Temperature sensor abnormality determination device, temperature sensor abnormality determination method, and temperature sensor abnormality determination program

Abstract

To predict abnormality of a temperature sensor using measurement values of a temperature sensor.SOLUTION: A temperature sensor abnormality determination device 1 for determining abnormalities of temperature sensors A1 (A2, B1, and B2) measuring temperatures in an atmosphere of a measurement object comprises: a data accumulation unit 2 which accumulates measurement values of a plurality of latest samples every prescribed time of the temperature sensor A1; a detection unit 3 which detects whether measurement values of the temperature sensors A1 (A2, B1, and B2) accumulated in the data accumulation unit 2 follow a normal distribution; and a sensor warning determination unit 4 which determines whether the temperature sensors A1 (A2, B1, and B2) are a temperature sensor which warns an abnormality on the basis of the detection results of the detection unit 3.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a temperature sensor abnormality determination device, a temperature sensor abnormality determination method, and a temperature sensor abnormality determination program for determining abnormalities in a plurality of temperature sensors that measure temperatures at substantially the same location on a measurement object. [Background technology]

[0002] For example, in a heating furnace as a measurement target, temperature sensors such as thermocouples and resistance thermometers are used to control the heater temperature. In a heating furnace, the temperature inside the furnace is high, and the temperature sensors used to control the heater temperature deteriorate over time, reducing the accuracy of the detection results and making it impossible to accurately grasp the temperature inside the furnace, and the temperature sensor must be replaced.

[0003] Therefore, techniques disclosed in the following Patent Documents 1 and 2 are known as methods for determining when to replace a temperature sensor. In Patent Document 1, the resistance value is measured from the voltage drop when a current is supplied to the temperature sensor from a current source, and the point of deterioration is predicted from the change in the measured resistance value over time, thereby notifying the user when to replace the temperature sensor. In Patent Document 2, the number of excessive temperature events experienced by the temperature sensor, the duration of the excessive temperature events, the temperature between the excessive temperature events, etc. are accumulated in the memory of the process variable transmitter or the memory of the temperature sensor, and the temperature sensor is monitored to notify the user when to replace the temperature sensor. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 10-232170 [Patent Document 2] Special Publication No. 2015-522160 Summary of the Invention [Problem to be solved by the invention]

[0005] However, there is a problem that a dedicated device and parameter management are required for a method that measures resistance values to predict the lifespan of a temperature sensor, as in Patent Document 1. Also, there is a problem that a dedicated parameter is required to manage the temperature sensor, as in Patent Document 2, which counts the number of times the temperature sensor is used and indicates when it is time to replace it.

[0006] Therefore, the present invention has been made in consideration of the above problems, and aims to provide a temperature sensor abnormality determination device, a temperature sensor abnormality determination method, and a temperature sensor abnormality determination program that can predict temperature sensor abnormalities using the temperature sensor measurement values. [Means for solving the problem]

[0008] To achieve the above objectives, Claims of the invention 1 The temperature sensor abnormality determination device described in is a temperature sensor abnormality determination device that determines an abnormality in a temperature sensor that measures the temperature in the atmosphere of a measurement object, a data storage unit that stores the latest multiple sample measurements of the temperature sensor at predetermined time intervals; a Shapiro-Wilk tester that calculates a sum of squares of samples of the temperature sensor measurement values stored in the data accumulator, sorts the temperature sensor measurement values stored in the data accumulator in ascending order, calculates a test statistic from the sum of squares, and tests whether the temperature sensor measurement values are normally distributed by comparing the test statistic with a comparison reference value based on the number of sample measurements and a coefficient used in the Shapiro-Wilk test; and a sensor warning determiner that determines whether the temperature sensor is a temperature sensor that issues an abnormality warning based on the test result of the Shapiro-Wilk tester.

[0010] Claims of the invention 2 The temperature sensor abnormality determination method described in is a temperature sensor abnormality determination method using a temperature sensor abnormality determination device that determines an abnormality in a temperature sensor that measures the temperature in the atmosphere of a measurement object, storing the latest multiple sampled measurement values of the temperature sensor at predetermined time intervals using a data storage device included in the temperature sensor abnormality determination device; a step of calculating the sum of squares of the sampled measured values of the temperature sensor stored in the data accumulator by a Shapiro-Wilk tester provided in the temperature sensor anomaly determination device, sorting the measured values of the temperature sensor stored in the data accumulator in ascending order and calculating a test statistic from the sum of squares, and testing whether the measured values of the temperature sensor are normally distributed by comparing the test statistic with a comparison reference value based on the number of sampled measured values and a coefficient used in the Shapiro-Wilk test; The method further includes a step of determining, by a sensor warning determiner provided in the temperature sensor abnormality determination device, whether or not the temperature sensor is a temperature sensor that issues an abnormality warning based on the inspection result of the inspector.

[0012] Claims of the invention 3 The temperature sensor abnormality determination program described in is a temperature sensor abnormality determination program that determines an abnormality in a temperature sensor that measures the temperature of an atmosphere of a measurement object, Computer, a data storage unit that stores the latest multiple sample measurements of the temperature sensor at predetermined time intervals; a Shapiro-Wilk tester that calculates a sum of squares of samples of the temperature sensor measurement values stored in the data accumulator, sorts the temperature sensor measurement values stored in the data accumulator in ascending order, calculates a test statistic from the sum of squares, and tests whether the temperature sensor measurement values are normally distributed by comparing the test statistic with a comparison reference value based on the number of sample measurements and a coefficient used in the Shapiro-Wilk test; The temperature sensor is characterized by functioning as a sensor warning determiner that determines whether or not the temperature sensor is a temperature sensor that issues a warning about an abnormality based on the test result of the Shapiro-Wilk tester. [Effects of the Invention]

[0013] According to the present invention, it is possible to predict an abnormality in a temperature sensor using the most recent measurement value of the temperature sensor, without requiring a dedicated device or parameter management as in the prior art. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a block diagram of a temperature sensor abnormality determination device according to the present invention; [Figure 2] 1 is a diagram showing an example of a measurement system in which a temperature sensor abnormality determination device according to the present invention is employed; DETAILED DESCRIPTION OF THE INVENTION

[0015] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0016] First, an example of a measurement system in which the present invention is adopted will be described with reference to Fig. 2. As shown in Fig. 2, the measurement system 11 comprises a vacuum heat treatment furnace (heating furnace) 12 as a measurement target and four control loops 13.

[0017] The inside of the vacuum heat treatment furnace 12 is maintained in a vacuum state by a vacuum pump 14, and heaters 15 (15A, 15B, 15C, 15D) are arranged on the upper left and right sides and the lower left and right sides of the furnace, respectively.

[0018] Further, a plurality of temperature sensors A1, A2, B1, B2 are arranged near each heater 15 (15A, 15B, 15C, 15D) to detect the temperature in the atmosphere (furnace temperature) of the vacuum heat treatment furnace 12. In the example of Fig. 2, temperature sensor A1 is arranged near heater 15A on the upper left side of the furnace, temperature sensor A2 is arranged near heater 15B on the upper right side of the furnace, temperature sensor B1 is arranged near heater 15C on the lower left side of the furnace, and temperature sensor B2 is arranged near heater 15D on the lower right side of the furnace.

[0019] In this embodiment, the temperature sensors A1, A2, B1, and B2 are, for example, thermocouples whose electromotive force increases with fluctuations in their internal resistance values, but this is not limited to these, and other sensors such as resistance thermometers can also be used.

[0020] The four control loops 13 are a first zone upper control loop 13A, a second zone upper control loop 13B, a first zone lower control loop 13C, and a second zone lower control loop 13D.

[0021] The first zone upper control loop 13A, the second zone upper control loop 13B, the first zone lower control loop 13C, and the second zone lower control loop 13D are each configured with a PID controller 13a and an SCR (thyristor) 13b to control the furnace temperature of the vacuum heat treatment furnace 12 to a target temperature so that the furnace temperature distribution for each batch processing lot is approximately uniform, for example.

[0022] In the first zone upper control loop 13A, the measurement value (temperature data) of the temperature sensor A1 and the upper furnace temperature set value are input to the PID controller 13a, and the PID controller 13a outputs an operation amount (0 to 100%) corresponding to the difference between them to the SCR 13b to control the drive, and the SCR 13b controls the heater 15A on / off.

[0023] Similarly, in the second zone upper control loop 13B, the measurement value of the temperature sensor A2 and the upper furnace temperature set value are input to the PID controller 13a, and the PID controller 13a outputs an operation amount (0 to 100%) corresponding to the difference between them to the SCR 13b for drive control, and the SCR 13b controls the on / off of the heater 15B.

[0024] In addition, in the first zone lower control loop 13C, the measurement value of the temperature sensor B1 and the upper furnace temperature set value are input to the PID controller 13a, and the PID controller 13a outputs an operation amount (0 to 100%) corresponding to the difference between them to the SCR 13b for drive control, and the SCR 13b controls the heater 15C on / off.

[0025] Furthermore, in the second zone lower control loop 13D, the measurement value of the temperature sensor B2 and the upper furnace temperature set value are input to the PID controller 13a, and the PID controller 13a outputs an operation amount (0 to 100%) corresponding to the difference between them to the SCR 13b for drive control, and the SCR 13b controls the heater 15D on / off.

[0026] The temperature sensor abnormality determination device of this embodiment has the function of determining which of the multiple temperature sensors (including measurement system 11) is abnormal by examining the differences in the magnitude of the variance (measurement variation) of the measurement values of the multiple temperature sensors in the measurement system (in the example of Figure 2, the four temperature sensors A1, A2, B1, and B2 in measurement system 11) and alerting the operator.

[0027] As shown in FIG. 1, the temperature sensor abnormality determination device 1 is configured to include a data accumulator 2, a verifier 3, and a sensor warning determiner 4 in order to realize the above functions.

[0028] The data accumulator 2 is composed of a data storage memory that accumulates the measurement values for each of the temperature sensors A1, A2, B1, and B2, and constantly accumulates and stores the measurement values (temperature data) of the latest 10 samples from each of the temperature sensors A1, A2, B1, and B2 as reference samples.

[0029] The measurement values stored in the data accumulator 2 are, for example, the measurement values of the temperature sensors A1, A2, B1, and B2 for each lot (predetermined time) of batch processing. The number of reference samples stored in the data accumulator 2 is not limited to the measurement values of 10 samples and can be changed as appropriate.

[0030] Tester 3 tests whether the most recent measurements follow a normal distribution, and is configured, for example, by a Shapiro-Wilk tester.

[0031] The Shapiro-Wilk test is performed according to the following steps 1 to 4. Note that, due to the amount of calculation involved, the number of measurement samples is limited to 10.

[0032] Step 1: Calculate the sum of squares of the acquired data The variances of the temperature sensors A1, A2, B1, and B2 are calculated using the following formulas (1), (2), (3), and (4). Here, the number of samples n is set to 10.

[0033]

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[0034]

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[0035]

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[0036]

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[0037] In addition, S A1 is the sum of squares of the samples of temperature sensor A1 (the sum of squares of individual measurements - the average value), S A2 is the sum of squares of the samples of temperature sensor A2 (the sum of squares of individual measurements - the average value), S B1 is the sum of squares of the samples of temperature sensor B1 (the sum of squares of individual measurements - the average value), S B2 is the sum of squares of the samples of temperature sensor B2 (the sum of squares of individual measurements - the average value), x A1i is the individual data of temperature sensor A1, x A2i is the individual data of temperature sensor A2, x B1i is the individual data of temperature sensor B1, x B2i is the individual data of temperature sensor B2, x A1 The bar indicates the average value of temperature sensor A1, and x A2 The bar indicates the average value of temperature sensor A2, and x B1 The bar indicates the average value of the temperature sensor B1, and x B2 The bar indicates the average value of temperature sensor B2.

[0038] Step 2: Sort the data The data from temperature sensors A1, A2, B1, and B2 are sorted in ascending order. Here, the number of data samples is set to 10. Temperature sensor A1...x A1(1) ,x A1(2) ,x A1(3) ,…x A1(9) ,x A1(10) Temperature sensor A2...x A2(1) ,x A2(2) ,x A2(3) ,…x A2(9) ,x A2(10) Temperature sensor B1...x B1(1) ,x B1(2) ,x B1(3) ,…x B1(9) ,x B1(10) Temperature sensor B2...x B1(1) ,x B2(2) ,x B2(3) ,…x B2(9) ,x B2(10)

[0039] Step 3: Calculate the test statistic W The test statistics W for each of the temperature sensors A1, A2, B1, and B2 are calculated using the following equations (5), (6), (7), and (8). A1 is the test statistic for temperature sensor A1, W A2 is the test statistic for temperature sensor A2, W B1 is the test statistic for temperature sensor B1, W B2 is the test statistic for temperature sensor B2.

[0040]

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[0041]

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[0042]

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[0043]

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[0044] Step 4: Test The test is performed using the test statistic W>W(n,α).

[0045] W(n,α) is the comparison standard value for the Shapiro-Wilk test, n is the number of data (number of lots), α is the significance level (generally 0.05), and α i is the coefficient (known) used in the Shapiro-Wilk test.

[0046] Here, n=10 and α=0.05. Therefore, W A1 >W(10,0.05),W A2 >W(10,0.05),W B1 >W(10,0.05),W B2 If W(10,0.05) is satisfied, it is not significant at the significance level α. In other words, the temperature sensor in question is not normally distributed, and there is a possibility of a malfunction.

[0047] In addition, the coefficient α i is as shown in Table 1 below. Here, the number of data samples n is set to 10, so the number of coefficients is 5.

[0048] [Table 1]

[0049] The rejection region test table is shown in Table 2 below.

[0050] [Table 2]

[0051] If the verifier 3 determines through the above-mentioned test that the measurement value of the temperature sensor A1 is normally distributed, it outputs "0" as the test result signal, and if it determines that the measurement value of the temperature sensor A1 is not normally distributed, it outputs "1" as the test result signal.

[0052] Similarly, if the verifier 3 determines that the measurement value of temperature sensor A2 is normally distributed, it outputs "0" as the test result signal, and if it determines that the measurement value of temperature sensor A2 is not normally distributed, it outputs "1" as the test result signal. Furthermore, if the verifier 3 determines that the measurement value of temperature sensor B1 is normally distributed, it outputs "0" as the test result signal, and if it determines that the measurement value of temperature sensor B1 is not normally distributed, it outputs "1" as the test result signal. Furthermore, if the verifier 3 determines that the measurement value of temperature sensor B2 is normally distributed, it outputs "0" as the test result signal, and if it determines that the measurement value of temperature sensor B2 is not normally distributed, it outputs "1" as the test result signal.

[0053] When the sensor warning determiner 4 receives a test result signal "1" from the verifier 3, it determines that the temperature sensor whose measured value is determined not to be a normal distribution is abnormal, and warns the operator by sound, display, etc. For example, when the verifier 3 determines that the measured value of temperature sensor B1 is not a normal distribution and receives a test result signal "1" from the verifier 3, the sensor warning determiner 4 determines that temperature sensor B1 is abnormal, and warns the operator.

[0054] Next, a test example using specific measured values from the four temperature sensors A1, A2, B1, and B2 will be described.

[0055] Now, it is assumed that the measured values of the temperature sensors A1, A2, B1, and B2 for each lot are obtained as shown in Table 3 below.

[0056] [Table 3]

[0057] A normality test is performed on A1. The measured values obtained are sorted in ascending order (A2 is also sorted in parallel), resulting in Table 4 below.

[0058] [Table 4]

[0059] α when the number of samples is n=10 i The number of coefficients is 5, and according to the numerical table in Table 1 above, α1 = 0.5739, α2 = 0.3291, α3 = 0.2141, α4 = 0.1224, and α5 = 0.0399.

[0060] Above α i From the sum of squares S=294.43, the test statistic W is calculated using the following formula (9).

[0061]

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[0062] From the numerical table, W(n,α) = W(10,0.05) = 0.842, and W = 0.8869 > 0.842, which is not significant. Therefore, it cannot be said that there is no normality. In other words, it cannot be said that temperature sensor A1 is faulty.

[0063] Similarly, a normality test is performed on A2. From the sum of squares s=795.67, the test statistic W is calculated using the following formula (10).

[0064]

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[0065] From the numerical table, W(n,α) = W(10,0.05) = 0.842, and W = 0.1095 < 0.842, so it is significant. Therefore, it can be said that there is no normality. In other words, it can be said that temperature sensor A2 is faulty.

[0066] In the above-described embodiment, the case where the temperature inside the vacuum heat treatment furnace 12 in the measurement system 11 is detected by four temperature sensors A1, A2, B1, and B2 has been described as an example, but the number of measurement systems and temperature sensors is not limited.

[0067] Furthermore, in the above-described embodiment, the components of the temperature sensor abnormality determination device 1 (each of the data accumulator 2, the verification device 3, and the sensor warning determination device 4) may be configured as a computer equipped with an arithmetic processing device, a memory device, etc., and the processing of each component may be executed by a program.

[0068] As described above, according to the embodiment described above, it is possible to predict abnormalities in a temperature sensor using the most recent measurement value output by the temperature sensor itself, without requiring dedicated devices or parameter management as in the conventional art.

[0069] While the best mode for the temperature sensor anomaly determination device, temperature sensor anomaly determination method, and temperature sensor anomaly determination program according to the present invention has been described above, the present invention is not limited to the description and drawings of the mode. In other words, all other modes, embodiments, and operational techniques that are made by those skilled in the art based on the mode are naturally included in the scope of the present invention. [Explanation of symbols]

[0070] 1. Temperature sensor abnormality detection device 2 Data Accumulator 3. Tester 4 Sensor warning detector 11 Measurement System 12 Vacuum heat treatment furnace 13 Control Loop 13A Zone 1 upper control loop 13B Zone 2 upper control loop 13C Zone 1 Lower Control Loop 13D Second Zone Lower Control Loop 13a PID controller 13b SCR (thyristor) 14 Vacuum pump 15 (15A, 15B, 15C, 15D) Heater A1, A2, B1, B2 temperature sensors

Claims

1. A temperature sensor abnormality determination device that determines an abnormality in a temperature sensor that measures the temperature of an atmosphere of a measurement object, a data storage unit that stores the latest multiple sample measurements of the temperature sensor at predetermined time intervals; a Shapiro-Wilk tester that calculates the sum of squares of the sampled measured values of the temperature sensor stored in the data storage device, sorts the measured values of the temperature sensor stored in the data storage device in ascending order, calculates a test statistic from the sum of squares, and tests whether the measured values of the temperature sensor are normally distributed by comparing the test statistic with a comparison reference value based on the number of sampled measured values and a coefficient used in the Shapiro-Wilk test; a sensor warning determiner that determines whether the temperature sensor is a temperature sensor that issues an abnormality warning based on the test results of the Shapiro-Wilk tester.

2. A temperature sensor abnormality determination method using a temperature sensor abnormality determination device that determines an abnormality in a temperature sensor that measures the temperature of an atmosphere of a measurement object, comprising: storing the latest multiple sampled measurement values of the temperature sensor at predetermined time intervals using a data storage device included in the temperature sensor abnormality determination device; a step of calculating the sum of squares of the sampled temperature sensor measurement values stored in the data accumulator using a Shapiro-Wilk tester included in the temperature sensor anomaly determination device, sorting the temperature sensor measurement values stored in the data accumulator in ascending order and calculating a test statistic from the sum of squares, and testing whether the temperature sensor measurement values are normally distributed by comparing the test statistic with a comparison reference value based on the number of sampled measurement values and a coefficient used in the Shapiro-Wilk test; A temperature sensor abnormality determination method characterized by including a step of determining, by a sensor warning determiner provided in the temperature sensor abnormality determination device, whether the temperature sensor is a temperature sensor that warns of an abnormality based on the inspection result of the inspection device.

3. A temperature sensor abnormality determination program for determining an abnormality in a temperature sensor that measures a temperature in an atmosphere of a measurement object, Computer, a data storage unit that stores the latest multiple sample measurements of the temperature sensor at predetermined time intervals; a Shapiro-Wilk tester that calculates the sum of squares of the sampled measured values of the temperature sensor stored in the data storage device, sorts the measured values of the temperature sensor stored in the data storage device in ascending order, calculates a test statistic from the sum of squares, and tests whether the measured values of the temperature sensor are normally distributed by comparing the test statistic with a comparison reference value based on the number of sampled measured values and a coefficient used in the Shapiro-Wilk test; A temperature sensor abnormality determination program for causing the temperature sensor to function as a sensor warning determiner that determines whether the temperature sensor is a temperature sensor that issues an abnormality warning based on the test results of the Shapiro-Wilk tester.

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