JP2014169935A - Temperature measuring method, and temperature measuring apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To highly accurately measure a surface temperature of a measuring object without calculating a combination solution of emissivity and without being influenced by fluctuation in the emissivity.SOLUTION: A temperature measuring apparatus 1 performs spectrometric measurement of radiation energy emitted from a measuring object (steel plate 5) and performs signal-processing of obtained optical spectrum information to measure a surface temperature of the measuring object. In the temperature measuring apparatus 1, an FTIR (Fourier Transform Infrared) spectrometer 2 measures the optical spectrum information of the radiation energy emitted from the measuring object. A regression equation creation part 3 and a temperature estimation part 4 perform base decomposition of the optical spectrum information of the measuring object and calculate the surface temperature of the measuring object by using a coefficient to be multiplied to the obtained base.

Description

  The present invention relates to a temperature measurement method and a temperature measurement apparatus that spectroscopically measure radiant energy emitted from a measurement object and measure the surface temperature of the measurement object by performing signal processing on the obtained spectrum information.

  There are various techniques for measuring the temperature of an object to be measured. Among them, the radiation temperature measurement technique is a technique for measuring the surface temperature of the measurement object in a non-contact manner using the radiated light from the measurement object, and is practically used as a radiation thermometer. The radiation thermometer includes a photoelectric conversion element and an optical filter, measures the radiant energy of the measurement object in a predetermined wavelength band, and converts the measured radiant energy value into temperature, thereby determining the surface temperature of the measurement object. taking measurement. Such radiation thermometers include monochromatic radiation thermometers that measure radiant energy at a single wavelength, two-wavelength radiation thermometers that measure radiant energy at two wavelengths (two-color radiation thermometers), and many more wavelengths. There is a multi-wavelength radiation thermometer (multi-color radiation thermometer) that measures radiant energy.

  Since the radiant energy of the measurement object is the value obtained by multiplying the radiant energy from the ideal black body by the emissivity of the measurement object, when measuring the surface temperature of the measurement object using a radiation thermometer, Requires the value of the emissivity of the object to be measured. For this reason, in the monochromatic radiation thermometer, the emissivity of the measurement object is measured in advance, and the surface temperature of the measurement object is measured using the emissivity measured in advance. Patent Document 1 discloses that both the emissivity and the surface temperature of the measurement object are obtained by measuring the radiant energy of the measurement object while changing the contribution ratio of the radiant energy emitted from the radiation source to the measurement object. Techniques for measuring are disclosed.

  On the other hand, as a radiation thermometer having no radiation source as described above, there are the following two-wavelength radiation thermometers. In other words, this two-wavelength type radiation thermometer pre-measures and sets the ratio of emissivity at two wavelengths, or assumes that the emissivities are the same at two adjacent wavelengths, and then the object to be measured Measure the surface temperature of the object. However, since the emissivity changes according to the state of the measurement object, the temperature measurement error becomes large especially when the emissivity of the measurement object changes with time. For this reason, in the two-wavelength radiation thermometer and the multi-wavelength radiation thermometer, various proposals have been made to reduce the temperature measurement error.

  Specifically, in Patent Documents 2 to 4 and Non-Patent Document 1, the emissivity of the measurement object is dynamically corrected, and the surface temperature of the measurement object is measured using the dynamically corrected emissivity. Techniques to do this are disclosed. In detail, in the techniques of these documents, a kind of calibration curve is made using an empirical formula obtained from experimental measurement data or a theoretical formula of spectral emissivity, or a relational formula (experimental formula, theoretical formula) is created. The emissivity is dynamically corrected by a method of determining a combined emissivity solution that satisfies the above.

JP-A-2-245624 Japanese Patent Publication No. 3-4855 Japanese Patent Laid-Open No. 2-85730 JP-A-2-238333

J.L.gardner, T.P.Jones and R.Davis, "A six-wavelength radiation pyrometer", High Temp-High pressure, vol.13, No.5, p.459-466 (1981)

  In the prior art, the combined emissivity solution is determined by giving the initial value of the emissivity having a small error to the actual value of the emissivity to the relational expression (empirical formula, theoretical formula) and performing the calculation repeatedly. Therefore, the calculation accuracy determines the measurement accuracy of the radiation thermometer. For this reason, in order to construct a radiation thermometer with high measurement accuracy, it is necessary to use a highly accurate relational expression. However, in particular, when an empirical formula is used as a relational expression, it is necessary to perform many experiments in order to obtain a highly accurate empirical formula, which requires a lot of time and labor. In addition, in order to obtain a combined solution of emissivity, software and hardware are required. Especially, since iterative calculations are time-consuming operations, software and hardware capable of high-speed processing are required. In addition, when the emissivity changes with time, there is no guarantee that repeated calculations will always converge to the actual value of emissivity.

  As described above, the prior art has a problem caused by determining the combined emissivity solution by calculation. Therefore, it has been expected to provide a technique capable of measuring the surface temperature of the measurement object with high accuracy without calculating the emissivity combination solution and without being affected by the change in emissivity.

  The present invention has been made to solve the above-described problems, and its purpose is to calculate the surface temperature of the measurement object without being influenced by fluctuations in emissivity without calculating a combined solution of emissivities. Is to provide a temperature measuring method and a temperature measuring apparatus capable of measuring the temperature with high accuracy.

  In order to solve the above-described problems and achieve the object, a temperature measurement method according to the present invention performs spectroscopic measurement of radiant energy emitted from a measurement object, and performs signal processing on the obtained spectral spectrum information to measure the measurement object. A temperature measurement method for measuring a surface temperature, a measurement step for measuring spectral spectrum information of radiant energy emitted from a measurement object, and a base decomposition of the spectral spectrum information of the measurement object. And a temperature estimation step of calculating a surface temperature of the measurement object using a coefficient multiplied by.

  The temperature measurement method according to the present invention is characterized in that, in the above-mentioned invention, the temperature estimation step base-decomposes spectral spectrum information of the measurement object into two or more bases.

  In the temperature measurement method according to the present invention as set forth in the invention described above, the temperature estimation step performs the basis decomposition using principal component analysis.

  The temperature measurement method according to the present invention is characterized in that, in the above invention, the temperature estimation step calculates a surface temperature of the measurement object using a coefficient multiplied by a main component after the second main component. To do.

  The temperature measuring device according to the present invention is a temperature measuring device that spectroscopically measures radiant energy emitted from a measuring object and measures the surface temperature of the measuring object by performing signal processing on the obtained spectral spectrum information. A measurement means for measuring spectral spectrum information of radiant energy emitted from the measurement object; and a base decomposition of the spectral spectrum information of the measurement object, and the measurement object using a coefficient multiplied by the basis-resolved base Temperature estimation means for calculating the surface temperature of the liquid crystal.

  In the temperature measurement apparatus according to the present invention as set forth in the invention described above, the temperature estimation means base decomposes spectral information of the measurement object into two or more bases.

  In the temperature measuring apparatus according to the present invention as set forth in the invention described above, the temperature estimating means performs the basis decomposition using principal component analysis.

  Moreover, the temperature measuring device according to the present invention is characterized in that, in the above invention, the temperature estimating means calculates a surface temperature of the measurement object using a coefficient multiplied by a main component after the second main component. To do.

  According to the temperature measuring method and the temperature measuring apparatus according to the present invention, it is possible to measure the surface temperature of the measurement object with high accuracy without calculating the combined emissivity solution and without being affected by the variation of the emissivity. it can.

FIG. 1 is a scatter diagram showing the relationship between the height and weight of members of a certain group. FIG. 2 is a diagram illustrating the relationship between multi-point wavelength information and the first principal component. FIG. 3A is a diagram showing a black body radiant energy spectrum with respect to seven stages of temperature. FIG. 3B is a diagram illustrating a result of logarithmic calculation performed on the blackbody radiant energy spectrum illustrated in FIG. 3A. FIG. 4 is a diagram illustrating the first principal component and the second principal component obtained by performing the principal component analysis on the logarithm calculation value of the radiant energy illustrated in FIG. 3B. FIG. 5A is a diagram illustrating a reconstruction example of a black body radiant energy spectrum using the first principal component. FIG. 5B is a diagram illustrating a reconstruction example of a black body radiant energy spectrum using the first principal component and the second principal component. FIG. 6 is a diagram illustrating an example of emissivity fluctuation. FIG. 7 is a diagram showing a spectral energy spectrum acquired from the measurement object. FIG. 8 is a diagram showing the relationship between the score of the second principal component obtained by executing the principal component analysis on the ideal black body radiant energy spectrum and the temperature of the measurement object. FIG. 9A is a diagram showing the results of measuring the surface temperature of the measurement object based on the relationship shown in FIG. FIG. 9B is a diagram illustrating a result of measuring the surface temperature of the measurement object using the conventional technique. FIG. 10 is a block diagram showing a configuration of a temperature measuring apparatus according to an embodiment of the present invention. FIG. 11 is a schematic diagram showing the internal configuration of the FTIR shown in FIG. FIG. 12 is a flowchart showing the flow of regression equation creation processing according to an embodiment of the present invention. FIG. 13 is a flowchart showing the flow of temperature estimation processing according to an embodiment of the present invention.

[Concept of the present invention]
In the measurement of the surface temperature of the measurement object using the radiant energy of the measurement object, the spectrum of the emissivity preliminarily assumed in the black body radiant energy spectrum L _B (λ, T) as shown in the following formula (1). A measurement value L (λ, T) multiplied by ε (λ) is measured. Note that the parameter λ in the formula (1) indicates the measurement wavelength of the radiant energy, and the parameter T indicates the surface temperature of the measurement object.

Here, when the log (natural logarithm) of both sides of the formula (1) is taken and deformed, the following formula (2) is obtained. Therefore, the natural logarithm logL _B (λ, T) of the blackbody radiant energy spectrum is estimated by substituting the measured value L (λ, T) and the emissivity spectrum ε (λ) into the right side of Equation (2). A value can be calculated. Here, the reason for expressing it as “estimated value” is because it is not known whether the spectrum ε (λ) of emissivity assumed in advance is accurate. That is, when the emissivity spectrum ε (λ) deviates from the assumed value, the calculated natural logarithm logL _B (λ, T) of the black body radiant energy spectrum is not a correct value.

However, the black body radiant energy spectrum L _B (λ, T) is originally expressed by Planck's radiation law expressed by the following formula (3). Note that the parameters c ₁ and c ₂ in Equation (3) indicate physical constants. Therefore, even if the natural logarithm logL _B (λ, T) of the black body radiant energy spectrum includes an error due to the emissivity spectrum ε (λ), the natural logarithm logL of the black body radiant energy spectrum that can be originally taken. _Since the shape of _B (λ, T) is determined, the true shape of the natural logarithm logL _B (λ, T) of the blackbody radiant energy spectrum is estimated regardless of the error caused by the emissivity spectrum ε (λ). There is a possibility. Therefore, as a technique for paying attention to the shape of the natural logarithm logL _B (λ, T) of the blackbody radiant energy spectrum, consider principal component analysis as an example and consider performing base decomposition.

First, a general principal component analysis method will be described with reference to FIG. Figure 1 is a scatter diagram showing the relationship between the height X ₁ and weight X ₂ members of a population. In general, since the person is large stature X ₁ is said to weight X ₂ is heavy, scatter diagram shown in FIG. 1 has a distribution of right-up. Line L ₁ of the inserted upper right in FIG. 1 is a line passing through the center of the distribution, so to speak represent a measure of "body size". The principal component analysis method means that the essential interpretation of the combination data (two-dimensional information) of the height X ₁ and the weight X ₂ is represented by a one-dimensional scale “body size t ₁ ”. This is a statistically derived method. Mathematically, this “body size” is the first principal component, and the essential information next to the first principal component orthogonal to the first principal component is the second principal component. In the example shown in FIG. 1, the second principal component can be physically referred to as a scale (line segment L ₂ ) of “obesity level t ₂ ”.

  In the example shown in FIG. 1, the original two-dimensional information (height and weight) is reduced to one-dimensional information “body size” by principal component analysis. Therefore, the essence can be extracted from wavelength information of multiple points by applying the information processing of extracting the essence to the radiant energy spectrum waveform in the surface temperature estimation. In this case, the multi-point wavelength information is expressed as one point on the space having the same number of dimensions as the number of measurement wavelengths as shown in FIG. For example, if spectral data of n wavelengths (n = 250) for seven temperatures are given, seven points on an n-dimensional space (250-dimensional space) are given. Accordingly, considering the spread of the distribution of these seven points in the n-dimensional space, the direction with the largest spread becomes the direction of the first principal component, which distinguishes the aforementioned seven points, that is, most distinguishes the seven temperatures. It will be a powerful clue.

Here, logL _B (λ, which is the result of logarithmic operation on the black body radiant energy spectrum L _B (λ, T) (obtained by measuring the black body furnace) for the seven temperatures shown in FIG. 3A. , T) (FIG. 3B) shows the first principal component and the second principal component obtained when the principal component analysis is performed. The first principal component shown in FIG. 4 is a spectrum waveform most representative of the natural logarithm logL _B (λ, T) of the black body radiant energy spectrum for seven temperatures. The reason why the black body radiant energy spectrum L _B (λ, T) is logarithmically calculated is that the black body radiant energy spectrum L _B (λ, T) is multiplied when the surface temperature of the measurement object is actually measured. This is to separate the emissivity ε (λ) that affects in the form of addition of log ε (λ).

Next, the second principal component is a vector space orthogonal to the first principal component, in which the second largest variation of seven points is extracted, which is shown in FIG. Intuitively, the first principal component looks like a basis expressing average energy that increases with temperature, and the second principal component looks like a basis for expressing a fine shape. Thereafter, the main components after the third main component can be obtained in the same manner. These low-order principal component information is essential spectral information (base spectrum) of the original seven blackbody radiant energy spectra logL _B (λ, T).

In order to verify that these low-order principal component information is indeed essential spectral information (basic spectrum) of the original seven blackbody radiant energy spectra L _B (λ, T), FIG. 5A and FIG. 5B show the fit when the seven blackbody radiant energy spectra L _B (λ, T) are reconstructed. Reconstruction is to construct the original spectrum by performing a product-sum operation in which the basis vectors are multiplied and added, that is, a linear operation. Depending on how much low-order basis vector information is included in the original seven blackbody radiant energy spectra L _B (λ, T), the degree of fit at the time of reconstruction changes. FIG. 5A shows the result of reconstruction with only the first principal component, and FIG. 5B shows the result of reconstruction with the second principal component.

As is clear from FIG. 5B, it can be seen that by using up to the second principal component, all of the seven blackbody radiant energy spectra L _B (λ, T) are reconstructed very well. This is because each blackbody radiant energy spectrum L _B (λ, T) does not need to be expressed by wavelength information of N points (N = 250), that is, by coordinates of N dimensions (250 dimensions). It means that it can be expressed only by information of two points corresponding to two coefficients, ie, a linear sum of basis vectors. In other words, it can be said that N-dimensional data is compressed into two-dimensional data. At this time, although the number of dimensions is greatly compressed, it is important that it is reconstructed in an essential spectral form called a “basis vector”, and it is affected by disturbances such as the emissivity fluctuation described above. It is assumed that it is difficult.

  The logarithmic calculation result shown in FIG. 3B will be supplemented in the form of a mathematical formula. The logarithmic calculation result shown in FIG. 3B is obtained by performing logarithmic calculation on the radiant energy at N points in the wavelength direction (horizontal axis) measured every 0.32 μm within the wavelength range of 2 to 10 μm, for example. Here, the log value of the radiant energy is expressed as x (i, j). Parameter i (= 1 to 250) represents the measurement wavelength number, and parameter j (= 1 to 7) represents the temperature number. It is assumed that the temperature for the parameter j is y (j). Further, a principal component vector obtained as a result of performing the principal component analysis on the log value x (i, j) of the radiant energy is defined as w (i, k). The description of how to determine the principal component vector w (i, k) is left to the general literature of principal component analysis. However, in brief, the variation for the parameter j in the following formula (4) is maximized. The first principal component w (i, 1) is determined so that the variation with respect to the parameter j in the following formula (5) among vectors orthogonal to the first principal component w (i, 1) is maximized. The second principal component w (i, 2) is determined.

  The size of each principal component (i = 2 square root of the sum of squares of each component) is 1. The parameter k represents the main component number. In the principal component vector w (i, k), the parameter i can be considered in the range of 1 to 250, and the parameter k can be mathematically considered in the range of 1 to 7. In this example, in the range of k = 1, 2. Think. Generally, the case where the parameter k is smaller (low-order principal component) represents the essence of the log value x (i, j) of the radiant energy, but the method of selecting the range of the parameter k is particularly limited in the present invention. do not do. A value obtained by reconstructing the original radiant energy data using only the first principal component w (i, 1) is expressed by the following formula (6).

  The parameter a (k, j) in the equation (6) is a constant (scalar) mathematically called a principal component score or score. The state before the logarithmic calculation is performed on the mathematical formula (6), that is, the value represented by the mathematical formula (7) shown below is the reconstruction example shown in FIG. 5A. Note that e in Equation (7) represents the base of the natural logarithm.

  Similarly, a value obtained by adding the second principal component w (i, 2) to the first principal component w (i, 1) and reconstructing the original radiant energy data is expressed by the following formula (8). Similarly, the reconfiguration example shown in FIG. 5B is obtained by displaying Equation (8) in a state before logarithmic calculation. The log value x (i, j) of the original radiant energy can be reproduced with the radiant energy data reconstructed using the second principal component w (i, 2). This is because, when the actual temperature y (j) is estimated, instead of using the log value x (i, j) of the radiant energy composed of 250 points of data, the score a () which is data of at most 2 points. This means that the quality of information does not deteriorate even if k, j) is used.

  The score a (k, j) is derived by calculating the inner product of the principal component vector w (i, k) and the log value x (i, j) of the original radiant energy. It is derived by the following formula (9).

  Based on the above concept, an example in which a temperature measurement simulation of a measurement object whose emissivity changes as shown in FIG. 6 will be described. When the emissivity changes as described above, the measured radiant energy is measured as a value obtained by multiplying the black body radiant energy as shown in FIG. 3A by the emissivity. A spectral energy spectrum measured corresponding to each emissivity at 800 ° C. is shown in FIG. As is clear from the figure, when the emissivity is low at 800 ° C., the waveform is similar to that when the emissivity is as expected at 750 ° C. In the conventional method, the emissivity changes in this way. It is difficult to accurately measure the measuring object.

  Therefore, after logarithmically calculating these waveforms, an essential basis for expressing the blackbody radiant energy spectrum (the first principal component and the first principal component and the waveform obtained by subtracting log ε (λ) which is the assumed emissivity data) is obtained. The second principal component) is used for the expression. Then, pay attention to the coefficient multiplied to the base. This is because the coefficients corresponding to the first principal component and the second principal component are considered to have essential information in order to express the blackbody radiation energy spectrum waveform. However, since the present invention assumes a phenomenon of increase / decrease in emissivity, the first principal component considered to represent the average amount of energy is likely to be affected by increase / decrease in emissivity. Conceivable. Conversely, the second principal component that is orthogonal to the first principal component is unlikely to be affected by the increase or decrease in emissivity.

  Therefore, here, attention is paid to the coefficient of the second principal component. Then, when the principal component analysis is applied to the ideal black body radiant energy spectrum, the relationship between the coefficient (score) for the second principal component and the temperature of the measurement object is as shown in FIG. all right. For this reason, a calibration curve representing the relationship between the coefficient for the second principal component and the temperature of the object to be measured is calculated from the relationship shown in FIG. 8, and the second calculated from the measured radiant energy spectrum when the emissivity increases or decreases. The surface temperature of the measurement object was estimated using the coefficient for the two main components. As a result, it was confirmed that even when the emissivity was increased or decreased as shown in FIG. 6, it was within the error as shown in FIG. 9A. In addition, it was confirmed that the error when measuring at a single wavelength (in this case, a wavelength of 2 μm) is large as shown in FIG. 9B. Therefore, it is more desirable to calculate the calibration curve using a coefficient corresponding to the second principal component or a coefficient corresponding to the second principal component and the subsequent components.

  As described above, as the original information for temperature estimation, which is the object of the present invention, principal component analysis is performed on spectral information consisting of a large number of wavelengths, and the original spectral information is reconstructed with lower-order principal components. In this case, it is confirmed that the information (principal component score) of the coefficient multiplication of the basis vector is effective. In other words, in light of the previous example, instead of estimating the temperature from the original (original) N-point (250 points) wavelength data, the N-point wavelength data is the two main component scores up to the second principal component. The data is dimensionally compressed, and temperature data is estimated from the two-point information by a normal multiple regression method. This is because, as shown in FIGS. 5A and 5B, considering that the N-point wavelength data can be sufficiently reproduced from the two-point information, the two-point information contains sufficient information for estimating the temperature. .

  Supplemented by the formula, the temperature is estimated from the two-point data that is the principal component score up to the second principal component instead of the following equation (10) that estimates the temperature from the original N-point wavelength data. The temperature is estimated using Equation (11) shown.

  Hereinafter, a temperature measuring device and a temperature measuring method thereof according to an embodiment of the present invention conceived based on the concept of the present invention will be described in detail with reference to the drawings.

[Configuration of temperature measurement device]
First, with reference to FIG. 10 and FIG. 11, a configuration of a temperature measurement device according to an embodiment of the present invention will be described.

  FIG. 10 is a block diagram showing a configuration of a temperature measuring apparatus according to an embodiment of the present invention. FIG. 11 is a schematic diagram showing the internal configuration of the FTIR shown in FIG. As shown in FIG. 10, the temperature measurement device 1 according to an embodiment of the present invention includes an FTIR (Fourier transform infrared spectrophotometer) 2, a regression equation creation unit 3, and a temperature estimation unit 4.

  FTIR2 measures the spectrum of the radiant energy from the steel plate 5 which is a measuring object as a measuring means. As shown in FIG. 11, the FTIR 2 includes a mirror 11, a half mirror 12, a movable mirror 13, a mirror 14, mirrors 15 and 16, and a detector 17, and the mirror 11, the half mirror 12, the movable mirror 13, and the mirror 14. ˜16 constitute an interferometer 18. The emitted light emitted from the steel plate 5 is guided to the interferometer 18, and the detector 17 measures the amount of light emitted from the interferometer 18.

  At this time, the spectral spectrum information of the radiant energy from the steel plate 5 is obtained by Fourier-transforming the signal of the detector 17 measured in time series while moving the movable mirror 13 of the interferometer 18. In this case, it takes time to move the movable mirror 13 to obtain one piece of spectral spectrum information, but there is no problem if the temperature fluctuation during that time is sufficiently small. In addition to this, various methods such as a method using a diffraction grating and a method using a wavelength selection filter are conceivable as methods for measuring a spectral spectrum, but any method may be used. As for the wavelength range, as is the case with general radiation thermometers, it is possible to use shorter wavelength ranges, such as the visible range and the near-infrared range, for high-temperature objects. It is.

  The regression equation creation unit 3 and the temperature estimation unit 4 as temperature estimation means are configured by an information processing device such as a microcomputer. The regression formula creation unit 3 calculates basic data (base spectrum and multiple regression coefficient) used when the temperature estimation unit 4 estimates the surface temperature of the steel sheet 5 by executing a regression formula creation process described later. The temperature estimation unit 4 measures the surface temperature of the steel plate 5 using the basic data calculated by the regression equation creation unit 3 by executing a temperature estimation process described later.

  The temperature measuring device 1 having such a configuration estimates the surface temperature of the steel plate 5 by executing the regression equation creation process and the temperature estimation process described below. Hereinafter, with reference to the flowcharts shown in FIGS. 13 and 14, the operation of the temperature measurement apparatus 1 when executing the regression equation creation process and the temperature estimation process will be described.

[Regression formula creation process]
First, with reference to the flowchart shown in FIG. 12, the operation of the temperature measurement device 1 when executing the regression equation creation process will be described.

  FIG. 12 is a flowchart showing the flow of regression equation creation processing according to an embodiment of the present invention. The flowchart shown in FIG. 12 starts at a predetermined timing in the adjustment process before measuring the surface temperature of the steel plate 5, and the regression equation creation process proceeds to step S1. In addition, when performing this regression equation creation process, the steel plate 5 shown in FIG. 10 shall be replaced with a blackbody furnace.

  In the process of step S1, the regression equation creation unit 3 acquires the spectral spectrum information of the radiant energy emitted from the black body furnace via the FTIR 2 as spectral spectrum information for creating a calibration curve. Thereby, the process of step S1 is completed and the regression equation creation process proceeds to the process of step S2.

  In the process of step S2, the regression equation creation unit 3 performs a logarithmic calculation between the spectral spectrum information for creating a calibration curve acquired by the process of step S1 and the emissivity fluctuation data of the steel sheet stored in advance. Here, the emissivity fluctuation data includes, for example, the spectrum of the radiant energy of the heated sample under various conditions and the spectrum of the radiant energy of the black body furnace set at the true temperature obtained from the thermocouple attached to the heated sample. It is obtained from the accumulated emissivity data by performing the ratio calculation. Thereby, the process of step S2 is completed and the regression equation creation process proceeds to the process of step S3.

  In the process of step S3, the regression equation creation unit 3 performs principal component analysis on the emissivity fluctuation data calculated by the process of step S2. If the main component of the emissivity variation can be assumed to be a so-called DC component in which the values of almost all components are approximately equal, the emissivity variation data may be determined as a DC component without using the emissivity variation data. Further, the regression equation creation unit 3 performs principal component analysis on the radiant energy spectrum of the black body furnace similarly calculated by the process of step S2 under the condition orthogonal to the principal component of the emissivity fluctuation data. Run. Thereby, the process of step S3 is completed and the regression equation creation process proceeds to the process of step S4.

  In the process of step S4, the regression equation creation unit 3 extracts the principal component to be used as a basis from the result of the principal component analysis obtained by the process of step S3. In addition, the regression equation creation unit 3 calculates the score a (k, j) of each base spectrum corresponding to the coefficient according to the present invention using the above-described equation (9). Thereby, the process of step S4 is completed, and the regression equation creation process proceeds to the process of step S5.

  In the process of step S5, the regression equation creation unit 3 uses the score a (k, j) calculated by the process of step S4 and the temperature of the blackbody furnace corresponding to the spectral spectrum information for creating the calibration curve as described above. By applying the equation (11), the multiple regression coefficient c (k) in the multiple regression equation of the aforementioned equation (11) is calculated. Then, the regression equation creation unit 3 uses the data of the basis spectrum (principal components w (i, k), k = 1, 2) and multiple regression coefficients (c (k), k = 1, 2) as the basic data. Output to the estimation unit 4. Thereby, the process of step S5 is completed and a series of regression equation creation processes are completed.

[Temperature estimation processing]
Next, with reference to the flowchart shown in FIG. 13, the operation of the temperature measurement device 1 when executing the temperature estimation creation process will be described.

  FIG. 13 is a flowchart showing the flow of temperature estimation processing according to an embodiment of the present invention. The flowchart shown in FIG. 13 starts at a predetermined timing after the regression equation creation process is completed, and the temperature estimation process proceeds to the process of step S11.

  In the process of step S11, the temperature estimation part 4 acquires the spectral spectrum information of the radiant energy from the steel plate 5 via FTIR2. Thereby, the process of step S11 is completed and the temperature estimation process proceeds to the process of step S12.

  In the process of step S12, the temperature estimation part 4 performs a logarithmic calculation process with respect to the spectral information acquired by the process of step S11, and assumes from a logarithm calculation value using Formula (2) mentioned above. The logarithmic value of the emissivity spectrum ε (λ) is subtracted. Thereby, the process of step S12 is completed and the temperature estimation process proceeds to the process of step S13.

  In the process of step S13, the temperature estimation unit 4 uses the subtraction process result x (i, j) of step S12 and the basis spectrum (principal component w (i, k), k = 1, By substituting 2) into the above-described equation (9), the score a (k, j) of the base spectrum of the measurement object is calculated. Thereby, the process of step S13 is completed and the temperature estimation process proceeds to the process of step S14.

  In the process of step S14, the temperature estimation unit 4 calculates the score a (k, j) calculated by the process of step S13 and the multiple regression coefficient (c (k), k = 1, The regression calculation is performed by applying 2) to the above-described mathematical expression (11), and the surface temperature of the steel plate 5 is estimated. Thereby, the process of step S14 is completed and a series of temperature estimation processes are complete | finished.

  As is clear from the above description, in the temperature measuring apparatus 1 according to an embodiment of the present invention, the radiation phenomenon due to the temperature change is detected based on the spectral spectrum information of the radiant energy from the steel plate 5 acquired through the FTIR 2. Acquired using the principal components essential for expression. As a result, the temperature of the measurement object can be measured with high accuracy without calculating the emissivity combination solution and without being affected by variations in emissivity.

1 Temperature measuring device 2 FTIR (Fourier transform infrared spectrophotometer)
DESCRIPTION OF SYMBOLS 3 Regression formula preparation part 4 Temperature estimation part 5 Steel plate 11, 14, 15, 16 Mirror 12 Half mirror 13 Movable mirror 17 Detector 18 Interferometer

Claims (8)

A temperature measurement method for measuring the surface temperature of a measurement object by spectrally measuring the radiant energy emitted from the measurement object and processing the obtained spectral spectrum information.
A measurement step of measuring spectral spectrum information of radiant energy emitted from the measurement object;
A temperature estimation step of performing base decomposition of spectral spectrum information of the measurement object and calculating a surface temperature of the measurement object using a coefficient multiplied by the basis-resolved base;
A temperature measuring method comprising:   The temperature measurement method according to claim 1, wherein the temperature estimation step base-decomposes spectral information of the measurement object into two or more bases.   The temperature measuring method according to claim 1, wherein the temperature estimating step performs the basis decomposition using principal component analysis.   The temperature measuring method according to claim 3, wherein the temperature estimating step calculates a surface temperature of the measurement object using a coefficient multiplied by a main component after the second main component. A temperature measurement device that performs spectroscopic measurement of radiant energy emitted from a measurement object, and measures the surface temperature of the measurement object by processing the obtained spectral spectrum information as a signal,
A measuring means for measuring spectral spectrum information of radiant energy emitted from the measurement object;
Temperature estimation means for performing base decomposition of spectral spectrum information of the measurement object, and calculating a surface temperature of the measurement object using a coefficient multiplied by the basis-resolved base;
A temperature measuring device comprising:   The temperature measuring apparatus according to claim 5, wherein the temperature estimation unit performs base decomposition of spectral information of the measurement object into two or more bases.   The temperature measuring apparatus according to claim 5 or 6, wherein the temperature estimation unit performs the basis decomposition using principal component analysis.   The temperature measuring device according to claim 7, wherein the temperature estimating unit calculates a surface temperature of the measurement object using a coefficient multiplied by a main component after the second main component.

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