Method for monitoring a metrological performance of a measuring device

Abstract

With reference to figure 4, the method for monitoring a metrological performance of a measuring device (300), wherein the measuring device provides a series of successive measured values of a physical parameter, can be summarized as follows: The method comprises the following: obtaining an initial set of successive measured values in the series of successive measured values in a reference state of the parameter (310); calculating an initial standard deviation of the initial set of successive measured values (320); calculating an initial average of the initial set of successive measured values (330); subsequently obtaining a monitoring set of successive measured values in the series of successive measured values in the reference state of the parameter (340); calculating a monitoring standard deviation of the monitoring set of successive measured values (350); calculating a monitoring average of the monitoring set of successive measured values (360); calculating a metrological distance of the monitoring set from the initial set on the basis of the initial standard deviation, the monitoring standard deviation, the initial average and the monitoring average (370); and evaluating the metrological performance of the measuring device on the basis of the metrological distance of the monitoring set (380).

Description

[0001] Method for monitoring the metrological performance of a measuring device

[0002] The present invention relates to a method for monitoring the metrological performance of a measuring device, wherein the measuring device provides a series of consecutive measured values ​​of a physical parameter, e.g., a flow rate of a medium. A critical parameter for measuring the flow rate is the zero point or zero flow value. Therefore, it is known to monitor the zero point as an aspect of the measurement performance. For example, publication WO 2022 / 256001 discloses measurement bias detection of a reference zero flow value, and publications WO 2022 / 256001 and WO 2022 / 255999 disclose how to determine and select the criteria for zero verification depending on the media properties. However, while the bias is an important aspect of the zero point, a limited analysis of the bias provides an incomplete analysis.In view of the above, it is an object of the invention to provide a method for a more comprehensive analysis of the measurement performance.

[0003] This object is achieved by the method according to independent claim 1.

[0004] The method according to the invention serves to monitor the metrological performance of a measuring device, the measuring device providing a series of consecutive measured values ​​of a physical parameter; the method comprising:

[0005] Obtaining an initial set of consecutive measured values ​​of the series of consecutive measured values ​​in a reference state of the parameter;

[0006] Calculating an initial standard deviation of the initial set of consecutive measurements;

[0007] Calculating an initial average of the initial set of consecutive measured values; then obtaining a monitoring set of consecutive measured values ​​of the series of consecutive measured values ​​in the reference state of the parameter;

[0008] Calculating a monitoring standard deviation of the monitoring set of consecutive measured values;

[0009] Calculating a monitoring average of the monitoring set of consecutive measured values;

[0010] Calculating a metrological distance of the monitoring set from the initial set based on the initial standard deviation, the monitoring standard deviation, the initial average, and the monitoring average; and

[0011] Assess the metrological performance of the measuring device based on the metrological distance of the monitoring set.

[0012] According to a further development of the invention, the method further comprises the following:

[0013] Obtaining a probability for the monitoring set based on the metrological distance of the monitoring set; wherein assessing the metrological performance of the monitoring device is based on the probability of the monitoring set.

[0014] According to a further development of the invention, the monitoring set comprises not fewer than three consecutive measured values, for example not fewer than thirty consecutive measured values, in particular not fewer than three hundred consecutive measured values.

[0015] According to a further development of the invention, the probability density value is obtained by means of a model which has an x 2 distribution with two degrees of freedom, where p(d 2 i), which is given as: where d 2 i is the measured distance.

[0016] According to a further development of the invention, the

[0017] Probability density value obtained using a model that uses a Weibull distribution w(d 2 i) includes, which is given as: where d 2 i is the metrological distance, ß is a shape parameter, n is a scale parameter, and y is a location parameter.

[0018] According to a further development of the invention, the parameters meet the following requirements:

[0019] 0.5 < ß < 1 , in particular 0.7 < ß < 0.9 and / or

[0020] 2 < r| < 4, in particular 2.5 < r| < 3.5; and / or y = 0.

[0021] According to a further development of the invention, the metrological distance of the monitoring set is calculated as where where where

[0022] Xi is a monitoring average of an i-th monitoring set of consecutive measured values, QI is a monitoring standard deviation of an i-th monitoring set of consecutive measured values, x re f is the initial average of the initial set of consecutive measurements, cref is an initial standard deviation of the initial set of consecutive measurements, and m is the number of consecutive measurements in each set.

[0023] According to a further development of the invention, the method further comprises the following:

[0024] Calculating a quotient of a difference between the monitoring standard deviation and the initial standard deviation divided by a difference between the monitoring average and the initial average, and

[0025] Calculating a monitoring angle as a function of the quotient, in particular as the arctangent of the quotient; and wherein the assessment of the metrological performance of the monitoring device is further based on the monitoring angle, in particular wherein possible causes for the metrological distance of the monitoring set are investigated based on the monitoring angle.

[0026] According to a further development of the invention, the method further comprises the following:

[0027] Installing the measuring device at a measuring point, whereby the initial set is obtained from the installed measuring device.

[0028] According to a further development of the invention, the measuring device comprises a flow meter, wherein the parameter is a flow rate, wherein the reference state of the parameter is a flow rate of zero, and wherein the assessment of the metrological performance of the monitoring device relates to a stability of a zero point, wherein the zero point comprises the monitoring set.

[0029] According to a further development of the invention, assessing the metrological performance of the monitoring device comprises the following: comparing the probability value of the monitoring set with a probability threshold; if the probability of the monitoring set is below a probability threshold, comparing the monitoring angle with a first monitoring angle threshold; if the monitoring angle is less than the first monitoring angle threshold, investigating probable causes of a bias effect; and / or if the probability of the monitoring set is below a probability threshold, comparing the monitoring angle with a second monitoring angle threshold; if the monitoring angle is greater than the second monitoring angle threshold, investigating probable causes of a lack of repeatability;in particular wherein the first monitoring angle threshold value is between 20° and 30°, in particular 25°, and / or the second monitoring angle threshold value is between 50° and 60°, in particular 55°;

[0030] According to a further development of the invention, the method further comprises the following:

[0031] Investigate the causes of a bias effect and the causes of a lack of repeatability when the monitoring angle is between the first monitoring angle threshold and the second monitoring angle threshold.

[0032] According to a further development of the invention, the method further comprises the following:

[0033] Comparing a monitoring vector comprising an average difference as a first component and the monitoring standard deviation as a second component with a boundary curve, the boundary curve being defined by a set of boundary vectors, the boundary vectors comprising a difference from the initial average as a first component and a standard deviation boundary as a second component; and

[0034] Outputting a signal when the monitor set lies outside a range bounded by the limit curve; wherein the average difference is defined as the difference between the monitor average and the initial average; and wherein the standard deviation limit is defined as a function of the difference between the initial average and a limit for the average difference.

[0035] According to a further development of the invention, the limit curve is given as where k is a constant greater than 1, for example k = 2.

[0036] According to a further development of the inventive method, the method further comprises checking whether the sum of the square of k times the monitoring standard deviation and the square of the average difference is less than or equal to the square of a limit for the average difference, where k is a constant greater than 1, for example k = 2; and outputting a signal if not.

[0037] According to a further development of the invention, the limit of the average difference depends on a temperature measurement value representing the temperature of the measuring device.

[0038] According to a further development of the invention, the method further comprises the following:

[0039] Providing a status signal related to the measurement performance of the measuring device when the probability of the monitoring set is below a probability threshold.

[0040] According to a further development of the invention, the status signal represents causes or groups of causes identified by the investigation of causes. Embodiments of the invention are discussed below with reference to the accompanying drawings, wherein

[0041] Fig. 1 : shows a diagram of metrological distances of monitoring sets in a framework of probabilities and limit curves;

[0042] Fig. 2: shows a probability representation for monitoring sets based on their measured distance;

[0043] Fig. 3: shows a block diagram of an embodiment of the method according to the present invention; and

[0044] Fig. 4: shows a block diagram of another embodiment of the method according to the present invention.

[0045] In the embodiments discussed below, the physical parameter in question is the zero point of a flowmeter, i.e., the offset measured at a reference condition of zero flow, which reference condition can be obtained, for example, by closing a valve in the pipeline in the direction of the flowmeter. When installing a flowmeter at a measuring point, e.g., in a processing plant, a factory zero point may require initial correction due to various reasons, e.g., mechanical stresses on the flowmeter. To this end, an initial set of consecutive zero point readings is obtained immediately after installation of the flowmeter at a reference condition of the parameter, i.e., a zero flow.An initial average of the initial set of consecutive measured values ​​is calculated, and this average is set as the zero value for the installed flowmeter. In addition, an initial standard deviation of the initial set of consecutive measured values ​​is calculated. The initial average and initial standard deviation of the zero point define the origin of the graph in Figures 1 and 2. Subsequent monitoring sets are obtained to verify the zero point during operation.

[0046] The points in Figures 1 and 2 represent monitoring sets, for each of which a set of consecutive measurements is subsequently obtained when the flow through the flowmeter is expected to be zero. For each monitoring set, a monitoring standard deviation of the monitoring set of consecutive measurements and a monitoring average of the monitoring set of consecutive measurements are calculated. The deviation of the monitoring average from the initial average defines the horizontal position of the monitoring set in the graphs, while the vertical position is defined by the corresponding difference in the standard deviations.

[0047] The dashed lines in the diagrams of Fig. 1 and 2 define levels of equal metrological distances di 2 to the origin of the diagram, ie from the mean and standard deviation of the original set. The metrological distance di 2can be calculated using the flowing equations: where where gi> CTref) where

[0048] Xi is a monitoring average of an i-th monitoring set of consecutive measured values, QI is a monitoring standard deviation of an i-th monitoring set of consecutive measured values, x re f is the initial average of the initial set of consecutive measurements, aref is an initial standard deviation of the initial set of consecutive measurements, and m is the number of consecutive measurements in each set. Based on the metrological distance di 2 and a proper distribution function, a probability is calculated for each of the monitoring sets. A suitable distribution can be an x 2 -distribution with two degrees of freedom. The corresponding probability density p(d 2 i), which is given as: 1 _ P(d 2i) = " e 2 where d 2 i is the metrological distance. The dashed lines of equal metrological distance are also lines of equal probability density. The integrated probability densities for metrological distances from 0 to the metrological distance of a dashed line define the probability for a monitoring set to fall within the range bounded by the dashed line, assuming the flowmeter and its operating conditions remain unchanged.

[0049] The choice of the correct distribution function requires verification for the specific arrangement of the process, e.g., type of flowmeter, number of consecutive values, etc. While the x 2-distribution works well for many situations, a Weibull distribution provides more parameters to adapt it to a specific situation. Thus, according to a further embodiment of the invention, the probability density value is obtained using a model that uses a Weibull distribution w(d 2 i) includes, which is given as: where d 2 i is the metrological distance, ß is a shape parameter, n is a scale parameter, and y is a location parameter. The parameters meet the following requirements: 0.5 < ß < 1, in particular 0.7 < ß < 0.9; 2 < r| < 4, in particular 2.5 < r| < 3.5; and y = 0.

[0050] The percentages corresponding to the curves in Fig. 1 indicate the complement of the probabilities, i.e., the probability for a monitoring set to fall outside the range bounded by the respective dashed line in an unchanged system. For example, the 32% on the inner dashed line indicates that 68% of all monitoring sets should fall within the range bounded by the inner dashed line, all metrological conditions unchanged. Similarly, the 5% line indicates that 95% should be bounded by the line and that the probability for a monitoring set to fall outside this curve is only 5% under unchanged conditions. However, it is evident that a significant number of monitoring sets fall outside the 5% curve, indicating a need for analysis, the details of which are discussed below.

[0051] The diagram in Fig. 1 further includes two curves, labeled L and 2L. These curves are not curves of equal metrological distance with respect to the initial monitoring set. Instead, they delimit ranges for the monitoring sets, resulting from a predetermined limit for the zero point (L) and the double limit (2L). At their respective intersections with the lower horizontal line, which corresponds to a standard deviation of zero, the curve L corresponds to the predetermined limit. For smaller differences between the average of a monitoring set and the average of the initial set, the standard deviation of a monitoring set may increase. More precisely, the limit curve L is defined by pairs of average differences and corresponding standard deviations, where the corresponding standard deviations GL are given as:

[0052] 1 > o L= (limit for the average difference) 2 — (average difference) 2

[0053] The factor is motivated by the consideration that only about 34% of all monitoring sets statistically fall within the interval of one standard deviation, while about 95% fall within the interval of two standard deviations. Thus, limiting by the factor ensures significantly stricter monitoring that the specified limit is met when a monitoring set is checked against the limit curve.

[0054] As shown in Fig. 1, a significant number of the monitoring sets lie outside the range bounded by the 95% probability curve. While all monitoring sets are still within the curve L, which results from a predetermined and / or required limit for the zero point. This means that the flowmeter can continue to operate, but early changes in the metrological performance can be detected and analyzed to prevent the flowmeter from operating outside its specification. A further aspect of the inventive method is discussed with reference to Fig. 2, which diagram shows the same equal probability density curves as the diagram in Fig. 1. In addition, the plotted monitoring sets are labeled with an overall probability in % and with a monitoring angle in °.The overall probability indicates the probability for the monitoring set to be outside the range bounded by an equal probability density curve passing through the monitoring set's position. The monitoring angle is calculated as the arctangent of a quotient of the difference between the monitoring standard deviation and the initial standard deviation divided by the difference between the monitoring average and the initial average. The monitoring angle is a simple means of indicating trends for causes of deviations in the zero point.High monitoring angles indicate problems of short-term fluctuations and noise repeatability, while low angles indicate bias problems and long-term zero drift, therefore it is beneficial to consider the monitoring angle when analyzing root causes of the metrological distance of a monitoring set from the initial set.

[0055] The embodiment of the method 100 shown in Fig. 3 begins with generating the origin 110 of the metrological distance curves shown in Figs. 1 and 2, comprising collecting data for an initial set of consecutive readings for the zero point of a newly installed meter, obtaining an initial average to be set as the new zero point, and obtaining an initial standard deviation of the initial set of consecutive readings.

[0056] During subsequent operation of the flowmeter, the method 100 includes a subsequent zero verification 120, comprising: obtaining a probability value 122 for a monitoring set of consecutive measurement values ​​based on its metrological distance from the initial set, and comparing the probability value to a threshold 124, which in the embodiment is set to 95%. If the test passes, the zero verification 120 is complete and can be repeated at an appropriate time. However, if the probability for the monitoring set is too low, the causes must be analyzed. This begins with a comparison of a monitoring angle 140 of the monitoring set in question with at least one threshold, here an angle of 25°. If the monitoring angle is below 25°, an analysis for bias-related problems 160 is started.If the angle is greater than 25° or greater than a second, higher threshold angle, which can be verified in a second test (not shown here), an analysis for the causes of the repetition problems is initiated. The angles between the two viewpoints can be considered.

[0057] The analysis for bias-related problems 160 begins with the investigation of bias root causes 162, which can include changes in both the flowmeter and the metering installation of which the flowmeter is a part. Root causes can include leaks, particularly leaky valves, sensor asymmetries in Coriolis flowmeters, buildup on the measuring tube walls, altered mechanical stresses acting on the sensor, and the like.

[0058] If a root cause is identified, a test 164 is performed to decide whether the root cause is attributable to the flowmeter. If so, a test 168 is performed to decide whether the flowmeter needs to be replaced or whether the flowmeter can be repaired. Depending on the decision, different further actions 200 are initiated. For example, a request for a replacement 202 or for a repair 204 is issued. If the root cause is not attributable to the flowmeter, another test 166 is performed to decide whether repair is an option, e.g., by fixing a leaking valve, or whether a new zero point adjustment is advisable. In this case, a request to this effect 206 is issued.

[0059] The analysis for repeatability 180 problems begins with investigating the root causes of repeatability 182, which may include inhomogeneous media turbulence, entrained gas bubbles, vibrations or pressure changes in the flowmeter environment, irregular leak rates, and the like.

[0060] If a root cause of repeatability is identified, a test 184 is performed to determine if it can be corrected. Further action 200 is initiated by issuing a request for repair 204. However, if correction is not an option, a decision 186 is made to continue exiting the zero point at its previous value and repeat the verification process at the appropriate time.

[0061] With reference to Fig. 4, the method for monitoring metrological performance of a measuring device 300, wherein the measuring device provides a series of consecutive measurements of a physical parameter, can be summarized as follows: The method includes: obtaining an initial set of consecutive measurements of the series of consecutive measurements in a reference state of the parameter 310; calculating an initial standard deviation of the initial set of consecutive measurements 320; calculating an initial average of the initial set of consecutive measurements 330; then obtaining a monitoring set of consecutive measurements of the series of consecutive measurements in the reference state of the parameter 340; calculating a monitoring standard deviation of the monitoring set of consecutive measurements 350;Calculating a monitoring average of the monitoring set of consecutive measured values ​​360; Calculating a metrological distance of the monitoring set from the initial set based on the initial standard deviation, the monitoring standard deviation, the initial average, and the monitoring average 370; and Assessing the metrological performance of the measuring device based on the metrological distance of the monitoring set 380.

Claims

Patent claims 1 . A method (300) for monitoring a metrological performance of a measuring device, wherein the measuring device provides a series of consecutive measured values ​​of a physical parameter, the method (300) comprising: Obtaining an initial set of consecutive measured values ​​of the series of consecutive measured values ​​in a reference state of the parameter (310); Calculating an initial standard deviation of the initial set of consecutive measured values ​​(320); Calculating an initial average of the initial set of consecutive measured values ​​(330); then obtaining a monitoring set of consecutive measured values ​​of the series of consecutive measured values ​​in the reference state of the parameter (340); Calculating a monitoring standard deviation of the monitoring set of consecutive measured values ​​(350); Calculating a monitoring average of the monitoring set of consecutive measured values ​​(360); Calculating a metrological distance of the monitoring set from the initial set based on the initial standard deviation, the monitoring standard deviation, the initial average and the monitoring average (370); and Assessing the metrological performance of the measuring device based on the metrological distance of the monitoring set (380).

2. The method of claim 1, further comprising: Obtaining a probability for the monitoring set based on the metrological distance of the monitoring set; wherein assessing the metrological performance of the monitoring device is based on the probability of the monitoring set.

3. The method according to claim 1, wherein the monitoring set comprises not less than three consecutive measured values, for example not less than thirty consecutive measured values, in particular not less than three hundred consecutive measured values.

4. The method according to claim 2 or 3, wherein a probability density value is obtained by means of a model which has an x 2 - distribution with two degrees of freedom, where p(d 2 i), which is given as: where d 2 i is the metrological distance.

5. The method according to claim 2 or 3, wherein the probability density value is obtained by means of a model having a Weibull distribution w(d 2 i) includes, which is given as: where d 2 i is the metrological distance, ß is a shape parameter, r| is a scale parameter, and y is a location parameter.

6. The method according to claim 5, wherein 0.5 < ß < 1 , in particular 0.7 < ß < 0.9 and / or 2 < r| < 4, in particular 2.5 < r| < 3.5; and / or y = 0.

7. Method according to one of the preceding claims, wherein the metrological distance of the monitoring set is calculated as where where where Xi is a monitoring average of an i-th monitoring set of consecutive measured values, oi is a monitoring standard deviation of an i-th monitoring set of consecutive measured values, x re f is the initial average of the initial set of consecutive measurements, Gref is an initial standard deviation of the initial set of consecutive measurements, and m is the number of consecutive measurements in each set.

8. The method according to any one of the preceding claims, further comprising: Calculating a quotient of a difference between the monitoring standard deviation and the initial standard deviation divided by a difference between the monitoring average and the initial average, and Calculating a monitoring angle as a function of the quotient, in particular as the arctangent of the quotient; and wherein the assessment of the metrological performance of the monitoring device is further based on the monitoring angle, in particular wherein possible causes for the metrological distance of the monitoring set are investigated based on the monitoring angle.

9. The method according to any one of the preceding claims, further comprising: Installing the measuring device at a measuring point, obtaining the initial set from the installed measuring device.

10. A method according to any one of the preceding claims, wherein the measuring device comprises a flow meter, wherein the parameter is a flow rate, the reference state of the parameter being a flow rate of zero, and wherein assessing the metrological performance of the monitoring device relates to a stability of a zero point, the zero point comprising the monitoring set. 11 . Method according to claim 2 and 8 or any claim dependent on these claims, wherein assessing the metrological performance of the monitoring device comprises: Comparing the probability value of the monitoring set with a probability threshold; if the probability of the monitoring set is below a probability threshold, comparing the monitoring angle with a first monitoring angle threshold; if the monitoring angle is less than the first monitoring angle threshold, investigating probable causes of a bias effect; and / or if the probability of the monitoring set is below a probability threshold, comparing the monitoring angle with a second monitoring angle threshold; if the monitoring angle is greater than the second monitoring angle threshold, investigating probable causes of a lack of repeatability;in particular wherein the first monitoring angle threshold value is between 20° and 30°, in particular 25°, and / or the second monitoring angle threshold value is between 50° and 60°, in particular 55°; 12. The method according to claim 11 or any preceding claim, further comprising: Investigate the causes of a bias effect and the causes of a lack of repeatability when the monitoring angle is between the first monitoring angle threshold and the second monitoring angle threshold.

13. The method according to any one of the preceding claims, further comprising: Comparing a monitoring vector comprising an average difference as a first component and the monitoring standard deviation as a second component with a boundary curve, the boundary curve being defined by a set of boundary vectors, the boundary vectors comprising a difference from the initial average as a first component and a standard deviation boundary as a second component; and Outputting a signal when the monitoring set lies outside a range bounded by the limit curve; wherein the average difference is defined as the difference between the monitoring average and the initial average; and wherein the standard deviation limit is defined as a function of the difference between the initial average and a limit for the average difference.

14. The method according to claim 13, wherein the limit curve is given as Standard deviation limit = - ■ (limit for the average difference) 2 — (average difference) K 2 where k is a constant greater than 1, for example k = 2.

15. The method according to any one of claims 1 to 12, further comprising: Check if the sum of the square of k times the Monitoring standard deviation and the square of the average Difference less than or equal to the square of a limit for the average Difference, where k is a constant greater than 1, for example k = 2; and output a signal if not.

16. The method according to claims 13 to 15, wherein the limit of the average difference depends on a temperature measurement value representing the temperature of the measuring device.

17. The method according to any one of the preceding claims, further comprising: providing a status signal relating to the measurement performance of the measuring device if the probability of the monitoring set is below a probability threshold.

18. A method according to claim 16 and 12 or any claim dependent on claim 12, wherein the status signal represents causes or groups of causes identified by the investigation of causes.

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