Temperature history analysis system and method

Abstract

To provide a technology capable of evaluating a temperature history at any point of a surface of a high-temperature member exposed to heat.SOLUTION: A temperature history analysis system includes an analysis device configured to acquire analysis data including an X-ray two-dimensional diffraction image at an evaluation point on a target surface exposed to heat to estimate a temperature history at the evaluation point from the X-ray two-dimensional diffraction image at the evaluation point on the basis of a relation between a temperature history obtained from an X-ray two-dimensional diffraction image of a reference sample composed of the same material as the target surface and having a known temperature history, and the X-ray two-dimensional diffraction image.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a technique for analyzing the temperature history of the surface of a high-temperature component exposed to heat. [Background technology]

[0002] Conventionally, there have been known methods for measuring the temperature of high-temperature components in a high-temperature range in order to measure the temperature of the environment in which high-temperature materials are used. Such methods include a method using a thermocouple embedded in the high-temperature component, and a method using a radiation thermometer that utilizes the intensity of infrared rays or visible light emitted from the high-temperature component. The measured temperature is used to evaluate the operating state of equipment equipped with the high-temperature component, the state of corrosion of the high-temperature component, and the like.

[0003] For example, Patent Document 1 discloses a method in which a radiation thermometer is installed in the turbine casing of a gas turbine, and the radiation thermometer detects the surface temperatures at multiple points on a moving blade while the gas turbine is operating, thereby generating a blade surface temperature pattern (i.e., the distribution of the blade surface temperature), and determining whether or not an abnormality has occurred by comparing the blade surface temperature pattern with a surface temperature pattern at the time of a known abnormality. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 9-53463 Summary of the Invention [Problem to be solved by the invention]

[0005] When measuring the temperature of a high-temperature component in a high-temperature range using a radiation thermometer as in Patent Document 1, there are restrictions on the placement of the radiation thermometer, making it difficult to measure the temperature of any desired location of the high-temperature component.

[0006] A high-temperature material that has been subjected to heat may have a temperature history, and it is possible to estimate the environmental temperature at which such a high-temperature material is used from the temperature history that the material of the high-temperature component has been subjected to.

[0007] The present disclosure has been made in consideration of the above circumstances, and has an object to propose a technique capable of evaluating the temperature history of any location on the surface of a high-temperature component exposed to heat. [Means for solving the problem]

[0008] In order to solve the above problem, a temperature history analysis system according to one embodiment of the present disclosure includes: The method includes an analysis device configured to acquire analytical data including a two-dimensional X-ray diffraction image of an evaluation point on a target surface exposed to heat, and estimate the temperature history of the evaluation point from the two-dimensional X-ray diffraction image of the evaluation point based on the relationship between the temperature history obtained from the two-dimensional X-ray diffraction image of a reference sample made of the same material as the target surface and having a known temperature history, and the two-dimensional X-ray diffraction image of the evaluation point.

[0009] In order to solve the above problems, a temperature history analysis method according to one embodiment of the present disclosure includes: 1. A method for analyzing a temperature history of a surface of a subject exposed to heat using a computer, comprising: acquiring analysis data including a two-dimensional X-ray diffraction image of the evaluation point on the target surface; And, and estimating the temperature history of the evaluation point from the two-dimensional X-ray diffraction image of the evaluation point based on the relationship between the temperature history obtained from the two-dimensional X-ray diffraction image of a reference sample made of the same material as the target surface and having a known temperature history and the two-dimensional X-ray diffraction image. Effect of the Invention

[0010] According to the present disclosure, a technique can be provided that can evaluate the temperature history of any location on the surface of a high-temperature component exposed to heat. [Brief description of the drawings]

[0011] [Figure 1]FIG. 1 is a diagram showing a schematic configuration of a temperature history analysis system according to an embodiment of the present disclosure. [Diagram 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of a computer. [Diagram 3] FIG. 3 is a diagram showing a process flow of the temperature evaluation model. [Figure 4] FIG. 4 is a flow chart for creating a temperature evaluation model. [Diagram 5] FIG. 5 shows an example of a two-dimensional X-ray diffraction image of a reference sample. [Figure 6] Figure 6 is a flow chart of the temperature history analysis. [Figure 7] FIG. 7 is an example of a temperature history distribution image of the evaluation sample. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Next, an embodiment of the present disclosure will be described with reference to the drawings. FIG. 1 is a diagram showing a schematic configuration of a temperature history analysis system 1 according to one embodiment of the present disclosure. The temperature history analysis system 1 shown in FIG. 1 analyzes the temperature history of a target surface, and obtains information related to the highest heat temperature received by the target surface from the temperature history. The target surface is a surface of a high-temperature component after being exposed to heat. A non-limiting example of a high-temperature component is the surface of a high-temperature part of a gas turbine. Examples of high-temperature parts of a gas turbine are a turbine blade, a turbine shroud, and a combustor. The temperature history of the surface of a high-temperature material obtained by the temperature history analysis system 1 can be used to estimate the temperature of the environment in which the high-temperature component is used, diagnose the soundness of the high-temperature component, and the like.

[0013] The target surface according to the present embodiment is a ceramic spray coating having heat resistance formed by spraying a ceramic spray material on a ceramic substrate. The spraying process is a surface treatment technique in which the spray material is collided with the substrate in a molten or semi-molten state to form a coating by laminating the spray material on the surface of the substrate. The coating formed by the spraying process tends to have fine crystal grains because it is quenched during the film formation process, and there may be amorphous regions before exposure to heat, and it is relatively easy to capture the process of crystallization due to exposure to heat with an X-ray diffraction image. The composition formula of the material of the ceramic spray coating is expressed as RE2Si2O7 or RE2SiO5. Here, RE is one or a mixture of two or more of the rare earth elements of Lu, Yb, Tm, Er, Ho, Dy, Gd, Tb, Eu, Sm, Pm, Nd, Pr, Ce, La, Y, and Sc. It has been confirmed that thermal spray coatings made of materials with the composition formula RE2Si2O7 and RE2SiO5 are amorphous after thermal spraying, but crystallize when subsequently subjected to temperature history, and therefore have the characteristic of being prone to changes in the two-dimensional X-ray diffraction pattern.

[0014] However, the target surface to which the temperature history analysis system 1 according to the present disclosure is applied is not limited to the above. For example, the sprayed coating is not limited to the ceramic sprayed coating, and may be a metal sprayed coating. Examples of the metal sprayed coating include coatings made of heat-resistant metal materials such as nickel-based, cobalt-based, and iron-based materials that impart heat resistance to the substrate. In addition, the ceramic sprayed coating is not limited to the above, and examples of the ceramic sprayed coating include coatings made of ceramic materials such as alumina, titanium oxide, chromium oxide, zirconia, and yttria that impart heat resistance to the substrate and at the same time provide a heat shielding effect. Furthermore, the target surface is not limited to the coating, and may be the surface of a ceramic or metal base material.

[0015] [Configuration of temperature history analysis system 1] The temperature history analysis system 1 includes an X-ray diffraction device 10, an analysis device 20, and a learning device 51. The analysis device 20 and the learning device 51 may be configured on a single piece of hardware.

[0016] X-ray Diffraction Equipment 10 The X-ray diffraction apparatus 10 may be a known X-ray diffraction apparatus capable of generating a two-dimensional diffraction image. A typical X-ray diffraction apparatus includes a sample stage for holding a sample, an X-ray irradiator for generating X-rays and irradiating a measurement range of the sample with the X-rays, and a two-dimensional X-ray detector for measuring the intensity of the diffracted X-rays emitted from the sample.

[0017] The X-ray diffraction apparatus 10 performs X-ray diffraction measurements to obtain two-dimensional diffraction images that reflect the crystal structure of the sample in the measurement area. The two-dimensional diffraction image of a single crystal region shows a geometric image consisting of spot-like bright spots. The two-dimensional diffraction image of a polycrystalline region shows a clear ring-shaped diffraction image, i.e., multiple concentric rings called Debye-Scherrer rings. The two-dimensional diffraction image of an amorphous region shows a broad halo called a halo ring.

[0018] 《Analysis device 20》 The analysis device 20 analyzes the temperature history of the target surface using a two-dimensional diffraction image of the target surface acquired from the X-ray diffraction device 10. The analysis device 20 can be realized by a computer 40 including, for example, a CPU (Central Processing Unit) 41, a memory 42, an auxiliary storage device 43 such as a HDD (Hard Disk Drive), a communication I / F 44 for connecting to a communication network by wire or wirelessly, an input device 45 such as a mouse, a keyboard, a touch sensor, or a touch panel, an output device 46 such as a liquid crystal display, and a media I / F 47 for reading and writing information from and to a portable storage medium, as shown in Fig. 2. Note that Fig. 2 is a diagram showing an example of the hardware configuration of the computer 40.

[0019] Each function of the analysis device 20 can be realized by loading a predetermined program stored in the auxiliary storage device 43 into the memory 42 and executing it with the CPU 41. Communication between the analysis device 20 and the X-ray diffraction device 10 and the learning device 51 can be realized, for example, by the CPU 41 using the communication I / F 44. Note that the above-mentioned predetermined program may be downloaded from a network via the communication I / F 44, or may be loaded from a storage medium connected to the media I / F 47.

[0020] Learning Device 51 The learning device 51 generates a temperature evaluation model 50 that the analysis device 20 uses for analysis. The learning device 51 can further train the generated temperature evaluation model 50. Like the analysis device 20, the learning device 51 can be realized by a computer 40 as shown in FIG. 2. Each function of the learning device 51 can be realized by loading a predetermined program stored in the auxiliary storage device 43 into the memory 42 and executing it by the CPU 41.

[0021] 3, the temperature evaluation model 50 generated by the learning device 51 is a machine-learned model configured to output a temperature history when a two-dimensional diffraction image is input. The learning device 51 acquires a two-dimensional diffraction image of a reference sample from the X-ray diffraction device 10, and generates the temperature evaluation model 50 by machine learning the relationship between the two-dimensional diffraction image of the reference sample and the temperature of the heat treatment of the reference sample.

[0022] [Example] Here, a temperature history analysis method using the temperature history analysis system 1 will be described in detail along with an embodiment. The evaluation sample according to this embodiment is a replica of a high-temperature component, and is a ceramic substrate on which a Yb2Si2O7 (ytterbium silicate) sprayed coating is formed. The target surface is the sprayed coating of the evaluation sample. Prior to analysis of the temperature history of the target surface, a temperature evaluation model 50 to be used in the analysis is created.

[0023] 《Creating Temperature Evaluation Model 50》 4 is a flow chart showing the creation of the temperature evaluation model 50. As shown in FIG. 4, the process of creating the temperature evaluation model 50 includes the following steps 1. to 3.

[0024] 1. Preparation of reference sample (Step S1) The target surfaces of the reference sample and the evaluation sample are made of the same material. First, a Yb2Si2O7 sprayed coating is formed on the surface of the ceramic substrate by spraying Yb2Si2O7 powder onto the ceramic substrate, and then the ceramic substrate is removed to produce a Yb2Si2O7 freestanding film. The Yb2Si2O7 freestanding film is subjected to heat treatment by being held in an air atmosphere at a predetermined temperature for a predetermined time. This heat treatment temperature is the temperature of the heat to which the reference sample is exposed. In this embodiment, the heat treatment time is 5 minutes. In this embodiment, the heat treatment temperatures are 1100, 1200, 1300, 1400, 1500, 1600, and 1700°C. However, the heat treatment temperature is not limited to this embodiment and is set based on the material of the target surface and the expected temperature history. The heat-treated Yb2Si2O7 freestanding film becomes the reference sample.

[0025] 2. Two-dimensional X-ray diffraction measurement of the reference sample (Step S2) The X-ray diffraction apparatus 10 performs two-dimensional X-ray diffraction measurement of the reference sample to generate a two-dimensional diffraction image of the reference sample. In this embodiment, an X-ray irradiator that irradiates the measurement range with X-rays of energy 10 keV is used, and the X-ray incidence angle θ on the target surface is set to 12.5 degrees. In this embodiment, a two-dimensional semiconductor detector (PILATUS 100K manufactured by DECTRIS) equipped with a light receiving element with a module size of 83.8 mm × 33.5 mm is used as the X-ray detector, the diffraction angle 2θ is set to 25 degrees, and the distance between the X-ray detector and the diffraction center of the target surface is set to 200 mm. However, the settings of the X-ray diffraction apparatus 10 are adjusted according to the material and shape of the target surface so that a two-dimensional diffraction image suitable for analysis is obtained.

[0026] For each reference sample, multiple measurements are performed at different measurement positions to obtain multiple 2D diffraction images for each reference sample. In this way, 2D diffraction images of the reference samples heat-treated at seven different temperatures, 1100, 1200, 1300, 1400, 1500, 1600, and 1700°C, are obtained.

[0027] FIG. 5 is an example of an X-ray two-dimensional diffraction image of a reference sample. The examples of two-dimensional diffraction images shown in FIG. 5 are two-dimensional diffraction images of reference samples heat-treated at temperatures of 1100, 1200, 1300, 1400, 1500, 1600, and 1700°C. When a thermal spray coating is exposed to heat, phase transformation and grain growth occur according to the heat temperature, which are reflected in the two-dimensional diffraction image. That is, the difference in the heat temperature to which the reference sample is exposed appears as a difference in the two-dimensional diffraction image of the reference sample. The difference in the two-dimensional diffraction image due to the exposure temperature can be seen in features such as the position where the Debye-Scherrer ring appears, the continuous / discontinuous state of the curve forming the ring, and the width of the curve forming the ring. Based on these features, the similarity of the two-dimensional diffraction images can be evaluated.

[0028] 3. Generation or training of the temperature evaluation model 50 (step S3) The learning device 51 acquires raw data, which are the two-dimensional diffraction image of the reference sample, which is input data (i.e., explanatory variables), and the known temperature history of the reference sample (i.e., the heat treatment temperature, which is also the maximum temperature in the temperature history), which is output data (i.e., objective variables), and preprocesses the raw data to create learning data. The preprocessing includes at least one of various processes such as converting the data format, checking for anomalies, extracting data, and changing variable names or file names. The learning data includes a large number of input data and output data datasets with different temperature histories.

[0029] The learning device 51 uses the learning data to train the AI ​​program and generate a trained model incorporating trained parameters. The trained model is defined as a "function expressed as a combination of an AI program and parameters (weights)." The learning device 51 according to this embodiment executes supervised learning. In general, supervised learning is a method of learning a correlation model for estimating required output data for new input data by using a combination of known input data and corresponding output data (i.e., correct answer data) as learning data and identifying features that imply a correlation between the input data and the output data from a large amount of learning data, and by learning a large amount of learning data, the correlation between the input data and the output data can gradually approach an optimal solution. For example, the learning device 51 applies the learning data to a neural network, which is a type of AI program, to learn and store parameters that are the relationship between the input data and the output data, that is, the strength of the connection between each neuron. The parameters include the weights of the synapses of the neural network. In this way, a trained model consisting of a combination of the structure of the neural network and the parameters, that is, the temperature evaluation model 50, is generated.

[0030] Although the learning device 51 according to the present embodiment employs a neural network as a machine learning algorithm and uses an AI program based on a neural network, the machine learning algorithm is not limited to this. For example, the machine learning algorithm may be any one of multiple regression analysis, decision tree, random forest regression, gradient boosting, kernel regression, and support vector regression.

[0031] <Temperature history analysis of evaluation samples> The analysis device 20 generates a temperature history distribution of the evaluation sample using the temperature evaluation model 50 generated as described above. Fig. 6 is a flow chart of the temperature history analysis.

[0032] The evaluation sample was prepared by forming a Yb2Si2O7 sprayed coating on a ceramic substrate measuring 15 mm x 15 mm x 3 mm by thermal spraying, and then heating the coating by applying a burner flame to the center of the coating. The sprayed coating of the evaluation sample was the target surface for the temperature history analysis. As shown in Figure 6, the process of temperature history analysis of the target surface includes the following steps i. to iii.

[0033] i. X-ray two-dimensional diffraction measurement of the target surface (step S11) An X-ray diffraction measurement of the evaluation sample is performed by the X-ray diffraction device 10, and a two-dimensional diffraction image of the evaluation sample is generated. The settings of the X-ray diffraction device 10 are the same as those of the X-ray two-dimensional diffraction measurement of the reference sample. 13 points x 13 points = 169 evaluation points are set on the surface of the evaluation sample at a pitch of 1 mm vertically and horizontally, X-ray diffraction measurement is performed for each evaluation point, and a two-dimensional diffraction image of each evaluation point is generated.

[0034] ii. Calculating the historical temperature of the target surface (step S12) The analysis device 20 acquires analysis data for each evaluation point of the evaluation sample. The analysis data includes the coordinates of the evaluation point and a two-dimensional diffraction image of the evaluation point. Information on the two-dimensional diffraction image is associated with the coordinates of the evaluation point. The analysis device 20 inputs the two-dimensional diffraction image for each evaluation point into a temperature evaluation model 50 generated by a learning device 51 to obtain an output of a temperature history. The temperature evaluation model 50 is, for example, a regression model that uses the two-dimensional diffraction image as an explanatory variable and estimates a temperature history as a continuous value. In this way, the analysis device 20 uses the temperature evaluation model 50 to calculate an estimated temperature history for each evaluation point.

[0035] iii. Evaluation of the temperature history of the evaluation sample (step S13) The analysis device 20 combines the coordinates of the evaluation points with the calculated temperature history to generate a temperature history distribution image as a temperature history evaluation result. FIG. 7 shows an example of a temperature history distribution image of the evaluation sample. The temperature history distribution image shown in FIG. 7 is a map having coordinate axes corresponding to the surface of the evaluation sample, in which the temperature history of the evaluation point (i.e., the maximum temperature of heat received by the evaluation point) is mapped to the coordinates of the evaluation point with a different color for each temperature. According to the temperature history distribution image of the evaluation sample, the temperature is highest at the center where the burner flame hits, and the temperature decreases with distance from the center. Thus, it can be seen that according to this embodiment, the temperature history of the evaluation sample can be evaluated with an accuracy of ±50°C.

[0036] In the above embodiment, the temperature history distribution of one clustered region of the evaluation sample was evaluated, but in the temperature history analysis system 1, an evaluation point can be set at any point on the target surface within a range where X-ray diffraction measurement is possible with the X-ray diffraction device 10, and the temperature history of the evaluation point can be obtained. For example, a large number of evaluation points may be set continuously over the entire target surface, a plurality of evaluation points may be set in a dispersed manner on the target surface, or a single evaluation point may be set at any point on the target surface.

[0037] [Summary] The temperature history analysis system 1 according to the first aspect of the present disclosure includes: The apparatus includes an analysis device 20 configured to acquire analytical data including a two-dimensional X-ray diffraction image of an evaluation point on a target surface exposed to heat, and estimate the temperature history of the evaluation point from the two-dimensional X-ray diffraction image of the evaluation point based on the relationship between the temperature history obtained from the two-dimensional X-ray diffraction image of a reference sample made of the same material as the target surface and having a known temperature history, and the two-dimensional X-ray diffraction image.

[0038] Depending on the temperature of the heat to which the target surface is exposed, a phase transformation occurs in the material of the target surface, and the growth of crystal grains differs. Therefore, the difference in the temperature of the heat to which the multiple samples are exposed appears as a difference in the two-dimensional X-ray diffraction images of the multiple samples. Conversely, if the temperatures of the heat to which the multiple samples are exposed are the same or close, a common feature appears in the two-dimensional X-ray diffraction images of the multiple samples. By utilizing this, the above-mentioned temperature history analysis system 1 clarifies the causal relationship between the temperature history and the two-dimensional X-ray diffraction image from the two-dimensional X-ray diffraction image of a reference sample having a known temperature history, and estimates the temperature history of the evaluation point from the two-dimensional X-ray diffraction image of the evaluation point based on the relationship between the temperature history and the two-dimensional X-ray diffraction image. Here, the temperature history is the maximum temperature of the heat received by the evaluation point. In the above-mentioned temperature history analysis system 1, when analyzing the temperature history of a high-temperature component, the position and number of the evaluation points are not limited as long as the surface of the high-temperature component can be measured by X-ray diffraction, and the temperature history of any point on the surface of the high-temperature component can be evaluated. The temperature history of the surface of the high-temperature component can be used to estimate the temperature of the environment in which the high-temperature component is used and to diagnose the soundness of the high-temperature component.

[0039] The temperature history analysis system 1 relating to the second item of the present disclosure is the temperature history analysis system 1 relating to the first item, in which the analysis device 20 has a trained model 50 that has been machine-learned to output the temperature history of the evaluation point when a two-dimensional X-ray diffraction image of the evaluation point is input.

[0040] The temperature history analysis system 1 relating to the third item of the present disclosure is the temperature history analysis system 1 relating to the second item, in which the trained model 50 is machine-trained using training data consisting of a combination of input data including a two-dimensional X-ray diffraction image of a reference sample and output data including a known temperature history of the reference sample.

[0041] According to the temperature history analysis system 1 relating to the second and third items, it is possible to perform a process of determining whether multiple two-dimensional X-ray diffraction images are similar / dissimilar using artificial intelligence (AI), and it is possible to determine the temperature history of the evaluation point with higher accuracy.

[0042] The temperature history analysis system 1 relating to the fourth item of the present disclosure is the temperature history analysis system 1 relating to the second or third item, and is equipped with a learning device 51 configured to acquire a two-dimensional X-ray diffraction image of a reference sample having a known temperature history, and generate a trained model 50 that has been machine-learned using training data consisting of a combination of input data including the two-dimensional X-ray diffraction image of the reference sample and output data including the known temperature history of the reference sample.

[0043] According to the above-described temperature history analysis system 1, a trained model 50 corresponding to the target surface is generated, and can be used to analyze the temperature history.

[0044] The temperature history analysis system 1 relating to the fifth item of the present disclosure is a temperature history analysis system 1 relating to any one of the first to fourth items, in which a plurality of evaluation points are set on the target surface, the analysis data includes coordinates of the evaluation points and two-dimensional X-ray diffraction images of the evaluation points, and the analysis device 20 outputs a temperature history distribution image of the target surface in which the temperature histories of the plurality of evaluation points are mapped based on the coordinates of the evaluation points.

[0045] According to the temperature history analysis system 1 described above, the temperature history of the target surface can be easily understood by using the temperature history distribution image.

[0046] The temperature history analysis system 1 according to a sixth item of the present disclosure is a temperature history analysis system 1 according to any one of the first to fifth items, which is equipped with an X-ray diffraction device 10 that measures a two-dimensional X-ray diffraction image of an evaluation point.

[0047] According to the above-described temperature history analysis system 1, it is possible to carry out X-ray diffraction measurement and analysis of a two-dimensional X-ray diffraction image consecutively.

[0048] A temperature history analysis system 1 according to a seventh item of the present disclosure is the temperature history analysis system 1 according to any one of the first to sixth items, in which the target surface is a thermal spray coating made of a metal material or a ceramic material.

[0049] Since thermal spray coatings tend to have fine crystal grains because they are rapidly cooled during the coating process, and amorphous regions may exist before exposure to heat, it is relatively easy to capture the process of crystallization caused by exposure to heat using X-ray diffraction. Therefore, the above-mentioned temperature history analysis system 1 can analyze the temperature history of the target surface with relatively high accuracy.

[0050] A temperature history analysis system 1 according to an eighth aspect of the present disclosure is the temperature history analysis system 1 according to any one of the first to seventh aspects, in which the target surface is a surface of a high-temperature component of a gas turbine.

[0051] The above-described temperature history analysis system 1 is suitable for interpreting the temperature history of high-temperature parts, such as turbine blades of a gas turbine, that are exposed to high heat.

[0052] A temperature history analysis method according to a ninth aspect of the present disclosure is a method for analyzing a temperature history of a target surface exposed to heat by a computer 40, comprising the steps of: Obtaining analytical data including a two-dimensional X-ray diffraction image of an evaluation point on a target surface; and and estimating the temperature history of the evaluation point from the two-dimensional X-ray diffraction image of the evaluation point based on the relationship between the temperature history obtained from the two-dimensional X-ray diffraction image of a reference sample made of the same material as the target surface and having a known temperature history and the two-dimensional X-ray diffraction image.

[0053] According to the above-mentioned temperature history analysis method, when analyzing the temperature history of a high-temperature component, the position and number of evaluation points are not limited as long as the surface of the high-temperature component can be measured by X-ray diffraction, and the temperature history of any point on the surface of the high-temperature component can be evaluated. The temperature history of the surface of the high-temperature component can be used to estimate the temperature of the environment in which the high-temperature component is used and to diagnose the soundness of the high-temperature component.

[0054] The functions performed by the analysis device 20 and the learning device 51 described herein may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), CPUs (Central Processing Units), conventional circuits, and / or combinations thereof, programmed to perform the described functions. Processors include transistors and other circuits and are considered to be circuitry or processing circuitry. A processor may be a programmed processor that executes a program stored in a memory. In this specification, a circuitry, unit, or means is hardware that is programmed to perform or executes the described functions. The hardware may be any hardware disclosed in this specification or any hardware that is programmed to perform or is known to perform the described functions. If the hardware is a processor, which is considered to be a type of circuitry, the circuitry, means, or unit is a combination of hardware and software used to configure the hardware and / or processor.

[0055] The above discussion of the present disclosure has been presented for purposes of illustration and description, and is not intended to limit the present disclosure to the form disclosed herein. For example, in the above detailed description, various features of the present disclosure are grouped together in one embodiment for the purpose of streamlining the present disclosure, but some of the features may be combined. In addition, the features included in the present disclosure may be combined into alternative embodiments, configurations, or aspects other than those discussed above. [Explanation of symbols]

[0056] 1: Temperature history analysis system 10: X-ray diffraction equipment 20:Analysis device 40: Computer 50: Temperature evaluation model (trained model) 51: Learning device

Claims

1. an analysis device configured to acquire analysis data including a two-dimensional X-ray diffraction image of an evaluation point on a target surface exposed to heat, and estimate a temperature history of the evaluation point from the two-dimensional X-ray diffraction image of the evaluation point based on a relationship between a temperature history obtained from a two-dimensional X-ray diffraction image of a reference sample made of the same material as the target surface and having a known temperature history, Temperature history analysis system.

2. The analysis device has a trained model that has been machine-learned to output a temperature history of the evaluation point when an X-ray two-dimensional diffraction image of the evaluation point is input. The temperature history analysis system according to claim 1 .

3. The trained model is machine-trained using training data consisting of a combination of input data including an X-ray two-dimensional diffraction image of the reference sample and output data including the known temperature history of the reference sample. The temperature history analysis system according to claim 2 .

4. a learning device configured to acquire an X-ray two-dimensional diffraction image of the reference sample having the known temperature history, and generate the trained model machine-learned using training data consisting of a combination of input data including the X-ray two-dimensional diffraction image of the reference sample and output data including the known temperature history of the reference sample; The temperature history analysis system according to claim 2 .

5. A plurality of the evaluation points are set on the surface of the target, and the analysis data includes coordinates of the evaluation points and two-dimensional X-ray diffraction images of the evaluation points; the analysis device outputs a temperature history distribution image of the target surface in which the temperature histories of the plurality of evaluation points are mapped based on the coordinates of the evaluation points. The temperature history analysis system according to claim 1 .

6. An X-ray diffraction apparatus for measuring a two-dimensional X-ray diffraction image of the evaluation point is provided. The temperature history analysis system according to claim 1 .

7. The target surface is a thermal spray coating made of a metal material or a ceramic material. The temperature history analysis system according to claim 1 .

8. The target surface is a surface of a hot component of a gas turbine. The temperature history analysis system according to claim 1 .

9. 1. A method for analyzing a temperature history of a surface of a subject exposed to heat using a computer, comprising: acquiring analysis data including a two-dimensional X-ray diffraction image of the evaluation point on the target surface; And, and estimating the temperature history of the evaluation point from the two-dimensional X-ray diffraction image of the evaluation point based on a relationship between a temperature history obtained from a two-dimensional X-ray diffraction image of a reference sample made of the same material as the target surface and having a known temperature history and the two-dimensional X-ray diffraction image. Temperature history analysis method.

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