Coriolis mass flowmeter signal processing method based on frequency mixing algorithm

Abstract

The invention discloses a signal processing method for a Coriolis mass flowmeter based on a frequency mixing algorithm, relates to the technical field of Coriolis mass flowmeters, and solves the problems that the stability of driving intensity has obvious influence on the quality of a pipeline vibration signal, and if the driving intensity is unreasonable, the pipeline vibration is asymmetric, and the signal quality is influenced. In order to solve the problem that an extra interference phase difference is introduced, the invention locks the optimal driving intensity by finely debugging the driving intensity and taking a waveform stability parameter as a core index, so that the problem of asymmetry of vibration signals caused by uneven driving can be effectively avoided, and the introduction of the extra interference phase difference is reduced. A stable signal foundation is laid for subsequent phase difference measurement; in the process, the waveform characteristic difference is quantitatively analyzed, so that the driving intensity is ensured to be in the state of enabling the vibration signal to be the most stable, the signal quality is improved from the source, and a premise is provided for high-precision measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of Coriolis mass flowmeters, and in particular to a Coriolis mass flowmeter signal processing method based on a frequency mixing algorithm. Background Art

[0002] As a high-precision device that directly measures mass flow, the Coriolis mass flowmeter is widely used in industrial fields such as chemical, petroleum, and pharmaceuticals because it is not affected by parameters such as fluid density and viscosity. Its core principle is to use the Coriolis force generated by the fluid in the vibrating pipe to calculate the mass flow by measuring the phase difference of the vibration signals at both ends of the pipe. The measurement accuracy of the phase difference directly determines the accuracy of flow measurement.

[0003] However, in actual applications, the measurement effect of Coriolis mass flowmeters is easily restricted by many factors: on the one hand, the stability of the driving strength has a significant impact on the quality of the pipeline vibration signal. If the driving strength is unreasonable, it will cause asymmetric pipeline vibration, introduce additional interfering phase differences, and interfere with the extraction of the true flow signal; on the other hand, the pipeline vibration signal is a high-frequency sine wave, and its phase difference is usually in the nanosecond level. It is difficult to directly measure the phase difference of the high-frequency signal and is easily affected by factors such as noise and electromagnetic interference, resulting in increased measurement errors; in addition, during long-term operation, the pipeline may experience abnormal vibrations (such as collision with the inner wall of the cavity) due to installation deviation, wear or fluid pulsation. If such abnormalities cannot be identified in time, the flow measurement results will be distorted, affecting the precise control of industrial production.

[0004] Therefore, how to improve the measurement accuracy and reliability of Coriolis mass flowmeters under complex working conditions by optimizing driving strength, accurately extracting phase differences, and dynamically monitoring measurement stability has become a technical problem that needs to be urgently solved in this field. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a Coriolis mass flowmeter signal processing method based on a mixing algorithm, which solves the problem that the stability of the driving strength has a significant impact on the quality of the pipeline vibration signal. If the driving strength is unreasonable, it will cause asymmetric pipeline vibration and introduce additional interfering phase differences.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a Coriolis mass flowmeter signal processing method based on a mixing algorithm, comprising the following steps:

[0007] Step 1: Debug the drive modules associated with both sides of the pipeline, adjust the drive strength, and record the signal waveforms of the vibration signals on both sides of the pipeline. Analyze and confirm the stability. From the analysis and confirmation process, lock in the optimal drive strength. The specific method is as follows:

[0008] The preset driving strength of the driving module is calibrated as Q, and the debugging range is determined based on Q. The debugging range is (Q±0.3Q);

[0009] A driving intensity is selected as the execution intensity within the confirmed debugging range, and the signal waveforms of the vibration signals on both sides of the pipeline associated with the corresponding execution intensity are obtained. The stability values ​​associated with the two sets of signal waveforms are confirmed: several peak points existing in a single signal waveform are confirmed in sequence, and the waveform segments associated with adjacent peak points are confirmed. The confirmed waveform segments are recorded as peak segments. The highest peak point and the lowest peak point are confirmed from the single peak segment. Based on the amplitude characteristics between the highest peak point and the lowest peak point, the difference amplitude is confirmed, and the confirmed difference amplitude is used as the waveform characteristic of the peak segment.

[0010] The different waveform features associated with different waveform segments in a single signal waveform are confirmed in sequence. From the confirmed waveform features, the waveform features with consistent values ​​are confirmed and used as standard features. If there are multiple groups of waveform features with consistent values, the waveform features with the largest number of consistent values ​​are used as standard features. The confirmed standard features are recorded as Bz, and the other waveform features are recorded as T. i , where i represents different waveform segments, using: |Bz-T i |÷Bz=X i Confirm the defining feature T i , and then from several limiting features T associated with a single signal waveform i In the example, select the minimum value T i min, and T i min is used as the stability parameter of this signal waveform;

[0011] Using the same stability parameter confirmation method, confirm the stability parameters associated with another set of signal waveforms, average the stability parameters of the two sets of signal waveforms associated with the current execution strength, confirm the parameter mean, and use the parameter mean as the strength feature of the current execution strength;

[0012] Confirm the intensity features associated with different execution intensities in sequence, select the maximum value from the confirmed intensity features, and use the execution intensity associated with the maximum value as the optimal driving intensity. If there are multiple groups of execution intensities associated with the maximum value, randomly select one group of execution intensities as the optimal driving intensity.

[0013] Step 2: After the optimal drive strength is confirmed, collect the vibration signals associated with both sides of the pipeline, mix the vibration signals on both sides with the reference signal, confirm the cosine component and the sine component, and then perform unified confirmation to identify the phase difference associated with the pipelines on both sides. The specific method is as follows:

[0014] After the optimal driving strength is implemented, the frequency w and amplitude associated with the corresponding vibration signals on both sides of the pipeline are collected, and the expression for the corresponding vibration signal is generated: as well as , where A1 and A2 represent the amplitudes associated with the corresponding vibration signals, and where δ1 and δ2 represent the phases associated with the corresponding vibration signals;

[0015] The confirmed entry side signal Mixing is performed with the associated reference signals R(t) = sin(wt) and Q(t) = cos(wt), where R(t) can be understood as the standard in the vertical direction and Q(t) as the standard in the horizontal direction. Multiplying S1(t) by R(t) yields: , divide this formula into: low-frequency component and high-frequency components , retain the low-frequency components , and record it as cosine component, filtering out high-frequency frequency components;

[0016] Multiply S1(t) and Q(t) in the same way and retain the resulting low-frequency component. , and record it as a sine component, and then use: Confirm the phase value δ1 associated with the corresponding vibration signal;

[0017] Then the exit side signal Perform mixing processing with the associated reference signals R(t) = sin(wt) and Q(t) = cos(wt), and simultaneously use the same processing method to determine the phase value δ2 associated with another set of vibration signals;

[0018] Based on the confirmed phase value δ1 and phase value δ2, the phase difference = (δ1-δ2) is used to lock the phase difference currently measured;

[0019] Step 3: Define a set of monitoring periods, record the different phase differences associated with different moments in the monitoring period, and identify and display the associated mass flow values ​​based on the fluctuation characteristics of the phase difference values. The specific process is as follows:

[0020] Taking the current moment as the reference moment, define a set of monitoring periods, where the monitoring period is a preset period. Record the different phase differences associated with different moments in the monitoring period in sequence, select the minimum and maximum values ​​from the recorded phase differences, and record the difference range F between the minimum and maximum values, where F = maximum value - minimum value.

[0021] And record the comparison process of the difference range F and the threshold Y1: if F≤Y1, perform average processing on several groups of phase differences to confirm the phase difference mean J, and use: K×J=mass flow rate to confirm its mass flow value and display it directly, where Y1 is the preset value and K is the proportional coefficient; if F>Y1, generate an unconfirmed signal for display.

[0022] The present invention provides a Coriolis mass flowmeter signal processing method based on a frequency mixing algorithm. Compared with the prior art, it has the following advantages:

[0023] From the perspective of measurement accuracy, by fine-tuning the drive strength in step one and using waveform stability parameters as the core indicator to lock in the optimal drive strength, we can effectively avoid vibration signal asymmetry caused by uneven drive, reduce the introduction of additional interfering phase differences, and lay a stable signal foundation for subsequent phase difference measurements. This process ensures that the drive strength is at the most stable state for the vibration signal by quantitatively analyzing the differences in waveform characteristics, improving signal quality from the source and providing the prerequisites for high-precision measurement.

[0024] In terms of the scientificity and reliability of signal processing, step two uses a mixing algorithm to process the vibration signal. Using an orthogonal reference signal, the phase information of the high-frequency vibration signal is converted into an easily measurable low-frequency DC component. The inverse tangent function is then used to accurately extract the phase difference, perfectly solving the difficulty of measuring the phase difference of high-frequency signals. This processing method, based on the characteristics of trigonometric functions and phase-locked loop synchronization technology, achieves a precise conversion of phase difference from signal extraction to calculation, enabling the accurate application of the linear relationship between mass flow and phase difference, significantly improving the scientificity and reliability of flow calculations.

[0025] In terms of practical adaptability and safety, step three analyzes phase difference fluctuation characteristics by setting a monitoring period, enabling timely identification of anomalies during the measurement process. When the phase difference fluctuation exceeds the threshold, a warning signal is generated to facilitate maintenance personnel intervention, effectively avoiding erroneous measurement results caused by unexpected situations such as pipeline collisions, ensuring the continuity and safety of flow monitoring. Furthermore, for cases with gentle fluctuations, the flow rate is calculated using the mean value, further smoothing out errors caused by instantaneous fluctuations and improving the stability of the measurement results.

[0026] In summary, this method significantly improves the measurement accuracy, stability and reliability of the Coriolis mass flowmeter under complex working conditions through the organic combination of drive optimization, precise signal processing and dynamic monitoring, reduces the impact of external interference and equipment abnormalities on the measurement results, and provides strong technical support for the precise control, cost accounting and safe production of fluid metering in industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0029] First embodiment

[0030] See also Figure 1 , the present application provides a Coriolis mass flowmeter signal processing method based on a mixing algorithm, comprising the following steps:

[0031] Step 1: debug the drive modules associated with both sides of the pipeline, adjust the drive strength, and record the signal waveforms of the vibration signals on both sides of the pipeline, analyze and confirm the stability, and lock the optimal drive strength from the analysis and confirmation process. Specifically, the Coriolis mass flowmeter is equipped with a drive sensor that can generate a drive signal to cause the internal pipeline to vibrate and deflect. Based on the specific offset, the offset processing process is identified to confirm the mass flow rate and other characteristics of the corresponding liquid. During the vibration deflection process, the pipeline will generate a corresponding vibration signal. Based on the waveform change process of the corresponding vibration signal, the optimal drive strength is confirmed to ensure the accuracy of the subsequent phase difference confirmation process.

[0032] The specific method for confirming the optimal driving strength is as follows:

[0033] The preset driving strength of the driving module is calibrated as Q, and the debugging range is determined based on Q. The debugging range is (Q±0.3Q), that is, the interval range is: [Q-0.3Q, Q+0.3Q];

[0034] A driving intensity is selected as the execution intensity within the confirmed debugging range, and the signal waveforms of the vibration signals on both sides of the pipeline associated with the corresponding execution intensity are obtained, and the stability values ​​associated with the two groups of signal waveforms are confirmed: several peak points existing in a single signal waveform are confirmed in sequence, and the trend of the line segments before and after the peak point is opposite, the trend of the front line segment is a climbing trend, and the trend of the rear line segment is a descending trend. The waveform segments associated with adjacent peak points are confirmed, and the confirmed waveform segments are recorded as peak segments. The highest peak point and the lowest peak point are confirmed from the single peak segment. Based on the amplitude characteristics between the highest peak point and the lowest peak point, the difference amplitude is confirmed, and the confirmed difference amplitude is used as the waveform characteristic of the peak segment. The difference amplitude = the highest peak point amplitude - the lowest peak point amplitude;

[0035] Confirm the different waveform features associated with different waveform segments in a single signal waveform in turn. From the confirmed waveform features, confirm the waveform features with consistent values ​​and use them as standard features (under normal circumstances, there are only a few groups or one group or no waveforms with inconsistent amplitudes, and the amplitude changes of other waveforms should be consistent). If there are multiple groups of waveform features with consistent values, the waveform features with the largest number of consistent values ​​are used as standard features. The confirmed standard features are recorded as Bz, and the other waveform features are recorded as T i , where i represents different waveform segments, using: |Bz-T i |÷Bz=X i Confirm the defining feature T i , and then from several limiting features T associated with a single signal waveform i In the example, select the minimum value T i min, and T i min is used as the stability parameter of this signal waveform;

[0036] Using the same stability parameter confirmation method, confirm the stability parameters associated with another set of signal waveforms, average the stability parameters of the two sets of signal waveforms associated with the current execution strength, confirm the parameter mean, and use the parameter mean as the strength feature of the current execution strength;

[0037] Confirm the intensity features associated with different execution intensities in sequence, select the maximum value from the confirmed intensity features, and use the execution intensity associated with the maximum value as the optimal driving intensity. If there are multiple groups of execution intensities associated with the maximum value, randomly select one group of execution intensities as the optimal driving intensity.

[0038] Specifically, a set of debugging ranges is determined based on the confirmed driving strength and the set specific range, and parameters are selected based on the driving strength associated with the corresponding debugging range. The execution strength is confirmed from the selected specific parameters, and then the difference amplitude is associated and confirmed based on the change of the specific band associated with the corresponding execution strength.

[0039] Under different execution intensities, the waveform has different stability change characteristics. Therefore, the execution intensities with the most gentle stability change characteristics can be identified and used as the optimal execution intensity. After the optimal execution intensity is confirmed, it is executed to ensure the stable operation process of the corresponding Coriolis mass flowmeter and achieve the best operating effect.

[0040] Step 2: After the optimal drive strength is confirmed, the vibration signals associated with both sides of the pipeline are collected and mixed with the reference signal to confirm the cosine component and the sine component. Then, a unified confirmation is performed to identify the phase difference associated with the pipelines on both sides. Specifically, the vibration signal has the corresponding amplitude, frequency, and associated phase. Based on the corresponding parameter characteristics, the analytical formula associated with the corresponding signal can be confirmed. Then, combined with the specific processing flow, the relevant components are confirmed in sequence, and then the phase difference is specifically confirmed to ensure the relevant accuracy in the subsequent data measurement process;

[0041] The specific method for identifying the phase difference associated with the pipelines on both sides is as follows:

[0042] After the optimal driving strength is implemented, the frequency w and amplitude associated with the corresponding vibration signals on both sides of the pipeline are collected, and the expression for the corresponding vibration signal is generated: as well as , where A1 and A2 represent the amplitudes associated with the corresponding vibration signals, and where δ1 and δ2 represent the phases associated with the corresponding vibration signals;

[0043] The confirmed entry side signal Mixing processing is performed with the associated reference signals R(t) = sin(wt) and Q(t) = cos(wt) (that is, multiplication processing, using the "product-to-sum-difference" property of trigonometric functions to decompose the signal). R(t) can be understood as the standard in the vertical direction, and Q(t) can be understood as the standard in the horizontal direction. Multiplying S1(t) by R(t) yields: , divide this formula into: low-frequency component and high-frequency components , retain the low-frequency components , and recorded as the cosine component, filtering out the high-frequency frequency components. Specifically, the two sets of reference signals are two signals with the same frequency and a phase difference of 90°. They can be understood as reference benchmarks in terms of measurement. One is in the horizontal direction and the other is in the vertical direction. Their frequency w is completely consistent with the pipeline vibration frequency (guaranteed by the PLL phase-locked loop), just like using a "slow motion lens" with the same frequency as the pendulum swing to measure the pendulum deflection;

[0044] Multiply S1(t) and Q(t) in the same way and retain the resulting low-frequency component. , and record it as a sine component, and then use: Confirm the phase value δ1 associated with the corresponding vibration signal;

[0045] Then the exit side signal Perform mixing processing with the associated reference signals R(t) = sin(wt) and Q(t) = cos(wt), and simultaneously use the same processing method to determine the phase value δ2 associated with another set of vibration signals;

[0046] Based on the confirmed phase values ​​δ1 and δ2, the phase difference = (δ1-δ2) is used to lock the currently measured phase difference. The core principle of the Coriolis flowmeter is that the mass flow rate is proportional to the phase difference, just like "the weight of an object is proportional to the extension of a spring". This relationship is expressed by the formula: q m =K×Δϕ (phase difference). Frequency mixing "transfers" the phase information of the high-frequency vibration signal to the low-frequency DC component (sine / cosine component), solving the problem of difficult high-frequency signal measurement. The inverse tangent function is used to "restore" the phase value from the DC component, just like using the two sides of a right triangle to find the angle. The phase difference is linearly related to the mass flow rate. Through a calibrated proportional coefficient, a direct conversion is completed to complete the final output from "signal difference" to "flow data". Simply put, the whole process is like: measuring the elongation of a spring with a ruler (mixing and filtering to extract phase information) → calculating the elongation difference (phase difference) → calculating the object weight (flow rate) based on the "elongation-weight" relationship. Each step is based on clear physical principles and mathematical relationships.

[0047] Step 3: Define a set of monitoring periods, record the different phase differences associated with different moments in the monitoring period, and identify and display the associated mass flow values ​​based on the fluctuation characteristics of the phase difference values. The specific identification process is as follows:

[0048] Taking the current moment as the reference moment, define a set of monitoring periods. The monitoring period is a preset period, which is prepared by the operator in advance based on experience. Generally, it is set to 3 minutes. The different phase differences associated with different moments in the monitoring period are recorded in sequence. The minimum and maximum values ​​are selected from the recorded phase differences, and the difference range F between the minimum and maximum values ​​is recorded, where F = maximum value - minimum value.

[0049] And record the comparison process of the difference range F and the threshold Y1: If F≤Y1, it means that its fluctuation characteristics are gentle, and the mass flow value can be confirmed. Then, several groups of phase differences are averaged to confirm the phase difference mean J, and the following formula is used: K×J=mass flow rate. The mass flow value is confirmed and displayed directly, where Y1 is the preset value and K is the proportional coefficient (determined by the flow meter hardware parameters, such as pipe stiffness, fluid density, etc., obtained through calibration);

[0050] If F>Y1, it means that the confirmed phase difference fluctuates greatly on the timeline, and an unconfirmed signal is generated for display. The relevant maintenance personnel will perform maintenance and repair by themselves. The possible reason is that the corresponding pipeline collides with the corresponding cavity wall, resulting in different vibration signals, which will cause the phase difference to fluctuate greatly.

[0051] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0052] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A Coriolis mass flowmeter signal processing method based on a frequency mixing algorithm, characterized in that: The following steps are involved: Step 1: Debug the drive modules associated with both sides of the pipeline, adjust the drive strength, and record the waveforms of the vibration signals on both sides of the pipeline. Analyze and confirm the stability, and lock in the optimal drive strength from the analysis and confirmation process. Step 2: After the optimal drive strength is confirmed, the vibration signals associated with both sides of the pipeline are collected and mixed with the reference signal to confirm the cosine and sine components. Then, a unified confirmation is performed to identify the phase difference associated with the pipelines on both sides; Step 3: Define a set of monitoring periods, record the different phase differences associated with different moments in the monitoring period, and identify and display the associated mass flow values ​​based on the fluctuation characteristics of the phase difference values.

2. The signal processing method of a Coriolis mass flowmeter based on a frequency mixing algorithm according to claim 1, characterized in that: In step 1, the specific method of locking the optimal driving strength is: Calibrate the preset driving strength of the driving module as Q, and confirm the debugging range based on Q; A driving intensity is selected as the execution intensity within the confirmed debugging range, and the signal waveforms of the vibration signals on both sides of the pipeline associated with the corresponding execution intensity are obtained. The stability values ​​associated with the two sets of signal waveforms are confirmed: several peak points existing in a single signal waveform are confirmed in sequence, and the waveform segments associated with adjacent peak points are confirmed. The confirmed waveform segments are recorded as peak segments. The highest peak point and the lowest peak point are confirmed from the single peak segment. Based on the amplitude characteristics between the highest peak point and the lowest peak point, the difference amplitude is confirmed, and the confirmed difference amplitude is used as the waveform characteristic of the peak segment. The different waveform features associated with different waveform segments in a single signal waveform are confirmed in sequence. From the confirmed waveform features, the waveform features with consistent values ​​are confirmed and used as standard features. If there are multiple groups of waveform features with consistent values, the waveform features with the largest number of consistent values ​​are used as standard features. The confirmed standard features are recorded as Bz, and the other waveform features are recorded as T. i , where i represents different waveform segments, using: |Bz-T i |÷Bz=X i Confirm the defining feature T i , and then from several limiting features T associated with a single signal waveform i In the example, select the minimum value T i min, and T i min is used as the stability parameter of this signal waveform; The same stability parameter confirmation method is used to confirm the stability parameters associated with another set of signal waveforms, and the stability parameters of the two sets of signal waveforms associated with the current execution intensity are averaged to confirm the parameter mean, which is used as the intensity feature of the current execution intensity.

3. The signal processing method of a Coriolis mass flowmeter based on a frequency mixing algorithm according to claim 2, characterized in that: Its debugging range is (Q±0.3Q).

4. The signal processing method of a Coriolis mass flowmeter based on a frequency mixing algorithm according to claim 2, characterized in that: The intensity features associated with different execution intensities are confirmed in turn, and the maximum value is selected from the confirmed different intensity features. The execution intensity associated with the maximum value is used as the optimal driving intensity. If there are multiple groups of execution intensities associated with the maximum value, one group of execution intensities is randomly selected as the optimal driving intensity.

5. The signal processing method of a Coriolis mass flowmeter based on a frequency mixing algorithm according to claim 1, characterized in that: In step 2, the specific method for confirming the phase difference associated with the pipelines on both sides is: After the optimal driving strength is implemented, the frequency w and amplitude associated with the corresponding vibration signals on both sides of the pipeline are collected, and the expression for the corresponding vibration signal is generated: as well as , where A1 and A2 represent the amplitudes associated with the corresponding vibration signals, and where δ1 and δ2 represent the phases associated with the corresponding vibration signals; The confirmed entry side signal Mixing is performed with the associated reference signals R(t) = sin(wt) and Q(t) = cos(wt), where R(t) can be understood as the standard in the vertical direction and Q(t) as the standard in the horizontal direction. Multiplying S1(t) by R(t) yields: , divide this formula into: low-frequency component and high-frequency components , retain the low-frequency components , and record it as cosine component, filtering out high-frequency frequency components; Multiply S1(t) and Q(t) in the same way and retain the resulting low-frequency component. , and record it as a sine component, and then use: Confirm the phase value δ1 associated with the corresponding vibration signal; Then the exit side signal Perform mixing processing with the associated reference signals R(t) = sin(wt) and Q(t) = cos(wt), and simultaneously use the same processing method to determine the phase value δ2 associated with another set of vibration signals; Based on the confirmed phase value δ1 and phase value δ2, the phase difference = (δ1-δ2) is used to lock the phase difference currently measured.

6. The signal processing method of a Coriolis mass flowmeter based on a frequency mixing algorithm according to claim 1, characterized in that: The specific process of step 3, identifying the mass flow value is as follows: Taking the current moment as the reference moment, define a set of monitoring periods, where the monitoring period is a preset period. Record the different phase differences associated with different moments in the monitoring period in sequence, select the minimum and maximum values ​​from the recorded phase differences, and record the difference range F between the minimum and maximum values, where F = maximum value - minimum value. And record the comparison process of the difference range F and the threshold Y1: if F≤Y1, perform average processing on several groups of phase differences to confirm the phase difference mean J, and use: K×J=mass flow rate to confirm its mass flow rate value and display it directly, where Y1 is the preset value and K is the proportional coefficient.

7. The signal processing method of a Coriolis mass flowmeter based on a frequency mixing algorithm according to claim 5, characterized in that: If F>Y1, an unconfirmable signal is generated for display.

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