Self-diagnosis method of gas ultrasonic flowmeter

Abstract

The invention relates to the technical field of flow meter self-diagnosis, in particular to a self-diagnosis method of a gas ultrasonic flow meter. The method comprises the following steps: S1, initializing and calibrating the gas ultrasonic flowmeter; s2, key parameters are collected from each sound channel of the gas ultrasonic flowmeter in real time, and the pulse utilization rate of each sound channel and the average time interval of system processing are recorded; s3, comparing the currently collected data with a preset standard, and evaluating indexes of the key parameters to identify potential problems; and S4, generating corresponding state indication and alarm according to an evaluation result. According to the design, key parameters such as the signal-to-noise ratio, the automatic gain control level, the axial flow velocity, the sound velocity and the flow velocity are collected and analyzed regularly and compared with preset standards or historical data, and the method can find and recognize potential problems in time. For example, signal strength decline, sound channel blockage, influence of temperature change on measurement results, and the like.

Description

Technical Field

[0001] The invention relates to the technical field of flow meter self-diagnosis, and in particular to a self-diagnosis method for a gas ultrasonic flow meter. Background Art

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

[0003] The object of the present invention is to provide a self-diagnosis method for a gas ultrasonic flowmeter to solve the problems of low diagnostic accuracy, long downtime and high maintenance cost of the traditional flowmeter mentioned in the above background technology.

[0004] To achieve the above object, the present invention provides a self-diagnosis method for a gas ultrasonic flow meter, comprising the following steps: S1. Initialize and calibrate the gas ultrasonic flow meter; S2. Collect key parameters from each channel of the gas ultrasonic flow meter in real time, and record the pulse usage rate of each channel and the average time interval of system processing; S3. Compare the currently collected data with the preset standards and evaluate the indicators of key parameters to identify potential problems; S4. Generate corresponding status indications and alarms based on the evaluation results.

[0005] As a further improvement of the technical solution, in S2, key parameters are collected from each channel of the gas ultrasonic flowmeter in real time, and the pulse usage rate of each channel and the average time interval of system processing are recorded, including the following steps: S2.1. Collect real-time data of 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 speed of sound between all channels; S2.4. Monitor and record the average time interval used in the signal processing process; S2.5. Track and record the ratio of the number of valid pulses received by each channel to the total number of pulses sent.

[0006] As a further improvement of the technical solution, in S3, comparing the currently collected data with the preset standard and evaluating the indicators of key parameters to identify potential problems includes the following steps: S3.1, check whether the signal-to-noise ratio is lower than the threshold m; S3.2. Analyze the change trend of the automatic gain control level through linear regression to determine whether there is a decrease in signal strength; S3.3. Evaluate the consistency of flow and sound velocity between channels through non-parametric test methods, identify potential blockage problems, and find out the temperature effects. S3.4, review the average time interval through the autoregressive integrated moving average model; S3.5. Check the pulse reception rate.

[0007] As a further improvement of the technical solution, in S3.2, the automatic gain control level change trend is analyzed by linear regression to determine whether there is a decrease in signal strength, including the following steps: S3.21, pre-processing the collected automatic gain control level data, if the data is not uniformly sampled, re-sampling the data to a fixed time interval; S3.22, constructing a linear regression model based on the preprocessed automatic gain control level data and performing model evaluation; S3.23. Determine the trend of the automatic gain control level based on the sign of the slope in the linear regression model. If the slope is negative, it means that the signal strength is decreasing.

[0008] As a further improvement of the technical solution, in S3, in S3.23, the linear regression model is: ; in, Indicates the automatic gain control level; Indicates time; represents the intercept term; express Each unit increase in the automatic gain control level The amount of change; represents the error term.

[0009] As a further improvement of the technical solution, in S3.3, the consistency of flow velocity and sound velocity between the channels is evaluated by a non-parametric test method, including the following steps: S3.31, preprocessing the collected flow velocity and sound velocity data; S3.32, grouping the flow velocity and sound velocity data according to different sound channels; S3.33, merge the data of 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, calculating the sum of the ranks of all data points; S3.35. Calculation Statistics; S3.36. Select the significance level; S3.37. Find the critical value in the Kruskal-Wallis H distribution table based on the degrees of freedom and significance level; S3.38 If If the statistic is greater than the critical value, it means that the flow velocity or sound speed between different channels is inconsistent.

[0010] As a further improvement of the technical solution, in S3.35, the calculated statistic is: ; in, It indicates the statistic for evaluating whether there are significant differences between the medians of multiple independent samples; Represents the total number of data points for all channels; Indicates the number of channels; Indicates The rank of the channels; Indicates The number of data points per channel; Indicates the index of the channel.

[0011] As a further improvement of the technical solution, in S3.4, reviewing the average time interval by an autoregressive integrated moving average model comprises the following steps: S3.41. Preprocess the collected average time interval data and use the ADF test method to check whether the time series is stable; S3.42, determine the autoregressive integrated moving average model parameters; S3.43, using the determined model parameters to fit the autoregressive integrated moving average model; S3.44, use the fitted autoregressive integrated moving average model to predict future time intervals; S3.45. Analyze the prediction results to see if there are obvious upward and downward trends. If the prediction results show significant changes in the time interval, it means that there is a problem with the system response speed.

[0012] As a further improvement of the technical solution, in S3.43, the autoregressive integrated moving average model is: ; in, Represents a time series at a point in time The value of represents a constant term; represents the autoregressive coefficient; represents the moving average coefficient; represents the white noise error term; Indicates a point in time; represents the number of autoregressive terms; represents the number of moving average terms; Represents a time series at a point in time The value of Represents a time series at a point in time value; Represents a time series at a point in time The value of Indicates at a point in time The prediction error of Indicates at a point in time The prediction error of Indicates at a point in time The prediction error of .

[0013] As a further improvement of the technical solution, in S4, generating corresponding status indications and alarms according to the evaluation results includes the following steps: S4.1, set the threshold of signal-to-noise ratio and automatic gain control level, set a maximum allowable flow velocity and sound velocity difference, set the response time range, and set the minimum pulse reception rate; S4.2. When all parameters do not exceed the threshold value and are within the safe range, the control system of the gas ultrasonic flow meter will display green. If the parameters deviate seriously 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. The LED light on the flow meter flashes to sound an alarm.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In the self-diagnosis method of the gas ultrasonic flow meter, by regularly collecting and analyzing key parameters such as signal-to-noise ratio, automatic gain control level, axial flow velocity, sound velocity and flow rate, and comparing them with preset standards or historical data, the method can timely discover and identify potential problems. For example, signal strength drop, sound channel blockage, the impact of temperature changes on measurement results, etc., allowing operators to take preventive maintenance measures to avoid unexpected downtime due to faults, and improve the working stability and reliability of the entire system.

[0015] 2. The self-diagnosis method of the gas ultrasonic flowmeter uses advanced statistical methods (such as linear regression analysis, Kruskal-Wallis H test, autoregressive integral moving average model) to evaluate the trend and consistency of various parameters, so that the flowmeter can identify possible signs of failure at an early stage. This not only helps to reduce the possibility of large-scale failures due to undetected problems, but also guides technicians to locate the problem more accurately and reduce unnecessary inspection work, thereby effectively reducing maintenance costs and time consumption. In addition, through the status indication and alarm mechanism, relevant personnel can be notified immediately to take action to prevent small problems from turning into big problems, further saving resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0018] Example: See Figure 1 As shown, this embodiment provides a self-diagnosis method for a gas ultrasonic flow meter, comprising the following steps: S1. Initialize and calibrate the gas ultrasonic flow meter to ensure that the flow meter is in normal working condition before starting self-diagnosis; First, the system is initialized, including hardware inspection, software loading, etc. The status of the internal memory is checked to ensure that all data records are normal; the working status of the ultrasonic transmitter and receiver is detected to see if they are normal, and the communication lines between the sensors are tested to see if they are intact. For each channel (if there is a multi-channel design), tests are performed separately to confirm its working performance; S2. Collect key parameters from each channel of the gas ultrasonic flow meter in real time, and record the pulse usage rate of each channel and the average time interval of system processing; Among them, the key parameters include signal-to-noise ratio, automatic gain control level, axial flow velocity, sound velocity and flow rate; In this embodiment, key parameters are collected from each channel of the gas ultrasonic flow meter in real time, and the pulse usage rate of each channel and the average time interval of system processing are recorded, including the following steps: S2.1. Collect real-time data of signal-to-noise ratio (SNR), automatic gain control (AGC) 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 speed of sound between all channels; S2.4. Monitor and record the average time interval used in the signal processing process; S2.5. Track and record the ratio of the number of valid pulses received by each channel to the total number of pulses sent (utilization rate).

[0019] S3. Compare the currently collected data with the preset standards and evaluate the indicators of key parameters to identify potential problems; Among them, the key parameters include signal-to-noise ratio, flow rate consistency, and sound velocity variation; In this embodiment, comparing the currently collected data with the preset standard and evaluating the indicators of key parameters to identify potential problems includes the following steps: S3.1, check whether the signal-to-noise ratio is lower than the threshold m, which may be a sign of noise interference or sensor failure; S3.2. Analyze the change trend of the automatic gain control level through linear regression to determine whether there is a decrease in signal strength; Furthermore, the principle of linear regression analysis is to describe the relationship between the independent variable and the dependent variable by fitting a linear equation, so as to predict the value of the dependent variable and evaluate the strength and direction of this relationship; by monitoring the change of AGC level over time, the trend of signal reception quality starting to decline can be identified early, which may be caused by sensor aging, dirt accumulation, increased environmental noise or other factors; when the AGC level is observed to continue to decline, it indicates that the flow meter may need to be maintained or cleaned, which can help to formulate a more accurate preventive maintenance plan and reduce unplanned downtime; good signal quality is the key to ensure accurate measurement of ultrasonic flowmeters. If the AGC level drops, it means that the original signal is weakened, which will directly affect the measurement accuracy of the flowmeter; a stable AGC level helps to ensure the stable operation of the system. When the AGC level fluctuates greatly, it may indicate that there are unstable factors in the system; The trend of the automatic gain control (AGC) level change is analyzed by linear regression to determine whether there is a decrease in signal strength, including the following steps: S3.21, pre-processing the collected automatic gain control level data, if the data is not uniformly sampled, re-sampling the data to a fixed time interval, such as one data point per minute or per second; S3.22. Construct a linear regression model based on the preprocessed AGC level data and perform model evaluation (calculate the slope ,if If it is negative, it means that the automatic gain control level decreases over time. is positive, it means that the automatic gain control level increases over time); Furthermore, the linear regression model is: ; in, Indicates the automatic gain control level; Indicates time; represents the intercept term, that is Automatic gain control level Expected value of express Each unit increase in the automatic gain control level The amount of change in , i.e. the slope; represents the error term, which represents the random fluctuations that cannot be explained by the model; S3.23. Determine the trend of the automatic gain control level based on the sign of the slope in the linear regression model. If the slope is negative, it means that the signal strength is decreasing. S3.3. Evaluate the consistency of flow and acoustic velocity between channels using nonparametric tests (Kruskal-Wallis H test) to identify potential blockages or other physical obstructions and to look for temperature effects or changes in material properties. Furthermore, the nonparametric test method is based on the rank of the data rather than the original value. It evaluates the difference or correlation by comparing the rank distribution between samples, and does not rely on the specific distribution assumptions of the data. The nonparametric test does not assume that the data follows a specific probability distribution (such as a normal distribution), so it is applicable to various types of flow meter data, including those that do not conform to the normal distribution, which makes the Kruskal-Wallis H test more flexible and able to handle various complex situations that may arise in practical applications. The Kruskal-Wallis H test can compare the medians of three or more groups of independent samples at the same time, which is particularly important for multi-channel flow meters. It can be used to detect whether there are significant differences between different channels and help identify possible local blockages, sensor failures or other influencing factors. The results of the Kruskal-Wallis H test can be expressed by a simple statistic H and its corresponding p-value, which helps to quickly judge the consistency level between different channels. When the H statistic exceeds the critical value, it indicates that at least one channel is significantly different from the other channels, indicating that the problem of the channel needs to be further checked. The consistency of flow and sound velocity between channels was assessed by a nonparametric test method (Kruskal-Wallis H test), which included the following steps: S3.31. Preprocess the collected flow velocity and sound velocity data, including removing outliers and missing values, to ensure data quality; S3.32, grouping the flow velocity and sound velocity data according to different sound channels. For example, if the flow meter has three sound channels (A, B, and C), the flow velocity data and sound velocity data of the three sound channels are collected respectively; S3.33. Combine the data of all channels into a list, sort the combined data in ascending order, assign a rank to each data point (rank refers to the position of each observation value in a set of data after being sorted in ascending or descending order). If there are identical data points (i.e., identical flow velocity values), assign the average rank; S3.34. For each channel, calculate the sum of the ranks of all data points , , It is The first The rank of the data points; S3.35. Calculation Statistics; S3.36. Select the significance level, such as 0.05; S3.37. Find the critical value in the Kruskal-Wallis H distribution table based on the degrees of freedom (the number of independent variables that can change freely when calculating statistics, reflecting the amount of information in the data that can be used to estimate unknown parameters, and the degrees of freedom are equal to the number of channels minus 1) and the significance level (such as 0.05, which is used to determine whether the results of the statistical test are statistically significant); S3.38 If If the statistic is greater than the critical value, it means that the flow rate or sound speed between different channels is inconsistent; S3.4. Review the average time interval through the Autoregressive Integrated Moving Average (ARIMA) model to ensure that the system response speed meets expectations; Furthermore, the principle of the ARIMA model is to fit time series data by combining three techniques: autoregression (AR), differencing (I), and moving average (MA), so as to capture trends, seasonality, and random fluctuations in the data for predicting future values; the ARIMA model is specifically designed to process time series data and is able to capture trends, seasonality, and random fluctuations in data over time, which makes it very suitable for analyzing time interval data in the signal processing process of flow meters, which are usually time-dependent; once the ARIMA model is properly fitted to historical data, it can be used to predict future average time intervals, and this predictive ability can help identify system response speeds. By comparing the difference between the actual observed value and the ARIMA model prediction value, anomalies can be quickly detected. If the actual time interval deviates significantly from the model prediction value, this may be a signal of a system problem, such as a hardware failure or a software error. By conducting in-depth analysis of the time interval data, the ARIMA model helps to locate the problem more accurately. For example, if the time interval is found to be continuously increasing, it may indicate that the system processing capacity is insufficient or there is a resource bottleneck. Conversely, if the time interval suddenly decreases, it may mean that the system configuration has changed or new optimization measures have been implemented. The average time interval is examined through the Autoregressive Integrated Moving Average (ARIMA) model, which includes the following steps: 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, resample the data to a fixed time interval, such as one data point per minute or per second, and use the ADF test method 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); S3.42. Determine the parameters of the autoregressive integrated moving average model, which include the autocorrelation function (which describes the correlation between the time series and its lag term) and the partial autocorrelation function (which describes the direct correlation between the time series and its lag term, controlling the influence of the intermediate lag term), the difference order d, the autoregressive order p and the moving average order q; S3.43, using the determined model parameters to fit the autoregressive integrated moving average model; Furthermore, the autoregressive integrated moving average model is: ; in, Represents a time series at a point in time The value of represents a constant term; represents the autoregressive coefficient; represents the moving average coefficient; represents the white noise error term; Indicates a point in time; represents the number of autoregressive terms; represents the number of moving average terms; Represents a time series at a point in time The value of Represents a time series at a point in time value; Represents a time series at a point in time The value of Indicates at a point in time The prediction error of Indicates at a point in time The prediction error of Indicates at a point in time The prediction error of S3.44, use the fitted autoregressive integrated moving average model to predict future time intervals; S3.45. Analyze the prediction results to see if there are obvious upward and downward trends. If the prediction results show significant changes in the time interval, it means that there is a problem with the system response speed.

[0020] S3.5. Check the pulse reception rate. Low usage may indicate a problem with signal transmission.

[0021] S4. Generate corresponding status indications and alarms according to the evaluation results; In this embodiment, generating corresponding status indications and alarms according to the evaluation results includes the following steps: S4.1, set the threshold of signal-to-noise ratio and automatic gain control level, set a maximum allowable flow velocity and sound velocity difference, set the response time range, and set the minimum pulse reception rate; S4.2. When all parameters do not exceed the threshold value and are within the safe range, the control system of the gas ultrasonic flow meter will display green. If the parameters deviate seriously 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. The LED light on the flow meter flashes to sound an alarm.

[0022] The above shows and describes 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 by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A self-diagnosis method for a gas ultrasonic flow meter, characterized in that: The following steps are involved: S1. Initialize and calibrate the gas ultrasonic flow meter; S2. Collect key parameters from each channel of the gas ultrasonic flow meter in real time, and record the pulse usage rate of each channel and the average time interval of system processing; S3. Compare the currently collected data with the preset standards and evaluate the indicators of key parameters to identify potential problems; S4. Generate corresponding status indications and alarms based on the evaluation results.

2. The self-diagnosis method of a gas ultrasonic flowmeter according to claim 1, characterized in that: In S2, key parameters are collected from each channel of the gas ultrasonic flowmeter in real time, and the pulse usage rate of each channel and the average time interval of system processing are recorded, including the following steps: S2.

1. Collect real-time data of 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 speed of sound between all channels; S2.

4. Monitor and record the average time interval used in the signal processing process; S2.

5. Track and record the ratio of the number of valid pulses received by each channel to the total number of pulses sent.

3. The self-diagnosis method of a gas ultrasonic flowmeter according to claim 2, characterized in that: In S3, the currently collected data is compared with the preset standard, and the indicators of key parameters are evaluated to identify potential problems, including the following steps: S3.1, check whether the signal-to-noise ratio is lower than the threshold m; S3.

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

3. Evaluate the consistency of flow and sound velocity between channels through non-parametric test methods, identify potential blockage problems, and find out the temperature effects. S3.4, review the average time interval through the autoregressive integrated moving average model; S3.

5. Check the pulse reception rate.

4. The self-diagnosis method of a gas ultrasonic flowmeter according to claim 3, characterized in that: In S3.2, the linear regression analysis of the automatic gain control level change trend is used to determine whether there is a decrease in signal strength, including the following steps: S3.21, pre-processing the collected automatic gain control level data, if the data is not uniformly sampled, re-sampling the data to a fixed time interval; S3.22, constructing a linear regression model based on the preprocessed automatic gain control level data and performing model evaluation; S3.

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

5. The self-diagnosis method of a gas ultrasonic flowmeter according to claim 4, characterized in that: In S3, in S3.23, the linear regression model is: ； in, Indicates the automatic gain control level; Indicates time; represents the intercept term; express Each unit increase in the automatic gain control level The amount of change; represents the error term.

6. The self-diagnosis method of a gas ultrasonic flowmeter according to claim 5, characterized in that: In S3.3, the consistency of flow velocity and sound velocity between the channels is evaluated by a non-parametric test method, comprising the following steps: S3.31, preprocessing the collected flow velocity and sound velocity data; S3.32, grouping the flow velocity and sound velocity data according to different sound channels; S3.33, merge the data of 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, calculating the sum of the ranks of all data points; S3.

35. Calculation Statistics; S3.

36. Select the significance level; S3.

37. Find the critical value in the Kruskal-Wallis H distribution table based on the degrees of freedom and significance level; S3.38 If If the statistic is greater than the critical value, it means that the flow velocity or sound speed between different channels is inconsistent.

7. The self-diagnosis method of a gas ultrasonic flowmeter according to claim 8, characterized in that: In S3.35, the calculated statistic is: ； in, It indicates the statistic for evaluating whether there are significant differences between the medians of multiple independent samples; Represents the total number of data points for all channels; Indicates the number of channels; Indicates The rank of the channels; Indicates The number of data points per channel; Indicates the index of the channel.

8. The self-diagnosis method of a gas ultrasonic flowmeter according to claim 7, characterized in that: In S3.4, the average time interval is examined by an autoregressive integrated moving average model, comprising the following steps: S3.

41. Preprocess the collected average time interval data and use the ADF test method to check whether the time series is stable; S3.42, determine the autoregressive integrated moving average model parameters; S3.43, using the determined model parameters to fit the autoregressive integrated moving average model; S3.44, use the fitted autoregressive integrated moving average model to predict future time intervals; S3.

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

9. The self-diagnosis method of a gas ultrasonic flowmeter according to claim 8, characterized in that: In S3.43, the autoregressive integrated moving average model is: ； in, Represents a time series at a point in time The value of represents a constant term; represents the autoregressive coefficient; represents the moving average coefficient; represents the white noise error term; Indicates a point in time; represents the number of autoregressive terms; represents the number of moving average terms; Represents a time series at a point in time The value of Represents a time series at a point in time value; Represents a time series at a point in time The value of Indicates at a point in time The prediction error of Indicates at a point in time The prediction error of Indicates at a point in time The prediction error of .

10. The self-diagnosis method of a gas ultrasonic flowmeter according to claim 9, characterized in that: In S4, generating corresponding status indications and alarms according to the evaluation results includes the following steps: S4.1, set the threshold of signal-to-noise ratio and automatic gain control level, set a maximum allowable flow velocity and sound velocity difference, set the response time range, and set the minimum pulse reception rate; S4.

2. When all parameters do not exceed the threshold value and are within the safe range, the control system of the gas ultrasonic flow meter will display green. If the parameters deviate seriously 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. The LED light on the flow meter flashes to sound an alarm.

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