthy feature data are displayed in the target box plot, and a box plot similarity comparison result is generated; In response to the histogram drawing operation, the specified dimension feature data, the historical health feature data, and the historical unhealthy feature data are displayed in their respective histograms, and histogram similarity comparison results are generated. In response to the normal distribution plotting operation, the specified dimension feature data, the historical health feature data, and the historical unhealthy feature data are displayed on the normal distribution plot, and a normal distribution similarity comparison result is generated; The similarity results of the current chart are obtained by summarizing the similarity comparison results of the box plot, the histogram, and the normal distribution.

4. The method according to claim 1, characterized in that, The step of performing data distribution difference analysis based on the specified dimension feature data, the historical health feature data, and the historical unhealthy feature data to obtain the current health assessment data for each specified dimension includes: Determine the health data distribution characteristics of the historical health characteristic data, the unhealth data distribution characteristics of the historical unhealthy characteristic data, and the current data distribution characteristics of the specified dimension characteristic data; The current health assessment data is determined based on a first difference between the health data distribution characteristics and the current data distribution characteristics, and a second difference between the unhealthy data distribution characteristics and the current data distribution characteristics.

5. The method according to claim 1, characterized in that, The preset safety type crack is a crack with a length less than a preset safety threshold or a crack with a length greater than or equal to the preset safety threshold but not extending to the end face; wherein, the preset safety threshold is less than or equal to 2 mm.

6. The method according to any one of claims 1 to 5, characterized in that, The specified dimension is determined by at least one of the following methods: The nonlinear relationship between the high-pressure turbine blade damage data and the engine high-pressure rotor speed is obtained by fitting the data. The nonlinear relationship data describes the nonlinear increase of the high-pressure turbine blade damage data with the increase of the engine high-pressure rotor speed, and indicates that the speed dimension is a specified dimension. Based on the positive correlation between engine vibration and the root crack growth rate of high-pressure turbine blades, the vibration dimension is designated as a specific dimension. Based on the cooling relationship between engine exhaust temperature and idling time, idling time is used as a specified dimension. Based on the relationship between the thermal expansion and deformation of high-pressure turbine blades and the location of stress concentration, the aircraft climb-related dimension is designated as a specific dimension.

7. The method according to any one of claims 1 to 5, characterized in that, The life extension management measures include at least one of the following: limiting the maximum load of the aircraft, mandating a minimum idling time, adjusting the flight mission of the target aircraft, and performing engine troubleshooting.

8. A system for extending the lifespan of high-pressure turbine blades, characterized in that, The system includes: The data acquisition module is used to acquire characteristic data of the engine under test across multiple specified dimensions. These specified dimensions are feature dimensions capable of causing specified types of damage to the high-pressure turbine blades. Each specified dimension corresponds to historical healthy characteristic data and historical unhealthy characteristic data. The multiple specified dimensions include speed dimension, vibration dimension, flight time dimension, idling time dimension, and aircraft climb-related dimensions. The speed dimension includes a speed accumulation dimension and a maximum speed dimension. The accumulated value on the speed accumulation dimension is obtained by statistically analyzing the time during the climb phase of the target aircraft when the high-pressure rotor speed of the engine under test exceeds 90% but is less than 115%. The vibration dimension includes... The dimensions include vibration accumulation dimension and maximum vibration accumulation dimension. The accumulated value in the vibration accumulation dimension is obtained by statistically analyzing the vibration duration of the engine under test during the flight of the target aircraft. The accumulated value in the maximum vibration accumulation dimension is obtained by statistically analyzing the maximum abnormal vibration value of the engine under test during the flight of the target aircraft. The aircraft climb-related dimensions include aircraft climb time dimension and aircraft climb rate dimension. The accumulated value in the aircraft climb time dimension is obtained by statistically analyzing the time from the start of the climb to the cruise altitude of the target aircraft. The accumulated value in the aircraft climb rate dimension is obtained based on the climb time and cruise altitude of the target aircraft. The chart analysis module is used to perform chart similarity analysis on each specified dimension based on the feature data of the specified dimension, the historical health feature data, and the historical unhealthy feature data, to obtain the current chart similarity result on each specified dimension. The distribution analysis module is used to perform data distribution difference analysis on each specified dimension based on the specified dimension feature data, the historical health feature data, and the historical unhealthy feature data, to obtain the current health assessment data on each specified dimension. The reliability analysis module is used to generate current reliability data of the high-pressure turbine blade in each specified dimension based on the current chart similarity results and the current health assessment data; wherein, the current reliability data is used to describe whether there are preset safety type cracks on the pressure surface region of the high-pressure turbine blade; The measure determination module is used to determine suitable life extension control measures for the high-pressure turbine blades based on the current reliability data; wherein the life extension control measures are used to guide the subsequent flight plan and / or subsequent troubleshooting plan of the target aircraft, and the target aircraft is an aircraft equipped with the engine to be tested.

9. A computer device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 7.

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