Self-diagnosis method for a gas ultrasonic flowmeter

Abstract

The present application relates to the technical field of flowmeter self-diagnosis, in particular to a self-diagnosis method of a gas ultrasonic flowmeter. It comprises the following steps: S1, initializing and calibrating the gas ultrasonic flowmeter; S2, collecting key parameters from each acoustic channel of the gas ultrasonic flowmeter in real time, and recording the pulse usage rate of each acoustic channel and the average time interval of system processing; S3, comparing the current collected data with the preset standard, and evaluating the indicators of key parameters to identify potential problems; S4, generating corresponding status indication and alarm according to the evaluation result. The present application designs to collect and analyze key parameters such as signal-to-noise ratio, automatic gain control level, axial flow rate, sound velocity and flow rate regularly, and compares them with the preset standard or historical data, so that the method can discover and identify potential problems in time, such as signal strength decline, acoustic channel blockage, temperature change influence on measurement results, etc.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flow meter self-diagnosis, in particular to a self-diagnosis method for gas ultrasonic flow meter. BACKGROUND

[0002] In industrial processes, accurate flow measurement is crucial for process control, product quality assurance, and safe operation. However, due to environmental factors such as temperature changes, pressure fluctuations, mechanical wear and tear, or sediment accumulation, the performance of ultrasonic flow meters may degrade over time, leading to increased measurement errors. When a flow meter fails, it often needs to be shut down for inspection and repair, which not only causes production interruption but also incurs additional costs. Traditional maintenance methods usually rely on periodic manual inspection or intervention only after obvious failures occur, which is time-consuming and labor-intensive and may not cover all potential problems. Therefore, a self-diagnosis method for gas ultrasonic flow meter is provided. SUMMARY

[0003] The purpose of the present application is to provide a self-diagnosis method for gas ultrasonic flow meter to solve the problems of low diagnosis accuracy, long downtime, and high maintenance cost of traditional flow meters mentioned in the background.

[0004] To achieve the above-mentioned purpose, the present application provides a self-diagnosis method for gas ultrasonic flow meter, comprising the following steps:

[0005] S1, initializing and calibrating the gas ultrasonic flow meter;

[0006] S2, collecting real-time key parameters from each channel of the gas ultrasonic flow meter and recording the pulse usage rate and average time interval of system processing for each channel;

[0007] S3, comparing the current collected data with the preset standard, evaluating the indicators of key parameters to identify potential problems;

[0008] S4, generating corresponding status indication and alarm according to the evaluation result.

[0009] As a further improvement of the present technical solution, in S2, collecting real-time key parameters from each channel of the gas ultrasonic flow meter and recording the pulse usage rate and average time interval of system processing for each channel, comprising the following steps:

[0010] S2.1, collecting real-time data of signal-to-noise ratio, automatic gain control level, and axial flow speed from each channel;

[0011] S2.2, recording the sound speed and actual measured flow speed of each channel;

[0012] S2.3, measure and record the average sound velocity of all channels;

[0013] S2.4, monitor and record the average time interval used in signal processing;

[0014] S2.5, track and record the proportion of the number of valid pulses received by each channel to the total number of transmitted pulses.

[0015] As a further improvement of the technical solution, in S3, by comparing the current collected data with the preset standard, the indicators of key parameters are evaluated to identify potential problems, including the following steps:

[0016] S3.1, check if the signal-to-noise ratio is lower than the threshold m;

[0017] S3.2, analyze the trend of automatic gain control level change by linear regression, and determine if there is a signal strength decrease;

[0018] S3.3, evaluate the consistency of flow rate and sound velocity between channels by non-parametric test method, identify potential blockage problems, and find the influence of temperature;

[0019] S3.4, review the average time interval by autoregressive integrated moving average model;

[0020] S3.5, check the pulse reception rate.

[0021] As a further improvement of the technical solution, in S3.2, by analyzing the trend of automatic gain control level change by linear regression, it is determined whether there is a signal strength decrease, including the following steps:

[0022] S3.21, preprocess the collected automatic gain control level data, if the data is not uniformly sampled, resample the data to a fixed time interval;

[0023] S3.22, construct a linear regression model based on the preprocessed automatic gain control level data and perform model evaluation;

[0024] S3.23, according to the sign of the slope in the linear regression model, determine the trend of the automatic gain control level, if the slope is negative, it means that the signal strength is decreasing.

[0025] As a further improvement of the technical solution, in S3, in S3.23, the linear regression model is:

[0026] ;

[0027] where, indicates the automatic gain control level; indicates the time; Represents the intercept term; express Automatic gain control level increases by one unit. The change in; This indicates the error term.

[0028] As a further improvement to this technical solution, in step S3.3, the consistency of flow velocity and sound velocity between each channel is evaluated using a non-parametric testing method, including the following steps:

[0029] S3.31. Preprocess the collected flow velocity and sound velocity data;

[0030] S3.32. Group the flow velocity and sound velocity data according to different sound channels;

[0031] S3.33. Merge the data from all channels into a list, sort the merged data in ascending order, and assign a rank to each data point;

[0032] S3.34. For each channel, calculate the sum of the ranks of all data points;

[0033] S3.35, Calculation Statistic;

[0034] S3.36. Select the significance level;

[0035] S3.37. Based on the degrees of freedom and significance level, find the critical value in the Kruskal-Wallis H distribution table;

[0036] S3.38, If If the statistic is greater than the critical value, it indicates that the flow rate or sound speed is inconsistent between different channels.

[0037] As a further improvement to this technical solution, in S3.35, the calculated statistic is as follows:

[0038] ;

[0039] in, It represents a statistic used to assess whether there are significant differences in the medians of multiple independent samples; This represents the total number of data points across all channels. Indicates the number of vocal tracts; Indicates the first The rank sum of each channel; Indicates the first Number of data points per channel; Index representing the audio channel.

[0040] As a further improvement to this technical solution, step S3.4 involves reviewing the average time interval using an autoregressive integral moving average model, including the following steps:

[0041] S3.41. Preprocess the collected average time interval data and use the ADF test to check whether the time series is stationary;

[0042] S3.42 Determine the parameters of the autoregressive integral moving average model;

[0043] S3.43. Use defined model parameters to fit the autoregressive integral moving average model;

[0044] S3.44. Use the well-fitted autoregressive integral moving average model to predict future time intervals;

[0045] S3.45. Analyze the prediction results to see if there are obvious upward or downward trends. If the prediction results show a significant change in the time interval, it indicates that there is a problem with the system response speed.

[0046] As a further improvement to this technical solution, in S3.43, the autoregressive integral moving average model is as follows:

[0047] ;

[0048] in, Indicates the time series at time points The value; Represents a constant term; Represents the autoregressive coefficient; Indicates the moving average coefficient; This represents the white noise error term; Indicates a point in time; Indicates the number of autoregressive terms; Indicates the number of terms in the moving average; Indicates the time series at time points The value; Indicates the time series at time points value; Indicates the time series at time points The value; Indicates a point in time The prediction error; Indicates a point in time The prediction error; Indicates a point in time The prediction error.

[0049] As a further improvement to this technical solution, in step S4, generating corresponding status indications and alarms based on the evaluation results includes the following steps:

[0050] S4.1 Set the threshold for signal-to-noise ratio and automatic gain control level, set a maximum allowable difference between flow velocity and sound velocity, set the response time range, and set the minimum pulse reception rate;

[0051] S4.2 When all parameters do not exceed the threshold and are within the safe range, the control system of the gas ultrasonic flow meter will display green. If the parameters deviate significantly from the standard value, it indicates that there is a fault in the system, and the control system of the gas ultrasonic flow meter will immediately display a red warning sign.

[0052] S4.3 An alarm is triggered by flashing LEDs on the flow meter.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] 1. The self-diagnostic method of this ultrasonic gas flow meter involves periodically collecting and analyzing key parameters, such as signal-to-noise ratio, automatic gain control level, axial velocity, sound velocity, and flow rate, and comparing them with preset standards or historical data. This method can promptly detect and identify potential problems, such as signal strength degradation, channel blockage, and the impact of temperature changes on measurement results. This allows operators to take preventative maintenance measures, avoiding unexpected downtime due to malfunctions and improving the overall system stability and reliability.

[0055] 2. The self-diagnostic method of this ultrasonic gas flow meter employs advanced statistical methods (such as linear regression analysis, Kruskal-Wallis H test, and autoregressive integral moving average model) to evaluate the trends and consistency of various parameters, enabling the flow meter to identify potential fault signs at an early stage. This not only helps reduce the possibility of large-scale failures due to undetected problems but also guides technicians to more accurately locate the problem, reducing unnecessary inspection work and effectively lowering maintenance costs and time consumption. Furthermore, through status indicators and alarm mechanisms, relevant personnel can be immediately notified to take action, preventing small problems from escalating into larger ones, further saving resources. Attached Figure Description

[0056] Figure 1 This is a flowchart of the overall method of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0058] Example: Please refer to Figure 1 As shown, this embodiment provides a self-diagnostic method for a gas ultrasonic flow meter, including the following steps:

[0059] S1. Initialize and calibrate the ultrasonic gas flow meter to ensure that the flow meter is in normal working condition before starting self-diagnosis.

[0060] The process begins with system initialization, including hardware checks and software loading. The status of the internal memory is checked to ensure that all data is recorded correctly. The working status of the ultrasonic transmitter and receiver is checked to ensure that they are working properly. The communication lines between the various sensors are tested to ensure that they are intact. For each channel (if there is a multi-channel design), tests are performed separately to confirm its performance.

[0061] S2. Collect key parameters from each channel of the ultrasonic gas flow meter in real time, and record the pulse utilization rate and average time interval of system processing for each channel.

[0062] Key parameters include signal-to-noise ratio, automatic gain control level, axial flow velocity, sound velocity, and flow velocity.

[0063] In this embodiment, key parameters are acquired in real time from each channel of the ultrasonic gas flow meter, and the pulse utilization rate and average time interval of system processing for each channel are recorded, including the following steps:

[0064] S2.1 Collect real-time data on signal-to-noise ratio (SNR), automatic gain control (AGC) level, and axial flow velocity from each channel;

[0065] S2.2 Record the sound velocity of each channel and the actual measured flow velocity;

[0066] S2.3 Measure and record the average sound velocity between all channels;

[0067] S2.4 Monitor and record the average time interval used during signal processing;

[0068] S2.5. Track and record the proportion (usage rate) of the number of valid pulses received by each channel to the total number of transmitted pulses.

[0069] S3. Compare the currently collected data with the preset standards and evaluate the indicators of key parameters to identify potential problems;

[0070] Key parameters include signal-to-noise ratio, flow velocity consistency, and sound velocity variation.

[0071] In this embodiment, the key parameters are evaluated to identify potential problems by comparing the currently collected data with preset standards, including the following steps:

[0072] S3.1 Check if the signal-to-noise ratio is lower than the threshold m, which may be a sign of noise interference or sensor failure;

[0073] S3.2. Analyze the trend of automatic gain control level changes through linear regression to determine if there is a decrease in signal strength;

[0074] Furthermore, the principle of linear regression analysis is to describe the relationship between independent and dependent variables by fitting a linear equation, thereby predicting the value of the dependent variable and assessing the strength and direction of this relationship. By monitoring changes in AGC levels over time, a trend of declining signal reception quality can be identified early, which may be due to sensor aging, dirt accumulation, increased environmental noise, or other factors. When a continuous decline in AGC levels is observed, it indicates that the flow meter may need maintenance or cleaning, which can help develop more accurate preventative maintenance plans and reduce unplanned downtime. Good signal quality is crucial for ensuring accurate measurement by ultrasonic flow meters. If the AGC level declines, it means that the original signal weakens, which will directly affect the measurement accuracy of the flow meter. Stable AGC levels help ensure stable system operation. When the AGC level fluctuates greatly, it may indicate the presence of unstable factors in the system.

[0075] To determine if there is a decrease in signal strength, linear regression analysis is used to analyze the trend of automatic gain control (AGC) level changes. This includes the following steps:

[0076] S3.21. Preprocess the collected automatic gain control level data. If the data is not uniformly sampled, resample the data to a fixed time interval, such as one data point per minute or per second.

[0077] S3.22. Construct a linear regression model based on the preprocessed automatic gain control level data, and evaluate the model (calculate the slope). ,if A negative value indicates that the automatic gain control level decreases over time. A positive value indicates that the automatic gain control level increases over time.

[0078] Furthermore, the linear regression model is as follows:

[0079] ;

[0080] in, Indicates the level of automatic gain control; Indicates time; This represents the intercept term, i.e. Automatic gain control level Expected value; express Automatic gain control level increases by one unit. The change in slope; This represents the error term, indicating random fluctuations that the model cannot explain;

[0081] S3.23. Based on the sign of the slope in the linear regression model, determine the trend of the automatic gain control level. If the slope is negative, it indicates that the signal strength is decreasing.

[0082] S3.3. Evaluate the consistency of flow rate and sound velocity between channels using non-parametric testing methods (Kruskal-Wallis H test), identify potential blockages or other physical obstacles, and look for existing temperature effects or changes in material properties.

[0083] Furthermore, nonparametric tests are based on the rank of the data rather than the raw values. They assess differences or correlations by comparing the rank distributions between samples, without relying on specific distributional assumptions about the data. Nonparametric tests do not assume that the data follows a specific probability distribution (such as a normal distribution), thus they are applicable to various types of flow meter data, including those that do not conform to a normal distribution. This makes the Kruskal-Wallis H test more flexible and capable of handling various complex situations that may arise in practical applications. The Kruskal-Wallis H test can compare the medians of three or more independent samples simultaneously, which is particularly important for multi-channel flow meters. It can be used to detect whether there are significant differences between different channels, helping to identify potential local blockages, sensor malfunctions, or other influencing factors. The results of the Kruskal-Wallis H test can be represented by a simple statistic H and its corresponding p-value, which helps to quickly determine the level of consistency between different channels. When the H statistic exceeds a critical value, it indicates that at least one channel has a significant difference from the other channels, suggesting that further investigation is needed to identify the problem in that channel.

[0084] The consistency of flow rate and sound velocity across channels was evaluated using a non-parametric test method (Kruskal-Wallis H test), including the following steps:

[0085] S3.31. Preprocess the collected flow velocity and sound velocity data, including removing outliers and missing values, to ensure data quality;

[0086] S3.32. Group the flow velocity and sound velocity data according to different sound channels. For example, if the flow meter has three sound channels (A, B, C), collect the flow velocity data and sound velocity data of these three sound channels respectively.

[0087] S3.33. Merge all channel data into a list, sort the merged data in ascending order, and assign a rank to each data point (rank refers to the position of each observation in a set of data after being sorted in ascending or descending order). If there are identical data points (i.e., the same flow rate value), assign an average rank.

[0088] S3.34. For each channel, calculate the sum of the ranks of all data points. , , It is the first The first in the 1st channel The rank of each data point;

[0089] S3.35, Calculation Statistic;

[0090] S3.36. Select a significance level, such as 0.05;

[0091] S3.37. Based on the degrees of freedom (the number of independent variables that can change freely when calculating a statistic, reflecting the amount of information in the data that can be used to estimate unknown parameters; the degrees of freedom equal to the number of vocal tracts minus 1) and the significance level (such as 0.05, used to determine whether the result of the statistical test is statistically significant), find the critical value in the Kruskal-Wallis H distribution table.

[0092] S3.38, If If the statistic is greater than the critical value, it indicates that the flow rate or sound speed is inconsistent between different channels;

[0093] S3.4 Review the average time interval using the Autoregressive Integrated Moving Average (ARIMA) model to ensure that the system response speed meets expectations;

[0094] Furthermore, the ARIMA model works by combining autoregressive (AR), differencing (I), and moving average (MA) techniques to fit time-series data, thereby capturing trends, seasonality, and random fluctuations in the data for predicting future values. The ARIMA model is specifically designed for processing time-series data, capturing trends, seasonality, and random fluctuations over time, making it ideal for analyzing time interval data in signal processing within flowmeters, which typically exhibit time dependence. Once the ARIMA model is appropriately fitted to historical data, it can be used to predict future average time intervals; this predictive ability helps identify system response speeds. By analyzing changing trends, the ARIMA model can identify potential problems early, such as increased processor load or decreased software performance. Comparing actual observations with ARIMA model predictions allows for rapid detection of anomalies. A significant deviation of actual time intervals from model predictions could signal system problems, such as hardware failures or software errors. In-depth analysis of time interval data helps pinpoint problems more precisely. For example, a continuous increase in time intervals might indicate insufficient system processing power or resource bottlenecks; conversely, a sudden decrease in time intervals could signify changes in system configuration or the implementation of new optimization measures.

[0095] Examining the average time interval using an autoregressive integral moving average (ARIMA) model includes the following steps:

[0096] S3.41. Preprocess the collected average time interval data, including removing outliers and missing values, to ensure data quality. If the data is not uniformly sampled, it needs to be resampled to a fixed time interval, such as one data point per minute or per second, and the ADF test method is used to check whether the time series is stationary (the ADF test is a statistical test to determine whether a time series has a unit root, thereby determining whether the time series is stationary).

[0097] S3.42. Determine the parameters of the autoregressive integral moving average model, including the autocorrelation function (which describes the correlation between the time series and its lagged terms) and the partial autocorrelation function (which describes the direct correlation between the time series and its lagged terms and controls the influence of the intermediate lagged terms), the difference order d, the autoregressive order p, and the moving average order q.

[0098] S3.43. Use defined model parameters to fit the autoregressive integral moving average model;

[0099] Furthermore, the autoregressive integral moving average model is as follows:

[0100] ;

[0101] in, Indicates the time series at time points The value; Represents a constant term; Represents the autoregressive coefficient; Indicates the moving average coefficient; This represents the white noise error term; Indicates a point in time; Indicates the number of autoregressive terms; Indicates the number of terms in the moving average; Indicates the time series at time points The value; Indicates the time series at time points value; Indicates the time series at time points The value; Indicates a point in time The prediction error; Indicates a point in time The prediction error; Indicates a point in time The prediction error;

[0102] S3.44. Use the well-fitted autoregressive integral moving average model to predict future time intervals;

[0103] S3.45. Analyze the prediction results to see if there are obvious upward or downward trends. If the prediction results show a significant change in the time interval, it indicates that there is a problem with the system response speed.

[0104] S3.5 Check the pulse reception rate; a low reception rate may indicate a problem with signal transmission.

[0105] S4. Generate corresponding status indicators and alarms based on the evaluation results;

[0106] In this embodiment, generating corresponding status indicators and alarms based on the evaluation results includes the following steps:

[0107] S4.1 Set the threshold for signal-to-noise ratio and automatic gain control level, set a maximum allowable difference between flow velocity and sound velocity, set the response time range, and set the minimum pulse reception rate;

[0108] S4.2 When all parameters do not exceed the threshold and are within the safe range, the control system of the gas ultrasonic flow meter will display green. If the parameters deviate significantly from the standard value, it indicates that there is a fault in the system, and the control system of the gas ultrasonic flow meter will immediately display a red warning sign.

[0109] S4.3 An alarm is triggered by flashing LEDs on the flow meter.

[0110] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A self-diagnostic method for a gas ultrasonic flow meter, characterized in that, Includes the following steps: S1. Initialize and calibrate the ultrasonic gas flow meter; S2. Collect key parameters from each channel of the ultrasonic gas flow meter in real time, and record the pulse utilization rate and average time interval of system processing for each channel. S3. Compare the currently collected data with the preset standards and evaluate the indicators of key parameters to identify potential problems; S3 includes the following steps: S3.1 Check if the signal-to-noise ratio is lower than the threshold m; S3.

2. Analyze the trend of automatic gain control level changes through linear regression to determine if there is a decrease in signal strength; S3.

3. Evaluate the consistency of flow rate and sound velocity between channels using non-parametric testing methods, identify potential blockage problems, and find out the existing temperature effects; In step S3.3, the consistency of flow velocity and sound velocity between channels is evaluated using a non-parametric testing method, including the following steps: S3.

31. Preprocess the collected flow velocity and sound velocity data; S3.

32. Group the flow velocity and sound velocity data according to different sound channels; S3.

33. Merge the data from all channels into a list, sort the merged data in ascending order, and assign a rank to each data point; S3.

34. For each channel, calculate the sum of the ranks of all data points; S3.35, Calculation Statistic; S3.

36. Select the significance level; S3.

37. Based on the degrees of freedom and significance level, find the critical value in the Kruskal-Wallis H distribution table; S3.38, If If the statistic is greater than the critical value, it indicates that the flow rate or sound speed is inconsistent between different channels; In S3.35, the calculated statistic is as follows: ； in, It represents a statistic used to assess whether there are significant differences in the medians of multiple independent samples; This represents the total number of data points across all channels. Indicates the number of vocal tracts; Indicates the first The rank sum of each channel; Indicates the first Number of data points per channel; Index representing the vocal tract; S3.4 Review the average time interval using an autoregressive integral moving average model; S3.5, Check the pulse reception rate; S4. Generate corresponding status indications and alarms based on the evaluation results.

2. The self-diagnostic method for a gas ultrasonic flow meter according to claim 1, characterized in that: In step S2, key parameters are acquired in real time from each channel of the ultrasonic gas flow meter, and the pulse utilization rate and average time interval of system processing for each channel are recorded, including the following steps: S2.1 Collect real-time data on signal-to-noise ratio, automatic gain control level, and axial flow velocity from each channel; S2.2 Record the sound velocity of each channel and the actual measured flow velocity; S2.3 Measure and record the average sound velocity between all channels; S2.4 Monitor and record the average time interval used during signal processing; S2.5 Track and record the proportion of valid pulses received by each channel to the total number of transmitted pulses.

3. The self-diagnostic method for a gas ultrasonic flow meter according to claim 2, characterized in that: In step S3.2, the process of determining whether there is a decrease in signal strength by analyzing the trend of automatic gain control level changes through linear regression analysis also includes the following steps: S3.

21. Preprocess the collected automatic gain control level data. If the data is not uniformly sampled, resample the data to a fixed time interval. S3.

22. Construct a linear regression model based on the preprocessed automatic gain control level data and evaluate the model. S3.

23. Based on the sign of the slope in the linear regression model, determine the trend of the automatic gain control level. If the slope is negative, it indicates that the signal strength is decreasing.

4. The self-diagnostic method for a gas ultrasonic flow meter according to claim 3, characterized in that: In S3, specifically in S3.23, the linear regression model is: ； in, Indicates the level of automatic gain control; Indicates time; Represents the intercept term; express Automatic gain control level increases by one unit. The change in; This indicates the error term.

5. The self-diagnostic method for a gas ultrasonic flow meter according to claim 4, characterized in that: In step S3.4, reviewing the average time interval using an autoregressive integral moving average model further includes the following steps: S3.

41. Preprocess the collected average time interval data and use the ADF test to check whether the time series is stationary; S3.42 Determine the parameters of the autoregressive integral moving average model; S3.

43. Use defined model parameters to fit the autoregressive integral moving average model; S3.

44. Use the well-fitted autoregressive integral moving average model to predict future time intervals; S3.

45. Analyze the prediction results to see if there are obvious upward or downward trends. If the prediction results show a significant change in the time interval, it indicates that there is a problem with the system response speed.

6. The self-diagnostic method for a gas ultrasonic flow meter according to claim 5, characterized in that: In S3.43, the autoregressive integral moving average model is: ； in, Indicates the time series at time points The value; Represents a constant term; Represents the autoregressive coefficient; Indicates the moving average coefficient; This represents the white noise error term; Indicates a point in time; Indicates the number of autoregressive terms; Indicates the number of terms in the moving average; Indicates the time series at time points The value; Indicates the time series at time points value; Indicates the time series at time points The value; Indicates a point in time The prediction error; Indicates a point in time The prediction error; Indicates a point in time The prediction error.

7. The self-diagnostic method for a gas ultrasonic flowmeter according to claim 6, characterized in that: In step S4, corresponding status indications and alarms are generated based on the evaluation results, including the following steps: S4.1 Set the threshold for signal-to-noise ratio and automatic gain control level, set a maximum allowable difference between flow velocity and sound velocity, set the response time range, and set the minimum pulse reception rate; S4.2 When all parameters do not exceed the threshold and are within the safe range, the control system of the gas ultrasonic flow meter will display green. If the parameters deviate significantly from the standard value, it indicates that there is a fault in the system, and the control system of the gas ultrasonic flow meter will immediately display a red warning sign. S4.3 An alarm is triggered by flashing LEDs on the flow meter.

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