Method, system and equipment for prolonging service life of high-pressure turbine blade

Abstract

The invention relates to the technical field of aircraft part service life analysis, and discloses a service life prolonging method, system and equipment for a high-pressure turbine blade, and the service life prolonging method comprises the steps: obtaining the specified dimension feature data of a to-be-detected engine in a plurality of specified dimensions, and carrying out the chart similarity analysis and data distribution difference analysis on the specified dimension feature data, so as to prolong the service life of the to-be-detected engine; according to the method, the current reliability data of the high-pressure turbine blade on each specified dimension is obtained, health state evaluation of the high-pressure turbine blade is realized, and further, a service life prolonging management and control measure suitable for the high-pressure turbine blade is determined based on the current reliability data, so that a follow-up flight scheme of a target aircraft can be guided, and the reliability of the high-pressure turbine blade is improved. The technical effect of prolonging the service life of the high-pressure turbine blade is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of aircraft parts life analysis, and in particular to a method, system and device for extending the life of a high-pressure turbine blade. Background Art

[0002] A civil aviation engine consists of a fan, a low-pressure compressor, a high-pressure compressor, a combustion chamber, a high-pressure turbine and a low-pressure turbine. Among them, the high-pressure turbine blades of the high-pressure turbine are the key power source of the engine. Once the high-pressure turbine blades are damaged, the high-pressure compressor will not be able to work, and the engine will lose all power, causing it to stall in the air.

[0003] As a time-limited component, high-pressure turbine blades have mandatory replacement requirements in the continuous airworthiness documents of aviation products such as aircraft, engines or propellers to ensure flight safety. The manufacturer will provide a recommended time interval for time-limited components, and the relevant departments will refer to the manufacturer's recommended interval and issue a minimum time interval standard. In order to ensure absolute safety, most airlines usually shorten the replacement interval on this basis. Moreover, the operating conditions encountered during actual flight will affect the actual service life of the high-pressure turbine blades, so there is an urgent need for a method to extend the life of high-pressure turbine blades. Summary of the invention

[0004] The present application provides a method, system and equipment for extending the life of high-pressure turbine blades, which can perform reliability detection on high-pressure turbine blades in an on-wing state, thereby guiding the aircraft's subsequent flight plan or subsequent troubleshooting plan through corresponding life extension control measures to extend the service life of the high-pressure turbine blades.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0006] In a first aspect, an embodiment of the present application provides a method for extending the life of a high-pressure turbine blade, the method comprising:

[0007] Acquire specified dimension feature data of the engine to be tested in multiple specified dimensions; wherein the multiple specified dimensions are feature dimensions that can cause specified types of damage to the high-pressure turbine blades; and each specified dimension corresponds to historical healthy feature data and historical unhealthy feature data;

[0008] For each of the specified dimensions, a chart similarity analysis is performed based on the specified dimension feature data, the historical healthy feature data, and the historical unhealthy feature data to obtain a current chart similarity result on each of the specified dimensions;

[0009] For each of the specified dimensions, 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 current health assessment data on each of the specified dimensions;

[0010] 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; wherein 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;

[0011] Determine life extension control measures suitable for the high-pressure turbine blades based on the current reliability data; wherein the life extension control measures are used to guide a subsequent flight plan and / or a subsequent troubleshooting plan of a target aircraft, and the target aircraft is an aircraft equipped with the engine to be tested.

[0012] The method for extending the life of high-pressure turbine blades proposed in the embodiment of the present application obtains specified dimensional feature data of the engine to be tested in multiple specified dimensions, and performs chart similarity analysis and data distribution difference analysis on the specified dimensional feature data to obtain the current reliability data of the high-pressure turbine blades in each of the specified dimensions, thereby realizing a health status assessment of the high-pressure turbine blades. Furthermore, based on the current reliability data, life extension control measures suitable for the high-pressure turbine blades are determined, thereby guiding the subsequent flight plan of the target aircraft and achieving the technical effect of extending the life of the high-pressure turbine blades. Not only can the safety of the aircraft during flight be improved, but also the high-pressure turbine blades can be fully utilized to reduce the economic cost caused by replacing parts such as high-pressure turbine blades.

[0013] In a second aspect, an embodiment of the present application provides a life extension system for a high-pressure turbine blade, the system comprising:

[0014] A data acquisition module is used to acquire specified dimension feature data of the engine to be tested in multiple specified dimensions; wherein the multiple specified dimensions are feature dimensions that can cause specified types of damage to the high-pressure turbine blades; and each specified dimension corresponds to historical healthy feature data and historical unhealthy feature data;

[0015] A chart analysis module, for performing a chart similarity analysis on each of the specified dimensions according to the specified dimension feature data, the historical healthy feature data, and the historical unhealthy feature data, to obtain a current chart similarity result on each of the specified dimensions;

[0016] A distribution analysis module, configured to perform data distribution difference analysis on each designated dimension according to the designated dimension feature data, the historical health feature data, and the historical unhealthy feature data, to obtain current health assessment data on each designated dimension;

[0017] A reliability analysis module, used for generating 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; wherein the current reliability data is used for describing whether there is a preset safety type crack on the pressure surface area of ​​the high-pressure turbine blade;

[0018] A measure determination module is used to determine a life extension control measure suitable for the high-pressure turbine blade based on 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, and the target aircraft is an aircraft equipped with the engine to be tested.

[0019] In a third aspect, an embodiment of the present application provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method described in any one of the above embodiments by executing the computer instructions.

[0020] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute any one of the methods in the above embodiments.

[0021] In a fifth aspect, an embodiment of the present application provides a computer program product, including computer instructions, and the computer instructions are used to enable a computer to execute any one of the methods in the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1a A step diagram of a method for extending the life of a high-pressure turbine blade provided in an embodiment of the present application;

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

[0025] Figure 2 A diagram of steps for obtaining similar results of the current chart on each specified dimension in an embodiment of the present application;

[0026] Figure 3a is a target box plot in any specified dimension in an embodiment of the present application;

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

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

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

[0030] Figure 3e is a normal distribution diagram in any specified dimension in the embodiment of the present application;

[0031] Figure 4 A diagram of the steps for obtaining the current health assessment data on each specified dimension in an embodiment of the present application;

[0032] Figure 5 A cross-sectional schematic diagram of a high-pressure turbine blade in an embodiment of the present application;

[0033] Figure 6 A module diagram of a life extension system for high-pressure turbine blades provided in an embodiment of the present application;

[0034] Figure 7 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

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

[0036] Civil aviation engines include a fan, a low-pressure compressor, a high-pressure compressor, a combustion chamber, a high-pressure turbine, and a low-pressure turbine. After the gas is mixed with the fuel in the combustion chamber and expanded and burned, it first passes through the high-pressure turbine blades, which drive the high-pressure compressor in front, and then passes through the low-pressure turbine, which drives the low-pressure compressor and the fan. Therefore, the high-pressure turbine blades of the high-pressure turbine are the key power source of the engine. If the high-pressure turbine blades are damaged, the high-pressure compressor will not work, and the engine will lose all power, causing it to stop in the air.

[0037] Civil aviation service life parts refer to parts that have mandatory replacement requirements in the continuing airworthiness documents of aviation products such as aircraft, engines or propellers. The continuing airworthiness documents stipulate a certain time interval for this. When the parts reach the specified time interval, regardless of their actual condition, they must be replaced compulsorily to ensure flight safety. The time interval for service life parts is usually recommended by the manufacturer, and the Civil Aviation Administration refers to the manufacturer's recommended interval and issues a minimum time interval standard, which is the longest time the part can be used. However, in order to ensure absolute safety, most airlines shorten the replacement interval on this basis.

[0038] In the related technology, on the one hand, during the actual flight of the aircraft, the aircraft often encounters severe working conditions, resulting in the actual service life of the high-pressure turbine blades being lower than the theoretical life. At this time, relying solely on time life management cannot detect problems in time, resulting in safety risks during the flight process. On the other hand, since the high-pressure turbine blades are expensive, if the time interval is blindly shortened to ensure safety, the usable life of the high-pressure turbine blades will be wasted, resulting in significant economic losses.

[0039] In order to timely understand the health status of high-pressure turbine blades, in related technologies, conventional civil aviation engines are usually inspected using a borescope method, for example, an industrial endoscope is inserted into the interior of the engine to be inspected, and the image is displayed through a camera for inspection by a professional engineer. However, an industrial endoscope cannot detect the key parts of the high-pressure turbine blade components (the bottom connecting tenon), so the borescope method cannot detect the damage to the key parts of the high-pressure turbine blades.

[0040] Based on the above problems, the present application provides a method, system and device for extending the life of a high-pressure turbine blade, wherein the method may include: first, obtaining the specified dimension feature data of the engine to be tested on multiple specified dimensions, wherein the multiple specified dimensions are feature dimensions that can cause specified types of damage to the high-pressure turbine blade; each specified dimension corresponds to historical healthy feature data and historical unhealthy feature data. Secondly, the specified dimension feature data is compared with the historical healthy feature data and the historical unhealthy feature data from two perspectives. Specifically, for each specified dimension, a chart similarity analysis is performed based on the specified dimension feature data, the historical healthy feature data and the historical unhealthy feature data to obtain the current chart similarity result on each specified dimension; data distribution difference analysis is performed based on the specified dimension feature data, the historical healthy feature data and the historical unhealthy feature data to obtain the current health assessment data on each specified dimension. Then, the current reliability data of the high-pressure turbine blade on each specified dimension is generated based on the current chart similarity result and the current health assessment data. Finally, the life extension control measures suitable for the high-pressure turbine blade are determined based on the current reliability data to guide the subsequent flight plan and / or subsequent troubleshooting plan of the target aircraft.

[0041] The life extension method, system and equipment for high-pressure turbine blades provided in the present application can perform reliability detection on high-pressure turbine blades in an on-wing state, thereby guiding the aircraft's subsequent flight plan or subsequent troubleshooting plan through corresponding life extension control measures to extend the service life of the high-pressure turbine blades.

[0042] The life extension method of high-pressure turbine blades provided in this specification can be applied to evaluate the health status of high-pressure turbine blades in civil aviation engines, obtain their current reliability data in multiple specified dimensions, and determine the life extension control measures suitable for high-pressure turbine blades based on the current reliability data to guide the subsequent flight plan and / or subsequent troubleshooting plan of the target aircraft, thereby extending the life of the high-pressure turbine blades. It is understandable that the method provided in this specification can also evaluate the health status of other components in civil aviation engines after adaptive modification, and also guide the target aircraft to extend the life of the corresponding components. Of course, in addition to civil aviation engines, other engines used for aircraft can also be applicable to the method provided in this specification.

[0043] It should be noted that in the life extension method of high-pressure turbine blades provided in the present application, the data basis for obtaining the characteristic data of a specified dimension can be the QAR data of the target aircraft. During the operation of the aircraft, the corresponding data will be collected through sensors and recorded in a certain format. These data are called QAR (Quick Access Recorder) data. After the aircraft lands, the QAR data will 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, status, etc. The QAR data can also be used as a basis for fault diagnosis. QAR data can have different recording frequencies, and the recording frequency will increase with the importance of the sensor recording the parameters, usually once every 1s.

[0044] According to an embodiment of the present application, an embodiment of a method for extending the 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 a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0045] In this embodiment, a method for extending the life of a high-pressure turbine blade is provided, which can be used for the above-mentioned civil aviation engine and other components. Figure 1a , Figure 1a The step diagram of the method for extending the life of a high-pressure turbine blade provided in an embodiment of the present application is shown in the figure. The method comprises:

[0046] S100. Obtain specified dimensional feature data of the engine to be tested in multiple specified dimensions; wherein the multiple specified dimensions are feature dimensions that can cause specified types of damage to the high-pressure turbine blades; and each specified dimension corresponds to historical healthy feature data and historical unhealthy feature data.

[0047] S200. For each designated dimension, perform a chart similarity analysis based on the designated dimension feature data, historical healthy feature data, and historical unhealthy feature data to obtain a current chart similarity result on each designated dimension.

[0048] S300. For each designated dimension, perform data distribution difference analysis based on the designated dimension feature data, historical health feature data, and historical unhealthy feature data to obtain current health assessment data on each designated dimension.

[0049] S400. 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 is a preset safety type crack on the pressure surface area of ​​the high-pressure turbine blade.

[0050] S500. Determine life extension control measures suitable for high-pressure turbine blades based on current reliability data; wherein the life extension control measures are used to guide subsequent flight plans and / or subsequent troubleshooting plans of a target aircraft, and the target aircraft is an aircraft equipped with an engine to be tested.

[0051] Specifically, the data basis for obtaining the specified dimension feature data can be the QAR data of the target aircraft, and the QAR data includes the flight-related parameters of the target aircraft and the engine performance-related parameters of the engine to be detected in the target aircraft. The specified dimension feature data of the engine to be detected is obtained from the QAR data according to multiple specified dimensions. Among them, the multiple specified dimensions are feature dimensions that can cause specified types of damage to the high-pressure turbine blades, and the specified type of damage can be understood as the damage caused to the high-pressure turbine blades when the target aircraft operates in an improper parameter range on the specified dimension.

[0052] To illustrate how to select a specified dimension, if there is a crack at the root of a high-pressure turbine blade, it will cause the crack to grow 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 and the longer the accumulation time, 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 greater impact on the safety of the target aircraft during flight. Therefore, the vibration condition of the transmitter can be selected as a specified dimension. It can be understood that the specified type of damage can be the growth of a crack at the root of a high-pressure turbine blade.

[0053] To illustrate how to select a specified dimension, for example, the engine idling time will affect the cooling of the high-pressure turbine blades. If the cooling is insufficient, the high-pressure turbine blades and turbine disks may expand and deform accordingly. Therefore, the engine idling time can be selected as a specified dimension. It is understandable that the specified type of damage can be the expansion and deformation of the high-pressure turbine blades.

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

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

[0056] Specifically, in the chart similarity analysis, the specified dimension feature data, historical health feature data, and historical unhealthy feature data on any specified dimension are represented by a specific type of chart to reflect the statistical characteristics of the three types of data. For example, the specific type of chart includes but is not limited to one or more chart types such as a scatter plot, a density plot, a quantile plot, or a violin plot, and the type of chart can be determined according to the actual usage scenario.

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

[0058] Furthermore, in the data distribution difference analysis, the specified dimension feature data, historical healthy feature data, and historical unhealthy feature data on any specified dimension all have certain data distribution characteristics, and the data distribution characteristics can be represented by multiple statistics in the above data. For example, the statistics include but are not limited to one or more of the types such as median, variance, skewness, and correlation coefficient, and the type of statistics can be determined according to the actual usage scenario. It can be understood that the data distribution difference analysis is performed on the specified dimension feature data, historical healthy feature data, and historical unhealthy feature data on any specified dimension, respectively, to obtain the difference between the engine to be tested and the engine in an unhealthy state, and the difference between the engine to be tested and the engine in a healthy state, thereby generating the current health assessment data on any specified dimension.

[0059] Furthermore, based on the current chart similarity results and the current health assessment data, the current reliability data of the high-pressure turbine blades in the engine to be tested in each specified dimension can be obtained. For example, the current chart similarity results 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 blades in each specified dimension. The classification model can adopt one or more types such as logistic regression, support vector machine (SVM), decision tree, random forest and neural network. The current reliability data of the high-pressure turbine blades is exemplified. Among them, the current reliability data is used to describe whether there are preset safety type cracks on the pressure surface area of ​​the high-pressure turbine blades. Please refer to Figure 1b , with the use of the engine to be tested, Figure 1bSome cracks may appear in the pressure surface area 100 shown. The cracks in the pressure surface area 100 may have different lengths. The preset safety type cracks may be cracks that exist in the pressure surface area but have no effect on the health of the engine for the time being. For example, the length of the preset safety type cracks is relatively small, and the preset safety type cracks in the pressure surface area need a certain amount of time to grow into long cracks that have a certain effect on the health of the engine.

[0060] The reliability data used to describe whether there are preset safety type cracks on the pressure surface area of ​​the high-pressure turbine blade is exemplified. The reliability data may include level 0 health state, level 1 health state and level 2 health state. Level 0 health state: the high-pressure turbine blade has no cracks in the pressure surface area; level 1 health state: the high-pressure turbine blade has tiny cracks in the pressure surface area, and the crack length is less than 2mm; level 2 health state: the high-pressure turbine blade has cracks in the pressure surface area, and the crack length is greater than 2mm, but does not extend to the end surface.

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

[0062] 1) There are cracks along the small neck of the high-pressure turbine blade that extend to the end face, and the extension length inside the end face is less than 1mm

[0063] 2) There is a crack along the small neck of the high-pressure turbine blade that extends to the end face and extends to more than 1mm inside the end face, but the crack does not extend to a third surface (such as the initiation surface, end face, bottom surface or another side surface)

[0064] 3) There are cracks along the small neck of the high-pressure turbine blade that extend to the end surface, and the cracks extend to a third surface (such as the initiation surface, end surface, bottom surface or another side surface)

[0065] 4) The high-pressure turbine blade breaks into two parts along the small neck, but the remaining material can still be restrained by the turbine disk and will not fall out

[0066] 5) The high-pressure turbine blade breaks into at least two parts along the small neck, and the turbine disk cannot prevent the part above the small neck from detaching from the turbine disk, and the broken part can completely detach from the engine (i.e. fly out)

[0067] Furthermore, since the current reliability data can describe whether there are preset safety type cracks on the pressure surface area of ​​the high-pressure turbine blade, reasonable life extension control measures are output for the high-pressure turbine blade 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 there are no preset safety type cracks on the pressure surface area, the target aircraft can continue to use the current flight plan without adjusting the flight plan of the target aircraft. If the current reliability data indicates that there are preset safety type cracks on the pressure surface area, the target aircraft cannot continue to use the current flight plan, and the flight plan of the target aircraft needs to be adjusted to reduce the probability of the preset safety type cracks continuing to grow or maintain the status quo of the preset safety type cracks.

[0068] The method for extending the life of high-pressure turbine blades provided in the present embodiment obtains specified dimensional feature data of the engine to be tested in multiple specified dimensions, and performs chart similarity analysis and data distribution difference analysis on the specified dimensional feature data to obtain current reliability data of the high-pressure turbine blades in each specified dimension, thereby achieving a health status assessment of the high-pressure turbine blades. Furthermore, based on the current reliability data, life extension control measures suitable for the high-pressure turbine blades are determined, thereby guiding the subsequent flight plan of the target aircraft and achieving the technical effect of extending the life of the high-pressure turbine blades. Not only can the safety of the aircraft during flight be improved, but also the high-pressure turbine blades can be fully utilized to reduce the economic cost caused by replacing parts such as high-pressure turbine blades.

[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 assessment data, including: searching in the 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. The reliability relationship data can be used to describe the corresponding relationship between the chart similarity result, the health assessment data and the reliability data of the high-pressure turbine blade in each specified dimension.

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

[0071] Specifically, reliability relationship data is established in advance for each specified dimension. On any specified dimension, if the current chart similarity result and the current health assessment data are determined, a search is performed in the reliability relationship data corresponding to the any specified dimension based on the current chart similarity result and the current health assessment data to obtain the corresponding current reliability data to determine whether there is a preset safety type crack on the pressure surface area of ​​the high-pressure turbine blade.

[0072] Reference Figure 2 , Figure 2 The step diagram for obtaining the similarity result of the current chart on each specified dimension in the embodiment of the present application is shown in the figure. As an embodiment of the present application, a chart similarity analysis is performed based on the specified dimension feature data, the historical healthy feature data, and the historical unhealthy feature data to obtain the similarity result of the current chart on each specified dimension, including:

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

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

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

[0076] S240. Summarize the box plot similarity comparison results, the histogram similarity comparison results, and the normal distribution similarity comparison results to obtain the current chart similarity results.

[0077] Specifically, the chart types used for chart similarity analysis can be box plots, histograms and normal distribution graphs; among them, the box plot is a statistical chart used to display the dispersion information of a set of data, mainly used to reflect the characteristics of the original data distribution, and can also compare the distribution characteristics of multiple groups of data; the histogram is a statistical report chart, which represents the data distribution by a series of vertical stripes or line segments of unequal heights; the normal distribution graph is a probability distribution graph drawn based on normally distributed data.

[0078] Furthermore, the box plot mainly reflects the similarity of the data in terms of central position, distribution range, variability, symmetry and overall distribution shape. Figure 3a , Figure 3aThis is a target box plot on any specified dimension in the embodiment of the present application, as shown in the figure, where self represents the distribution data of the feature data of the specified dimension, n represents the distribution data of the historical healthy feature data, and abn represents the distribution data of the historical unhealthy feature data. Figure 3a It can be determined that the box plot similarity comparison result is that in the corresponding specified dimension, the similarity between the specified dimension feature data and the historical healthy feature data is higher, while the similarity between the specified dimension feature data and the historical unhealthy feature data is lower.

[0079] Furthermore, the histogram mainly reflects the similarity of data in terms of distribution shape, central tendency, dispersion, multimodality and overall frequency. Figure 3b to Figure 3d , Figure 3b is a histogram of feature data of a specified dimension on any specified dimension in an embodiment of the present application, Figure 3c is a histogram of historical health feature data on any specified dimension in an embodiment of the present application, Figure 3d This is a histogram of historical unhealthy feature data on any specified dimension in the embodiment of the present application. As shown in the figure, on any specified dimension, the above data all have peaks at the same position, while the peak sizes of different data are different. In addition, the values ​​of the above data at different positions in the histogram are also different. By comparison, it can be obtained that the histogram similarity comparison result is that on the corresponding specified dimension, the similarity between the specified dimension feature data and the historical healthy feature data is higher, while the similarity between the specified dimension feature data and the historical unhealthy feature data is lower.

[0080] Furthermore, the normal distribution graph mainly reflects the similarity of the data in terms of mean, standard deviation, shape, tail characteristics, overall shape and degree of overlap. Figure 3e , Figure 3e This is a normal distribution diagram on 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 health 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, while the blue curve is farther away from the red curve. Therefore, the normal distribution similarity comparison result can be obtained that on the corresponding specified dimension, the specified dimension feature data has a higher similarity with the historical health feature data, while the specified dimension feature data has a lower similarity with the historical unhealthy feature data.

[0081] By using a variety of chart types to perform chart similarity analysis, not only can the similarities between the specified dimension feature data, historical healthy feature data, and historical unhealthy feature data be evaluated and analyzed from multiple aspects, thereby improving the comprehensiveness of the current chart similarity results, but it can also effectively reduce the interference caused by the randomness of the specified dimension feature data and improve the accuracy of the current chart similarity results.

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

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

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

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

[0086] Specifically, the types of the health data distribution characteristics, the unhealthy data distribution characteristics, and the current data distribution characteristics can be at least one of the data distribution characteristics such as the median, mode, variance, quantile, and range. The health data distribution characteristics and the current data distribution characteristics are compared to obtain the first difference data, and the unhealthy data distribution characteristics and the current data distribution characteristics are compared to obtain the second difference data. At this time, the current health assessment data can be determined based on 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 may be the mean and standard deviation of the above data. By quantitatively comparing the health data distribution feature with the current data distribution feature, first difference data may be obtained in the following form:

[0088] Δμ HS,i =|μ S,i -μ H,i |

[0089] Δσ HS,i =|σ S,i -σ H,i |

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

[0091] Accordingly, the second difference data is in the following form:

[0092] Δμ US,i =|μ S,i -μ U,i |

[0093] Δσ US,i =|σ S,i -σ U,i |

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

[0095] Further, 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 the form thereof is as follows:

[0096]

[0097] Among them, P H represents the health score, P U Indicates unhealthy score; α i is the correction factor for the i-th specified dimension.

[0098] As an embodiment of the present application, the multiple specified dimensions include a speed dimension, a vibration dimension, a flight time dimension, an idling time dimension, and an aircraft climb-related dimension. Among them, the speed dimension includes a speed accumulation dimension and a maximum 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, it is found through analysis that different feature dimensions have different degrees of influence on the life of the engine to be tested. In order to extend the life of the engine to be tested, it is necessary to determine the feature dimensions that can cause specified types of damage to the high-pressure turbine blades in the feature dimensions of the QAR data, that is, the specified dimensions. Specifically, the specified dimensions include the speed dimension, the vibration dimension, the flight time dimension, the idling time dimension, and the aircraft climb-related dimension. The cumulative value of each specified dimension can be obtained through the QAR data of the target aircraft.

[0100] In this embodiment, from the analysis of physical mechanism, the faster the engine speed, the greater the rotational stress generated, and the greater the impact on the life of the high-pressure turbine blades. From the analysis of operation and maintenance experience, starting from 90%, the speed will generate greater rotational stress on the high-pressure turbine blades, and long-term accumulation will lead to a reduction in the life of the high-pressure turbine blades. Based on this, the speed dimension characterizes the impact of the speed of the engine to be tested on its life. The speed dimension includes the speed accumulation dimension and the maximum speed dimension, wherein the cumulative value on the speed accumulation dimension is obtained by statistically analyzing the time when the speed of the engine high-pressure rotor exceeds 90% and is less than 115% during the target aircraft's climb phase. When counting, 1% is used as the minimum interval and 1 second is used as the minimum cumulative value, and the corresponding cumulative value is obtained on the speed accumulation dimension to determine the characteristic data on the speed accumulation dimension. The cumulative value on the maximum speed dimension is obtained by statistically analyzing the maximum value of the engine high-pressure rotor speed during the climb phase of the target aircraft. When counting, each flight process of the target aircraft is taken as a cycle, and the maximum speed of the engine high-pressure rotor in the cycle is statistically analyzed to determine the characteristic data on the maximum speed dimension.

[0101] In this embodiment, theoretical analysis and experimental data show that high vibration will cause abnormal stress on the engine rotor, and the vibration is positively correlated with the crack at the root of the blade, that is, the higher the vibration and the longer the accumulation time, the faster the crack growth rate. Based on this, the vibration dimension characterizes the impact of abnormal vibration on the life of the engine to be detected. The vibration dimension includes a vibration accumulation dimension and a maximum vibration accumulation dimension, wherein 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 used as the minimum interval and 1s is used 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 value of the 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 value of the abnormal vibration of the engine to be detected in the cycle is statistically analyzed to determine the characteristic data on the maximum vibration accumulation dimension.

[0102] In this embodiment, after analysis and research, it is found that the flight time of the aircraft in the air has a direct correlation with the engine life. Based on this, the flight time dimension characterizes the impact of the duration of the target aircraft's flight process on the life of the engine to be detected. The cumulative value on the specified dimension is obtained by statistically analyzing the duration of the target aircraft's flight process. When statistically analyzing, 30 minutes is used as the shortest flight time, 1 minute is used as the minimum interval, and 1 second is used 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, after analysis and research, it is found that the time the engine glides at idle power after the aircraft lands will directly determine the cooling of the engine and has a direct correlation with the life of the high-pressure turbine blades in the engine. Based on this, the idle operation time dimension characterizes the impact of the idle operation time of the target aircraft on the life of the engine to be tested. The cumulative value on the specified dimension is obtained by counting the time the engine glides at idle power after the target aircraft lands. When counting, starting from 0 minutes, 1 minute is used as the minimum interval, and 1 second is used as the minimum cumulative value to obtain the corresponding cumulative value to determine the characteristic data on the idle operation time dimension.

[0104] In this embodiment, after analysis and research, the aircraft begins to rise in altitude after takeoff until it reaches the cruising altitude. The rising time and rising speed in this process are directly related to the life of the high-pressure turbine blades. Based on this, the aircraft climb-related dimension characterizes the impact of the target aircraft's actions during the climb process on the life of the engine to be detected. The aircraft climb-related dimensions include the aircraft climb time dimension and the aircraft climb rate dimension, wherein the aircraft climb time dimension characterizes the impact of the target aircraft's climb time on the life of the engine to be detected, and the cumulative value on the aircraft climb time dimension is obtained by counting the time from the start of the target aircraft's climb to the cruising altitude. When counting, 10 minutes is used as the shortest climb time, 1 minute is used as the minimum interval, and 1 second is used as the minimum cumulative value to obtain the corresponding cumulative value to determine the characteristic data on the aircraft climb time dimension. The aircraft climb rate dimension characterizes the impact of the target aircraft's climb time speed on the life of the engine to be detected, and the cumulative value on the aircraft climb rate dimension is obtained based on the target aircraft's climb time and cruising altitude. When taking statistics, 5 feet per second is used as the minimum interval to count the climbing speed of the target aircraft during each flight, thereby obtaining the corresponding cumulative value to determine the characteristic data on the aircraft's climb rate dimension.

[0105] As an embodiment of the present application, the preset safety type crack is a crack whose length is less than a preset safety threshold or a crack whose length is greater than or equal to the preset safety threshold but does not extend to the end face; wherein the preset safety threshold is less than or equal to 2 mm.

[0106] Specifically, refer to Figure 5 , Figure 5 This is a cross-sectional schematic diagram of a high-pressure turbine blade in an embodiment of the present application, as shown in the figure, Figure 5 The left side shows the three-dimensional appearance of the high-pressure turbine blade, and the right side shows an enlarged view of the pressure surface area 100 on the high-pressure turbine blade, wherein the protrusion in the pressure surface area is used to connect with the slot in the engine, thereby fixing the high-pressure turbine blade to prevent the high-pressure turbine blade from falling out of the engine. It can be understood that the upper and lower surfaces of the protrusion are the stress-bearing surfaces of the high-pressure turbine blade, and when stress changes occur on the above-mentioned stress-bearing surfaces, cracks may occur in the large neck 101 and the small neck 102.

[0107] Further, by setting a preset safety type for the situation where cracks appear on the large neck 101 and the small neck 102, it is determined whether there is a safety risk on the high-pressure turbine blade. It is understandable that when there are no cracks on the large neck 101 and the small neck 102, there is no safety risk on the high-pressure turbine blade. If a tiny crack appears on the large neck 101 or the small neck 102, but the crack length indicated by the fluorescent penetrant detection is less than the preset safety threshold, it means that the high-pressure turbine blade is in the crack initiation period. At this time, the safety risk of the high-pressure turbine blade is small and it can be considered to be in a healthy state. If a crack appears on the large neck 101 or the small neck 102, the crack length indicated by the fluorescent penetrant detection is greater than or equal to the preset safety threshold, but the crack has not yet extended to the end face 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 is understandable 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 methods:

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

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

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

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

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

[0114] D=a 1 x 2 -b 1 x+c 1

[0115] Where D represents the damage degree data, x represents the engine high pressure rotor speed; a 1 、b 1 and c1 is the coefficient. According to the nonlinear relationship data, when the engine high-pressure rotor speed increases, the degree of damage will increase nonlinearly. The higher the speed, the greater the degree of damage. Among them, starting from the engine high-pressure rotor speed exceeding 90%, it will cause cumulative damage to the high-pressure turbine blades to a certain extent. Therefore, the engine high-pressure rotor speed is set between 90% and 115%, and the accumulated speed and the maximum speed of the flight are counted, and the above nonlinear relationship data is used to calculate the cumulative damage.

[0116] In this 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: the root crack growth rate of the high-pressure turbine blade is positively correlated with the engine vibration condition. The greater the vibration amplitude or vibration frequency of the engine, the faster the root crack growth rate.

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

[0118]

[0119] Wherein, T(t) represents the engine exhaust temperature, which refers to the temperature at the engine turbine outlet section after the fuel gas in the aircraft engine is discharged from the combustion chamber and passes through the high-speed rotating turbine; t represents the idling time; T max is the maximum temperature; T env is the ambient temperature; a 2 、b 2 and c 2 is the coefficient. According to the cooling relationship, when the engine idles for a sufficient time, it can be fully cooled. Therefore, in the next flight of the target aircraft, by extending the engine idle time, the full load time can be extended, that is, the moment when the stress concentration position changes from the big neck 101 to the small neck 102, so as to increase the stress time of the big neck 101 and reduce the probability of cracks in the small neck 102, thereby extending the life of the high-pressure turbine blades.

[0120] In this embodiment, the relationship between the heat expansion and deformation of the high-pressure turbine blades and the stress concentration position can be understood as follows: from the start of full-speed operation, the temperature of the high-pressure turbine blades and the turbine disk begins to rise, and the high-pressure turbine blades and the turbine disk will expand and deform due to heat; when the temperature of the high-pressure turbine reaches a set threshold value T 1 When the pressure is increased, the stress point of the high-pressure turbine blade will change, and the main stress point will be transferred from the large neck 101 to the small neck 102, resulting in a reduction in the life of the high-pressure turbine blade.

[0121] As an embodiment of the present application, the life extension management and control measures include at least one of limiting the maximum load of the aircraft, forcibly setting a minimum idle time, adjusting the flight mission of the target aircraft, and performing engine troubleshooting.

[0122] Specifically, since the current reliability data of the high-pressure turbine blades in each specified dimension is known, the influence of the operating parameters in each specified dimension on the high-pressure turbine blades can be known. For example, if the reliability of the high-pressure turbine blades is affected in the rotational speed accumulation dimension, the maximum load of the target aircraft can be limited according to the cumulative value in the rotational speed accumulation dimension to achieve the goal of reducing the engine power usage and prolonging the life of the high-pressure turbine blades in the engine to be tested.

[0123] If the reliability of the high-pressure turbine blades is affected in the maximum speed dimension, the maximum load of the target aircraft can be limited according to the cumulative value in the maximum speed dimension to achieve the goal of limiting the maximum power usage of the engine and extend the life of the high-pressure turbine blades in the engine to be tested.

[0124] If the reliability of the high-pressure turbine blades is affected in the vibration accumulation dimension or the maximum vibration accumulation dimension, relevant troubleshooting work will be required on the target aircraft, such as checking the vibration source in the target aircraft. At the same time, the maximum load of the target aircraft can be limited according to the cumulative value.

[0125] If the reliability of the high-pressure turbine blades is affected by the idle time dimension, a minimum idle time can be set for the target aircraft for the entire fleet, including the target aircraft, thereby extending the life of the high-pressure turbine blades in the engine to be tested.

[0126] If the reliability of high-pressure turbine blades is affected in the dimension of flight time, the flight plan can be dynamically adjusted. In particular, the flight plan should avoid arranging the same aircraft to continuously perform short-distance missions. At the same time, for aircraft that have performed multiple short-distance flight missions, it is necessary to avoid arranging them to continue to perform short-distance missions as much as possible.

[0127] If the reliability of the high-pressure turbine blades is affected in the aircraft climb time dimension or the aircraft climb rate dimension, the cumulative value of the engine to be tested in the idle time dimension may also be abnormal. The target aircraft can be arranged to execute a long climb time route while avoiding short-range missions.

[0128] Accordingly, please refer to Figure 6 The embodiment of the present application provides a life extension system for a high-pressure turbine blade, the system comprising:

[0129] The data acquisition module 201 is used to obtain the specified dimensional feature data of the engine to be tested in multiple specified dimensions; wherein the multiple specified dimensions are feature dimensions that can cause specified types of damage to the high-pressure turbine blades; and each specified dimension corresponds to historical healthy feature data and historical unhealthy feature data.

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

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

[0132] The reliability analysis module 204 is used to generate the 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 is a preset safety type crack on the pressure surface area of ​​the high-pressure turbine blade.

[0133] The measure determination module 205 is used to determine the life extension control measures suitable 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.

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

[0135] The current reliability data calculation unit is used to search in the reliability relationship data corresponding to each specified dimension according to the current chart similarity results and the current health assessment data to obtain the current reliability 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 blades in each specified dimension.

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

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

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

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

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

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

[0142] The current data distribution feature determination unit is used to determine the healthy data distribution features of the historical healthy feature data, the unhealthy data distribution features of the historical unhealthy feature data, and the current data distribution features of the specified dimension feature data.

[0143] The difference evaluation unit is used to determine the current health evaluation data based on the first difference data between the healthy 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 multiple specified dimensions in the data acquisition module 201 include a rotation speed dimension, a vibration dimension, a flight time dimension, an idling time dimension, and an aircraft climb-related dimension.

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

[0146] The vibration dimension includes the vibration accumulation dimension and the maximum vibration accumulation dimension.

[0147] Aircraft climb-related dimensions include aircraft climb time dimension and aircraft climb rate dimension.

[0148] In some optional embodiments, 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 speed of the engine high-pressure rotor and the damage data; wherein the nonlinear relationship data is used to describe the situation that the damage data of the high-pressure turbine blades increases nonlinearly with the increase of the speed of the engine high-pressure rotor, and indicates that the speed dimension is used as a specified dimension.

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

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

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

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

[0155] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

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

[0157] See also Figure 7 , Figure 7 It is a structural diagram of a computer device provided by an embodiment of the present application. As shown in the figure, the computer device includes: one or more processors 10, a memory 20, and an interface for connecting various components, including a high-speed interface and a low-speed interface. Each component uses different buses to communicate with each other, and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 7 A processor 10 is taken as an example.

[0158] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0159] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0160] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

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

[0162] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0163] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0164] The embodiment of the present application provides a computer program product, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method of any embodiment of the present application.

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

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

[0167] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0168] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0169] The present application is described with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, and the combination of the process and / or box in the flowchart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and / or one box or multiple boxes in the block diagram.

[0170] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0171] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0172] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0173] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0174] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

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

Claims

1. A method for extending the life of a high-pressure turbine blade, characterized in that: The method comprises: Acquire specified dimension feature data of the engine to be tested in multiple specified dimensions; wherein the multiple specified dimensions are feature dimensions that can cause specified types of damage to the high-pressure turbine blades; and each specified dimension corresponds to historical healthy feature data and historical unhealthy feature data; For each of the specified dimensions, a chart similarity analysis is performed based on the specified dimension feature data, the historical healthy feature data, and the historical unhealthy feature data to obtain a current chart similarity result on each of the specified dimensions; For each of the specified dimensions, 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 current health assessment data on each of the specified dimensions; 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; wherein 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; Determine life extension control measures suitable for the high-pressure turbine blades based on the current reliability data; wherein the life extension control measures are used to guide a subsequent flight plan and / or a subsequent troubleshooting plan of a target aircraft, and the target aircraft is an aircraft equipped with the engine to be tested.

2. The method according to claim 1, characterized in that The generating the current reliability data of the high pressure turbine blade in each of the specified dimensions according to the current chart similarity result and the current health assessment data comprises: The current reliability data is obtained by searching the reliability relationship data corresponding to each specified dimension according to the current chart similarity result and the current health assessment data; wherein the reliability relationship data is used to describe the correspondence between the chart similarity result, 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 performing of chart similarity analysis based on the specified dimension feature data, the historical healthy 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 plotting operation, the designated dimension feature data, the historical healthy feature data, and the historical unhealthy feature data are displayed in a target box plot, and a box plot similarity comparison result is generated; In response to the histogram drawing operation, the designated dimension feature data, the historical healthy feature data, and the historical unhealthy feature data are respectively displayed in respective histograms, and a histogram similarity comparison result is generated; In response to a normal distribution graph drawing operation, the designated dimension feature data, the historical healthy feature data, and the historical unhealthy feature data are displayed in a normal distribution graph, and a normal distribution similarity comparison result is generated; The similarity result of the current chart is obtained by summarizing the similarity comparison result of the box plot, the similarity comparison result of the histogram and the similarity comparison result of the normal distribution.

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

5. The method according to claim 1, characterized in that The multiple specified dimensions include a rotation speed dimension, a vibration dimension, a flight time dimension, an idling time dimension, and an aircraft climb-related dimension; The speed dimension includes a speed accumulation dimension and a maximum speed dimension; The vibration dimension includes a vibration accumulation dimension and a maximum vibration accumulation dimension; The aircraft climb related dimensions include an aircraft climb time dimension and an aircraft climb rate dimension.

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

7. The method according to any one of claims 1 to 6, characterized in that: The specified dimension is determined by at least one of the following methods: Fitting is performed based on the damage data of the high-pressure turbine blade and the speed of the high-pressure rotor of the engine to obtain nonlinear relationship data between the speed of the high-pressure rotor of the engine and the damage data; wherein the nonlinear relationship data is used to describe the situation that the damage data of the high-pressure turbine blade increases nonlinearly with the increase of the speed of the high-pressure rotor of the engine, and indicates that the speed dimension is used as a specified dimension; According to the positive correlation between engine vibration and root crack growth rate of high-pressure turbine blades, the vibration dimension is taken as a specified dimension; According to the cooling relationship between the engine exhaust temperature and the idling time, the idling time dimension is used as a specified dimension; According to the relationship between the thermal expansion and deformation of the high-pressure turbine blades and the stress concentration position, the aircraft climb-related dimension is taken as a specified dimension.

8. The method according to any one of claims 1 to 6, characterized in that: The life extension management and control measures include at least one of limiting the maximum load of the aircraft, forcibly setting a minimum idle time, adjusting the flight mission of the target aircraft, and performing engine troubleshooting.

9. A life extension system for high pressure turbine blades, characterized in that: The system comprises: A data acquisition module is used to acquire specified dimension feature data of the engine to be tested in multiple specified dimensions; wherein the multiple specified dimensions are feature dimensions that can cause specified types of damage to the high-pressure turbine blades; and each specified dimension corresponds to historical healthy feature data and historical unhealthy feature data; A chart analysis module, for performing a chart similarity analysis on each of the specified dimensions according to the specified dimension feature data, the historical healthy feature data, and the historical unhealthy feature data, to obtain a current chart similarity result on each of the specified dimensions; A distribution analysis module, configured to perform data distribution difference analysis on each designated dimension according to the designated dimension feature data, the historical health feature data, and the historical unhealthy feature data, to obtain current health assessment data on each designated dimension; A reliability analysis module, used for generating 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; wherein the current reliability data is used for describing whether there is a preset safety type crack on the pressure surface area of ​​the high-pressure turbine blade; A measure determination module is used to determine a life extension control measure suitable for the high-pressure turbine blade based on 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, and the target aircraft is an aircraft equipped with the engine to be tested.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 8 by executing the computer instructions.

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