JP2013221788A - Temperature measuring method and temperature measuring device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To highly accurately measure the temperature of a measurement object without calculating a combination solution of emittance and without being affected by fluctuation in emittance.SOLUTION: A regressive creation part 3 analyzes the basis of spectral information used for creating a calibration curve defined by a temperature measured value obtained by measuring a measuring object 5 by using a contact type thermometer 30, calculates a score a(k, j) of the basis, and calculates a multiple regression coefficient (c(k), k=1, 2) from the score a(k, j) and the temperature of the temperature instruction value of the contact type thermometer 30 corresponding to the spectral information for calibration curve creation. A temperature estimation part 4 calculates a score of the basis, based on the spectral information of the measuring object and the basis calculated by the regressive creation part 3, and estimates the temperature of the measuring object, based on the calculated store a(k, j) and the multiple regression coefficient (c(k), k=1, 2). Thus, without calculating a combination solution of emittance, the temperature of the measuring object can be highly accurately measured without being affected by fluctuation in emittance.

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 a temperature, thereby measuring the surface temperature of the measurement object. Measure. 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. That is, this two-wavelength type radiation thermometer measures and sets the ratio of emissivity at two wavelengths in advance, or assumes that the emissivity is equal at two adjacent wavelengths, and the surface of the object to be measured Measure the temperature. However, the emissivity changes according to the state of the measurement object, and when the emissivity of the measurement object changes with time, the temperature measurement error increases. 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. The technology to do is described. Specifically, in these documents, an empirical formula obtained from experimental measurement data or a theoretical formula of spectral emissivity is used to create a kind of calibration curve or satisfy a relational formula (empirical formula, theoretical formula). A technique for dynamically correcting the emissivity by a method of determining such a combined emissivity solution is described.

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, many experiments need to be performed in order to obtain a highly accurate empirical formula, which requires a lot of time and effort. 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 temperature of an object to be measured without being affected by fluctuations in emissivity without calculating a combined emissivity solution. An object of the present invention is to provide a temperature measuring method and a temperature measuring apparatus capable of measuring 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, wherein the measurement of the surface temperature calculates a score of a base spectrum acquired in advance based on spectral spectrum information obtained from the measurement object, and a calibration formula acquired in advance And the base spectrum and the calibration formula are determined according to a temperature measurement value obtained by measuring a measurement object using a contact thermometer.

  The temperature measurement method according to the present invention is the above-described invention, wherein the spectral spectrum information of the object to be measured and the radiant energy spectrum obtained by measuring a black body furnace having the same temperature as the temperature measured by the contact thermometer. An emissivity is calculated from a ratio with the spectrum information, and a spectrum orthogonal to a principal component obtained by performing principal component analysis on an emissivity variation based on the emissivity is determined as the base spectrum.

  Further, the temperature measurement method according to the present invention is characterized in that, in the above invention, the score of one or more principal components obtained by principal component analysis of the spectral spectrum information of the radiant energy of the black body furnace measured in advance for a plurality of temperatures. Then, a relational expression with the temperature of the black body furnace is calculated and reconstructed using the spectral spectrum information of the measurement object and a score corresponding to the temperature measurement value of the contact thermometer determined by the relational expression. The emissivity is calculated from the ratio to the spectral spectrum information obtained, and a spectrum orthogonal to the principal component obtained by principal component analysis of the emissivity variation based on the emissivity is determined as the base spectrum.

  In the temperature measurement method according to the present invention, the base spectrum is obtained by applying a partial least square method to the spectral spectrum information of the measurement object and the temperature measurement value obtained by the contact thermometer. It is characterized by determining.

  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. The surface temperature is measured by calculating a score of a base spectrum acquired in advance based on spectral spectrum information obtained from the measurement object, and using the score according to a calibration formula acquired in advance. The calibration equation is determined in accordance with a temperature measurement value obtained by measuring an object to be measured using a contact-type thermometer.

  Further, the temperature measuring device according to the present invention is the above-described invention, wherein the spectral spectrum information of the object to be measured and the radiant energy spectrum obtained by measuring a black body furnace having the same temperature as the temperature measured by the contact thermometer. An emissivity is calculated from a ratio with spectrum information, and a spectrum orthogonal to a principal component obtained by performing principal component analysis on an emissivity variation based on the emissivity is determined as the base spectrum.

  The temperature measuring device according to the present invention is characterized in that, in the above invention, the score of one or more principal components obtained by principal component analysis of the spectral spectrum information of the radiant energy of the black body furnace measured in advance for a plurality of temperatures. Then, a relational expression with the temperature of the black body furnace is calculated and reconstructed using the spectral spectrum information of the measurement object and a score corresponding to the temperature measurement value of the contact thermometer determined by the relational expression. A spectrum orthogonal to the principal component obtained by calculating an emissivity from a ratio with the spectral information obtained and analyzing the emissivity variation based on the emissivity as a principal component is determined as the base spectrum. .

  In the temperature measurement device according to the present invention, the base spectrum is obtained by applying a partial least square method to the spectral spectrum information of the measurement object and the temperature measurement value obtained by the contact thermometer. Is determined.

  According to the temperature measurement method and the temperature measurement device of the present invention, it is possible to measure the temperature of the measurement object with high accuracy without calculating an emissivity combination solution and without being affected by variations in emissivity. .

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 showing the relationship between the principal component vector of emissivity fluctuation and the principal component vector of radiant energy. FIG. 7 is a diagram illustrating an example of emissivity fluctuation. FIG. 8 is a diagram showing a spectral energy spectrum acquired from the measurement object. FIG. 9 is a diagram showing the relationship between the score of the first 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. 10A is a diagram showing the results of measuring the surface temperature of the measurement object based on the relationship shown in FIG. FIG. 10B is a diagram illustrating a result of measuring the surface temperature of the measurement object using the conventional technique. FIG. 11 is a block diagram showing a configuration of a temperature measuring apparatus according to an embodiment of the present invention. FIG. 12 is a schematic diagram showing an internal configuration of the FTIR shown in FIG. FIG. 13 is a schematic diagram illustrating a configuration of the contact thermometer illustrated in FIG. 11. FIG. 14 is a flowchart showing the flow of regression equation creation processing according to an embodiment of the present invention. FIG. 15 is a flowchart showing a flow of temperature estimation processing according to an embodiment of the present invention. FIG. 16 is a schematic diagram for explaining a configuration of a temperature measuring apparatus according to another embodiment of the present invention. FIG. 17 is a schematic diagram showing the internal configuration of the spectrometer shown in FIG. FIG. 18 is a diagram showing the results of measuring the emissivity of a plurality of steel plates under different temperature conditions. FIG. 19 is a diagram illustrating the relationship between the logarithmic value of the ratio of the measured value of the emissivity to the average value of the emissivity in Example 1 and the logarithm value expressed using the first principal component. FIG. 20A is a diagram showing a measurement error of the surface temperature of the measurement object measured by Example 1 and the prior art. FIG. 20B is a diagram showing a measurement error of the surface temperature of the measurement object measured by Example 1 and the prior art. FIG. 21 is a diagram showing the results of measuring the emissivity of a plurality of steel plates moving at different speeds. FIG. 22A is a diagram illustrating a relationship between a logarithmic value of a ratio of an actually measured emissivity value to an average emissivity value in Example 2 and a logarithmic value expressed using the first principal component. FIG. 22B is a diagram illustrating a relationship between a logarithmic value of a ratio of an actually measured emissivity value to an average emissivity value in Example 2 and a logarithmic value expressed using the first principal component and the second principal component. is there. FIG. 23 shows the first principal component of the emissivity variation, the second principal component of the emissivity variation, and the main radiant energy orthogonal to the first and second principal components obtained in the principal component analysis of Example 2. It is a figure which shows a component. FIG. 24 is a diagram showing a measurement error of the surface temperature of the measurement object measured by Example 2 and the prior art.

[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 spectrum of emissivity 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 shown in the following formula (3). Note that the parameters c ₁ and c ₂ in Equation (3) indicate physical constants. Accordingly, 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 may be estimated regardless of the emissivity spectrum ε (λ). is there. Therefore, as one method for paying attention to the shape of the natural logarithm logL _B (λ, T) of the blackbody radiant energy spectrum, consider performing principal component analysis (basic 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, if the information processing of extracting this essence is applied to the radiant energy spectrum waveform in the surface temperature estimation, the essence can be extracted from the multi-point wavelength information. 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, assuming that spectral data of n wavelengths for seven temperatures is given, seven points on an n-dimensional space are given. Therefore, 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 fits when the seven blackbody radiant energy spectra L _B (λ, T) are reconstructed. Reconstruction constitutes the original spectrum by performing a product-sum operation in which the basis vectors are multiplied and added together, that is, a linear operation is performed. 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 black body radiant energy spectrum L _B (λ, T) does not need to be expressed by wavelength information at N points, that is, by N-dimensional coordinates, and there are two coefficients that are linear sums of two basis vectors. It means that it can be expressed with only two points of information. 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 logarithmically calculating the radiant energy at N = 250 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). The parameter i (= 1 to 250) represents the measurement wavelength number, and the 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 main component (i = 1 to N square root of the sum of squares of each component) is 1. 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 N. 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. Then, the reconstructed example shown in FIG. 5A is obtained by displaying the formula (6) as a state before the logarithmic operation, that is, a value represented by the following formula (7). 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 reconstructed 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 is estimated, instead of using the log value x (i, j) of the radiant energy composed of the data of 250 points, the score a (k, j) which is data of at most 2 points. Means that the quality of the information will not be reduced.

  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).

The above is the basic concept when performing principal component analysis on spectral spectrum data. Here, an application method of principal component analysis is further considered so as not to be affected by the emissivity fluctuation of the measurement object. When there is a change in emissivity, the emissivity is divided into a known emissivity value ε (λ) and an emissivity variation δε (λ) that can change depending on operating conditions and the like, and corresponds to Equation (1). The measured value L (λ, T) can be described as the following formula (10). Here, the parameter ε ₀ (λ) in the equation (10) represents a reference emissivity such as a set value, and the parameter δε (λ) represents a change in emissivity under various conditions.

When the log (natural logarithm) of both sides of the above equation (10) is taken and deformed, the following equation (11) is obtained. In the conventional radiation temperature measurement, it is assumed that the emissivity ε ₀ (λ) at the measurement wavelength is known, and the monochromatic thermometer is represented by the following equation (12), and the two-color thermometer is represented by the following equation (13). The surface temperature is obtained by making the assumption expressed by and solving the equation. However, these assumptions do not hold strictly and temperature errors often occur.

Therefore, here, assuming that the behavior of the emissivity variation of the measurement object is known in advance, a principal component analysis is performed on the emissivity variation data, and the first principal component v (i, 1) of the emissivity variation is calculated. To do. The first principal component v (i, 1) of the emissivity variation represents the statistical behavior of the emissivity variation of the measurement object. In other words, all the vectors orthogonal to the principal component vector of the emissivity variation can be said to be vectors that are not subjected to the emissivity variation. FIG. 6 shows a conceptual diagram of the above description. That is, as shown in FIG. 6, the principal component vector V ₂ of emissivity variation and the principal component vector V ₁ of radiant energy orthogonal to the emissivity variation are not affected by the emissivity variation, and the sensitivity to the temperature of the measurement object is maximized. Become.

  Therefore, by performing the principal component analysis of the radiant energy under the constraint that the first principal component v (i, 1) of the emissivity variation is always orthogonal, the essence of the radiant energy is not affected by the emissivity variation. Information can be extracted. As a specific procedure, as shown in the following formula (14), for a value obtained by removing the first principal component v (i, 1) of the emissivity fluctuation from the radiant energy x (i, j) in advance. To perform principal component analysis. As a result, the required principal components are all orthogonal to the first principal component v (i, 1) of the emissivity fluctuation. In many cases, the first principal component v (i, 1) of the emissivity variation is statistical, and the actual emissivity variation is completely different from the first principal component v (i, 1) of the emissivity variation. It is also possible that there is a shift without matching. However, even in such a case, the principal component obtained using Equation (14) is considered to be substantially orthogonal to the first principal component v (i, 1) of the emissivity fluctuation. It is considered that the conditions that are most resistant to errors are achieved.

  Based on the above concept, an example in which a temperature measurement simulation of a measurement object whose emissivity changes as shown in FIG. 7 will be described. This example is an example in which the following formula (15) is established when the variation in emissivity is a constant multiple, that is, when the parameter K is a constant. Therefore, the main component of the emissivity fluctuation after logarithmic calculation is a so-called DC component in which all wavelength components have the same value. 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. 5A 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., and this is the case with the monochromatic thermometer among the conventional methods. It is difficult to accurately measure a measurement object whose emissivity changes.

  Therefore, after logarithmically calculating these waveforms, a base (in this case, a direct current component) expressing emissivity fluctuations and emissivity fluctuations are obtained with respect to the waveform obtained by subtracting log ε (λ) which is assumed emissivity data. This is expressed using a base (first principal component) that is orthogonal to the base to be expressed and is essential for expressing the blackbody radiant energy spectrum. Then, pay attention to the coefficient multiplied by the first principal component. The reason is that the coefficient corresponding to the first principal component of the black body radiant energy spectrum has essential information for expressing the black body radiant energy spectrum waveform without being affected by the emissivity fluctuation. It is possible.

  Here, focusing on the coefficient of the first principal component after removing the principal component of the emissivity fluctuation, the principal component analysis when the principal component analysis is applied to the ideal black body radiant energy spectrum It can be seen that the relationship between the coefficient (score) and the temperature to be measured is as shown in FIG. Therefore, from the relationship shown in FIG. 9, a calibration curve representing the relationship between the coefficient for the second principal component and the temperature of the measurement object is calculated, and the first calculated from the measured radiant energy spectrum when the emissivity increases or decreases. The temperature was estimated using the coefficient for the principal component. As a result, it was confirmed that even when the emissivity increased or decreased as shown in FIG. 7, it was within the error as shown in FIG. 10A. Note that the error when measuring with a two-color thermometer (in this case, the wavelength 2 μm and the wavelength 4 μm) is the error when the condition where the ratio of emissivity is completely equal as shown in FIG. 10B. Is small, but large when the ratio of emissivity is not necessarily equal and fluctuates.

  Therefore, as the original information for temperature estimation that is the object of the present invention, the principal component analysis is performed on the spectral information consisting of a large number of wavelengths, and the original spectral information is reconstructed with the lower-order principal components. It was confirmed that the information (principal component score) of the coefficient multiplication of the basis vector is effective. In other words, instead of estimating the temperature from the original N-point wavelength data, the N-point wavelength data is dimensionally compressed into two-point data that is a score up to the second principal component, and the two-point information The temperature data is estimated by the usual 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. .

  In addition to the formula (16) shown below for estimating the temperature from the original N-point wavelength data, the formula shown below for estimating the temperature from the two-point data that is the score up to the second principal component. (17) is used to estimate the temperature.

  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. 11 and FIG. 12, the structure of the temperature measuring apparatus which is one Embodiment of this invention is demonstrated.

  FIG. 11 is a block diagram showing a configuration of a temperature measuring apparatus according to an embodiment of the present invention. FIG. 12 is a schematic diagram showing an internal configuration of the FTIR shown in FIG. FIG. 13 is a schematic diagram illustrating a configuration of the contact thermometer illustrated in FIG. 11. As shown in FIG. 11, the temperature measurement apparatus 1 according to an embodiment of the present invention includes an FTIR (Fourier transform infrared spectrophotometer) 2, a contact thermometer 30, an air cylinder 40, a regression equation creation unit 3, and A temperature estimation unit 4 is provided.

  FTIR2 measures the spectral spectrum of the radiant energy from the steel plate 5 which is a measurement object. As shown in FIG. 12, 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.

  The contact-type thermometer 30 measures the temperature of the steel plate 5 by bringing a thermocouple into contact with the steel plate 5 as a measurement object. In a temperature measurement scene in an actual manufacturing process, the temperature is measured for a steel sheet that is being conveyed at a predetermined speed in a furnace such as an annealing furnace. For this reason, in this embodiment, the contact-type thermometer 30 employ | adopts what is generally used as what measures the temperature of a moving body.

  That is, for example, as shown in FIG. 13, the contact-type thermometer 30 has a body 33 placed thereon via a touch roll 31 for stably following the steel plate 5 conveyed at a predetermined speed. A sled-like metal foil 35 attached to 33 is configured to slide on the steel plate 5. A thermosensitive portion 371 of a thermocouple 37 is disposed on the back side of the metal foil 35, and temperature measurement is performed by the thermosensitive portion 371. Here, the body 33 supports the thermocouple 37, holds the heat sensitive part 371 at a predetermined position on the back side of the metal foil 35, and is arranged so as to measure the temperature via the metal foil 35. The contact-type thermometer 30 is configured to be movable in the vertical direction indicated by an arrow in FIG. 13 by driving an air cylinder 40 attached to the body 33. The metal foil 35 is attached to the steel plate 5 at the time of temperature measurement. On the other hand, when the temperature is not measured while being contacted and slid, the plate is separated from the steel plate 5 and retreated upward. The temperature indication value (temperature measurement value) of the contact thermometer 30 is output to the regression equation creation unit 3 as needed.

  The regression equation creation unit 3 and the temperature estimation unit 4 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. 14 and 15, 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. 14, the operation of the temperature measurement device 1 when executing the regression equation creation process will be described.

  FIG. 14 is a flowchart showing the flow of regression equation creation processing according to an embodiment of the present invention. The flowchart shown in FIG. 14 starts at a predetermined timing before the surface temperature of the steel plate 5 is measured, and the regression equation creation process proceeds to step S1. Before executing this regression formula creation process, spectral spectrum information of radiant energy emitted from a black body furnace at various temperatures is measured in advance by FTIR2, and the obtained spectral spectrum information and the black body furnace at that time are measured. Assume that the temperature is associated with the database. In parallel with this regression formula creation process, the contact-type thermometer 30 intermittently contacts the steel plate 5 in the furnace to measure the temperature of the steel plate 5, and simultaneously with this temperature measurement, the FTIR 2 emits the radiation of the steel plate 5. Spectral spectrum information of energy is acquired, and the temperature indication value and spectral spectrum information are input to the regression equation creation unit 3 as needed.

  In the process of step S1, the regression equation creation unit 3 is based on the temperature indication value input from the contact thermometer 30 as described above, and the spectral spectrum information of the radiant energy of the black body furnace associated with the temperature. Is obtained from the aforementioned database and acquired as spectral spectrum information for preparing a calibration curve. Thereby, the process of step S1 is completed and the regression equation creation process proceeds to the process of step S2.

  Here, it is assumed that the temperature indication value input from the contact thermometer 30 varies depending on the contact state between the metal foil 35 and the steel plate 5 when the temperature is measured. For this reason, it is not limited to the case where the temperature indication value input from the contact-type thermometer 30 is used as it is, but for example, a maximum value or an average value of a plurality of temperature indication values measured within a predetermined time is secondarily calculated. A value may be used.

  In addition, when the temperature range assumed as the temperature of the object to be measured is wide, it may be difficult to measure the spectrum information of the black body furnace at all temperatures so as to cover the temperature range. is there. In such a case, the principal component analysis is performed on the spectral spectrum information of the black body furnace measured at several points in the temperature range in advance to calculate a score, and a relational expression between the score and the black body furnace temperature is calculated. You may make it keep. In step S1, a score corresponding to the temperature indicated by the temperature indication value is obtained from the calculated relational expression, and the spectrum information at the temperature indicated by the temperature indication value is obtained using the obtained score, as in FIG. 5B. It may be reconstructed by this method and acquired as spectral spectrum information for creating a calibration curve.

  In the process of step S2, the regression equation creation unit 3 performs FTIR2 when measuring the temperature of the spectral spectrum information for preparing the calibration curve acquired at any time by the process of step S1 and the temperature indication value used to acquire the corresponding spectral spectrum information. The emissivity data is accumulated by performing a ratio calculation with the spectral spectrum information of the radiant energy of the steel plate 5 obtained through the above. Then, logarithmic calculation of emissivity fluctuation data obtained from the accumulated emissivity data is performed. 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. 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 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 calculates the temperature indication value of the contact thermometer 30 corresponding to the score a (k, j) calculated by the process of step S4 and the spectral spectrum information for creating the calibration curve. The multiple regression coefficient c (k) in the multiple regression equation of the mathematical formula (17) is calculated by applying the temperature to the mathematical formula (17). 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.

  Here, the principal component of the emissivity fluctuation is obtained, and the basis that is not affected by the emissivity fluctuation is extracted by obtaining the main component of the radiant energy orthogonal to the main ingredient of the emissivity fluctuation. It is not limited to this. For example, using PLS (partial least squares) or the like, a base having the strongest correlation with the temperature indication value of the contact thermometer 30 is directly obtained based on spectral spectrum information acquired from a steel plate affected by emissivity fluctuation. Alternatively, various mathematical statistical analysis methods can be used for base extraction.

[Temperature estimation processing]
Next, with reference to the flowchart shown in FIG. 15, the operation of the temperature measuring device 1 when executing the temperature estimation creation process will be described.

  FIG. 15 is a flowchart showing a flow of temperature estimation processing according to an embodiment of the present invention. The flowchart shown in FIG. 15 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 measurement object corresponding to the second coefficient according to the present invention 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 equation (17), 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 measurement device 1 according to an embodiment of the present invention, the regression equation creation unit 3 performs base decomposition on the spectral spectrum information for creating a calibration curve, and the base score a (k , J), and multiple regression coefficients (c (k), k = 1, 2) are calculated from the score a (k, j) and the temperature data corresponding to the spectral information for creating the calibration curve. Specifically, at this time, the temperature of the steel plate 5 is measured by the contact thermometer 30 in the annealing furnace used in the actual manufacturing process, and the spectral spectrum information of the radiant energy of the steel plate 5 is acquired simultaneously with this temperature measurement, Based on the temperature indication value of the equation thermometer 30, the spectrum information obtained from the steel plate 5 is used to obtain the base spectrum and calculate the multiple regression coefficient. And the temperature estimation part 4 calculates the score a (k, j) applied to a base based on the spectral-spectrum information of a measurement object, and the base calculated by the regression type preparation part 3, and calculated score a ( k, j) and the multiple regression coefficients (c (k), k = 1, 2) are used to estimate the temperature of the measurement object. Thereby, since the base spectrum and multiple regression coefficient can be obtained according to the temperature of the measurement object measured in advance using a contact-type thermometer or the like, when measuring the temperature of the measurement object, the atmosphere in the furnace, etc. An error inherent to the annealing furnace can be suppressed. Therefore, the temperature of the measurement object can be measured with high accuracy without calculating the emissivity combination solution and without being affected by the change in emissivity.

  In addition, the structure of the temperature measuring apparatus for implement | achieving this invention is not limited to the structure shown in FIG. FIG. 16 is a schematic diagram showing the inside of a configuration of a temperature measuring device 1a according to another embodiment of the present invention and a part of an annealing furnace to which the temperature measuring device 1a is applied. FIG. 17 is a schematic diagram showing the internal configuration of the spectrometer 8 shown in FIG. In FIG. 16, the same components as those in the above-described embodiment are denoted by the same reference numerals.

  This temperature measuring device 1a is for measuring the temperature of a steel plate 5a heated in a furnace such as an annealing furnace in a manufacturing process, and includes a furnace body 9 of the annealing furnace and a heat insulating material 91 on the inner surface of the furnace body 9. Optical fiber 6 inserted into through-hole 93 penetrating, collimating lens 7 installed on one end side located inside the furnace of optical fiber 6, spectrometer 8 connected to the other end outside the furnace of optical fiber 6, return An expression creation unit 3 and a temperature estimation unit 4 are provided. Light (measurement light) radiated substantially in parallel from the steel plate 5 a that is the measurement object enters the spectroscope 8 through the collimating lens 7 and the optical fiber 6.

  Here, the collimator lens 7 at one end and one end of the optical fiber 6 inserted into the through hole 93 is separated from the surroundings by a water-cooled light-shielding tube 95 so that light emitted from the heat insulating material 91 or the like is not mixed into the measurement light. Has been. Further, the interior space of the water-cooled light shielding tube 95 is purged with nitrogen filled through the piping 97 to prevent contamination of the lens portion and the like.

  The spectroscope 8 is realized by, for example, a cross Chelney Turner type spectroscope, and includes a collimating mirror 81, a diffraction grating 82, a focus mirror 83, and a detector 84. In the spectrometer 8, the measurement light incident from the other end of the optical fiber 6 is converted into parallel light by the collimator mirror 81 and then incident on the diffraction grating 82 and split. Then, the split measurement light passes through the focus mirror 83 and is detected by detecting all wavelengths simultaneously by the detector 84. In this example, the steel plate 5a in the furnace is the object to be measured, and a relatively high temperature of about 800 ° C. to 1100 ° C. is measured. For this reason, a one-dimensional array of Si CCD, photodiode array, or the like is used as a detection element of the detector 84, and a relatively short wavelength range, specifically, 0.4 μm to 0.8 μm or 0.4 μm. The wavelength range of ˜1.0 μm is detected.

[Example 1]
FIG. 18 is a diagram showing the results of measuring the emissivity of a plurality of steel plates under different temperature conditions of 800 to 1100 ° C. in a furnace. The emissivity shown in FIG. 18 shows the output (spectral spectrum information) when a black body furnace is measured in advance by changing the temperature and the steel sheet is measured by the temperature measuring device 1a shown in FIG. 16, and a thermocouple (not shown). Is obtained by performing a ratio calculation between the true temperature measured by bringing the steel plate into contact with the steel plate and the spectral spectrum information of the radiant energy of the black body furnace at the same temperature.

  The average value of the emissivity is calculated from the data shown in FIG. 18, and the logarithm of the ratio of the measured value of the emissivity to the average value of the emissivity (the change in emissivity) is calculated (thin line; log δε in Equation (11)) FIG. 19 shows (λ)) and the first principal component (thick line). As can be seen from FIG. 19, there are portions that do not coincide with each other in detail, but the variation in emissivity can be expressed by the first principal component almost statistically. Then, the principal component analysis of the radiant energy is performed under the constraint orthogonal to the first principal component of the emissivity variation, and the temperature is measured by calculating the score for the principal component (first principal component). 20A and 20B. As is clear from FIGS. 20A and 20B, according to the temperature measurement device of one embodiment of the present invention, the temperature of the measurement object is not affected by the variation in emissivity compared to the two-color thermometer. It was confirmed that can be measured with high accuracy.

[Example 2]
FIG. 21 is a diagram showing the results of measuring the emissivity of a plurality of steel plates conveyed in the furnace at different speeds with the furnace temperature kept constant at 860 ° C. This assumes a case where the moving speed (conveying speed) of the steel sheet in the furnace changes. When the moving speed of the steel plate changes, the heating time in the furnace changes. As a result, since the oxide film thickness changes, the emissivity also varies as shown in FIG.

  The average value of the emissivity was calculated from the data shown in FIG. 21, the logarithm of the ratio of the measured value of the emissivity to the average value of the emissivity (emissivity variation) was calculated, and the principal component analysis was performed. FIG. 22A shows a representative example of the comparison between the obtained logarithm (thin line) and the expression obtained by using only the first principal component (thick line). FIG. 22B shows a representative example of the comparison between the obtained logarithm (thin line) and the expression (thick line) expressed using the first principal component and the second principal component (thick line). In Example 2, when the change in emissivity was expressed by only the first main component, as shown in FIG. On the other hand, when the second principal component was used in addition to the first principal component, as shown in FIG.

  Therefore, in Example 2, the principal component analysis of the radiant energy was performed under the constraint of being orthogonal to the first principal component of the emissivity variation and orthogonal to the second principal component. The first principal component of the emissivity variation, the second principal component of the emissivity variation, and the main component of the radiant energy orthogonal to the emissivity variation (first and second principal components) obtained by this principal component analysis. It shows in FIG. And the temperature measurement was performed by calculating the score with respect to the main component (1st main component) of the radiant energy.

  FIG. 24 shows a temperature error when measuring a steel plate of 860 ° C. by performing principal component analysis using the second principal component as described above, and the same 860 using a monochromatic radiation thermometer with a center wavelength of 0.9 μm. The comparison with the temperature error when measuring a steel plate of ℃ is shown. As shown in FIG. 24, when the temperature error was measured using the proposed method of Example 2 at a maximum of 1.1 ° C., the temperature was measured using a monochromatic thermometer. The temperature error was 5.1 ° C. The standard deviation was 0.4 ° C. in the proposed method of Example 2 and 1.7 ° C. when a monochromatic thermometer was used. As is apparent from the results shown in FIG. 24, according to the temperature measurement device of one embodiment of the present invention, the temperature of the measurement object is not affected by the variation in emissivity compared to a monochromatic thermometer. It was confirmed that can be measured with high accuracy.

  For comparison, without using the second principal component of emissivity variation, only the first principal component of emissivity variation is used as in Example 1, and the main component of radiant energy orthogonal to this is calculated. Was used to measure the temperature. As a result, the temperature error was 2.8 ° C. at maximum and the standard deviation was 0.6 ° C. As described above, in the application situation of Example 2, the accuracy is higher than the temperature measurement using the monochromatic thermometer even when only the first principal component is used, but the emissivity fluctuation is better when the second principal component is used. It was confirmed that the temperature of the measurement object can be measured with high accuracy without being affected by the above.

  Although the embodiment to which the invention made by the present inventors has been described has been described above, the present invention is not limited by the description and the drawings, which form part of the disclosure of the present invention according to this embodiment. That is, other embodiments, examples, operational techniques, and the like made by those skilled in the art based on the present embodiment are all included in the scope of the present invention.

1,1a Temperature measuring device 2 FTIR (Fourier transform infrared spectrophotometer)
DESCRIPTION OF SYMBOLS 3 Regression formula preparation part 4 Temperature estimation part 5,5a Steel plate 11, 14, 15, 16 Mirror 12 Half mirror 13 Movable mirror 17 Detector 18 Interferometer 30 Contact type thermometer 35 Metal foil 37 Thermocouple 40 Air cylinder 6 Optical fiber 7 Collimating Lens 8 Spectrometer 81 Collimating Mirror 82 Diffraction Grating 83 Focus Mirror 84 Detector

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.
The measurement of the surface temperature is performed by calculating a score of a base spectrum acquired in advance based on spectral spectrum information obtained from the measurement object, and using the score according to a calibration formula acquired in advance.
The temperature measurement method characterized by determining the said base spectrum and the said calibration formula according to the temperature measurement value which measured the measuring object using the contact-type thermometer.   The emissivity is calculated from the ratio between the spectral information of the measurement object and the temperature measured by the contact thermometer and the spectral information of the radiant energy obtained by measuring the black body furnace at the same temperature. The temperature measurement method according to claim 1, wherein a spectrum orthogonal to a principal component obtained by performing principal component analysis on an emissivity variation based on the spectrum is determined as the base spectrum. Calculate a relational expression between one or more principal component scores obtained by performing principal component analysis on the spectral energy information of the radiant energy of the black body furnace measured in advance for a plurality of temperatures and the temperature of the black body furnace. Every
The emissivity is calculated from the ratio between the spectral information of the measurement object and the spectral information reconstructed using the score corresponding to the temperature measurement value of the contact thermometer determined by the relational expression. The temperature measurement method according to claim 2, wherein a spectrum orthogonal to a principal component obtained by performing principal component analysis on emissivity fluctuations based on is determined as the base spectrum.   2. The temperature according to claim 1, wherein the base spectrum is determined by applying a partial least square method to spectral information of an object to be measured and a temperature measurement value obtained by the contact thermometer. Measuring method. 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,
The measurement of the surface temperature is performed by calculating a score of a base spectrum acquired in advance based on spectral spectrum information obtained from the measurement object, and using the score according to a calibration formula acquired in advance.
The temperature measurement apparatus, wherein the base spectrum and the calibration equation are determined according to a temperature measurement value obtained by measuring an object to be measured using a contact thermometer.   The emissivity is calculated from the ratio between the spectral information of the measurement object and the temperature measured by the contact thermometer and the spectral information of the radiant energy obtained by measuring the black body furnace at the same temperature. The temperature measurement apparatus according to claim 5, wherein a spectrum orthogonal to a principal component obtained by performing principal component analysis on emissivity fluctuations based on is determined as the base spectrum. Calculate a relational expression between one or more principal component scores obtained by performing principal component analysis on the spectral energy information of the radiant energy of the black body furnace measured in advance for a plurality of temperatures and the temperature of the black body furnace. Every
The emissivity is calculated from the ratio between the spectral information of the measurement object and the spectral information reconstructed using the score corresponding to the temperature measurement value of the contact thermometer determined by the relational expression. The temperature measurement apparatus according to claim 6, wherein a spectrum orthogonal to a main component obtained by performing a principal component analysis on an emissivity variation based on the base is determined as the base spectrum.   6. The base spectrum is determined by applying a partial least square method to spectral spectrum information of an object to be measured and a temperature measurement value obtained by the contact thermometer. Temperature measuring device.