A new energy high-stability control valve intelligent detection method and system

Abstract

The present invention relates to the field of control valve circuit technology, specifically a new energy, high-stability control valve circuit intelligent detection method and system, comprising the following steps: collecting pressure sensor data at the control valve inlet and outlet, monitoring the current valve inlet pressure and outlet pressure, calculating the pressure difference between the two through continuous data acquisition, and analyzing whether the difference exceeds the range of conventional operating data. The present invention achieves earlier fault diagnosis and early warning by analyzing subtle changes in pressure differences and sound frequencies, thereby reducing energy loss and improving the operating efficiency of the valve circuit system. The intelligent processing of integrated pressure difference and sound data can not only quickly respond to control valve problems, but also accurately determine the operating status of the control valve, thereby optimizing the gas supply efficiency and safety of hydrogen energy vehicles. By continuously monitoring the functional status of the control valve, it can predict and prevent faults, thereby extending the life of the control valve circuit and improving the operating efficiency of the valve circuit.

Description

Technical Field

[0001] The present invention relates to the technical field of control valve circuits, and in particular to an intelligent detection method and system for a new energy high-stability control valve circuit. Background Art

[0002] Control valve circuit technology involves the use of various types of control valves to regulate fluid flow, including the flow, pressure and temperature of gases, liquids or steam. These control valves, such as solenoid valves, proportional valves or rotary valves, are often integrated into a wider automation system to optimize the efficiency and safety of industrial processes. The design and implementation of control valve circuits need to consider the valve's response speed, accuracy and compatibility with other parts of the system.

[0003] Among them, the intelligent detection method for new energy high-stability control valve circuit is a technology for intelligent detection and control of control valve circuits in new energy vehicle systems. This method monitors the performance of control valves through integrated sensor technology and data analysis software to ensure the optimization of valve operation, thereby improving the stability and efficiency of the entire hydrogen energy vehicle system. It has a wide range of uses, including improving hydrogen supply efficiency, reducing energy loss, preventing equipment failure, and extending the life of hydrogen energy vehicles through continuous monitoring.

[0004] Existing technologies are unable to provide sufficient real-time data monitoring and immediate fault analysis, resulting in slow response to dynamic changes in the fluid and inability to make real-time adjustments, which can easily lead to reduced efficiency of the valve system and even safety accidents. If abnormal changes in hydrogen flow cannot be identified and adjusted in real time, gas leakage or excessive pressure will occur, increasing the operating risk and maintenance cost of the valve system. Existing technologies are insufficient in early fault detection and accurate assessment of the valve system status, and intervention is often carried out only after the problem has developed to a serious stage, increasing the complexity of the valve circuit and potential fault repair costs. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a new energy high-stability control valve intelligent detection method and system.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution: a new energy high stability control valve intelligent detection method, comprising the following steps:

[0007] S1: Collect pressure sensor data at the inlet and outlet of the control valve, monitor the current inlet pressure and outlet pressure of the valve, calculate the pressure difference between the two through continuous data acquisition, analyze whether the difference exceeds the range of normal operating data, and record the deviation in the data to obtain pressure difference offset data;

[0008] S2: Acoustic sensors are used to collect acoustic wave data during the operation of the control valve, continuously monitoring the frequency and intensity of the acoustic waves. Spectral analysis is performed on the collected acoustic wave data to identify abnormal points that are inconsistent with conventional acoustic wave characteristics and obtain abnormal acoustic wave indicators.

[0009] S3: Analyze the pressure differential offset data, calculate the pressure differential change rate, evaluate the frequency and amplitude of the pressure differential fluctuations in continuous time periods, identify the key time intervals of pressure differential fluctuations, and perform gradient analysis on the data of the time intervals to determine whether there is an abnormal pressure differential change trend, thereby obtaining the pressure differential fluctuation trend;

[0010] S4: Based on the abnormal sound wave indicators, analyze the abnormal signal strength and duration, determine the signal interval that meets the abnormal acoustic characteristic pattern, calculate the abnormal signal frequency within the interval and its distribution ratio during the operation process, judge the potential control valve structure problem, and obtain acoustic abnormality diagnostic data.

[0011] The improvements of the present invention are that the pressure difference offset data includes inlet pressure deviation, outlet pressure deviation, and pressure offset index; the abnormal sound wave indicators include frequency deviation value, intensity deviation value, and abnormal spectrum identification; the pressure difference fluctuation trend includes fluctuation rate index, frequency fluctuation index, and amplitude fluctuation index; the acoustic abnormality diagnostic data includes acoustic signal strength index, acoustic duration index, and acoustic frequency distribution.

[0012] The present invention is improved in that the steps of obtaining the pressure difference offset data are specifically as follows:

[0013] S111: Collecting pressure sensor data at the inlet and outlet of the control valve, continuously collecting inlet pressure values ​​and outlet pressure values ​​within a certain period of time, and recording the corresponding time series, then calculating the instantaneous pressure difference at each time point to generate an instantaneous pressure difference series;

[0014] S112: Based on the instantaneous pressure difference sequence, the sequence is normalized using the formula:

[0015] ;

[0016] Calculate the normalized pressure difference at each time point , get the standardized pressure difference trend value, where, Representative The inlet pressure at a given time point, Representative The outlet pressure at a certain time point, and are the mean and standard deviation of the instantaneous pressure difference series respectively;

[0017] S113: calling the standardized pressure difference trend value to determine whether it exceeds the normal operating range, filtering data exceeding the normal range, and recording deviations in the data to obtain pressure difference offset data.

[0018] The present invention is improved in that the steps of obtaining the abnormal sound wave index are specifically as follows:

[0019] S211: collecting acoustic wave data during the operation of the control valve through an acoustic sensor, continuously monitoring the frequency and intensity of the acoustic waves, and arranging them into time series data to generate an acoustic wave time series;

[0020] S212: Analyze the frequency offset and intensity change based on the sound wave time series using the formula:

[0021] ;

[0022] Calculate the acoustic wave characteristic deviation at each time point ,in, Representative The frequency of the sound wave at a point in time, Representative The intensity of the sound wave at a certain time point, and are the mean and standard deviation of the sound wave frequency, and are the mean and standard deviation of the sound wave intensity;

[0023] S213: Based on the acoustic wave characteristic deviation, identify abnormal points whose deviation exceeds the normal range, filter abnormal acoustic wave data, and extract corresponding key acoustic parameters to obtain abnormal acoustic wave indicators.

[0024] The present invention is improved in that the step of obtaining the pressure difference fluctuation trend is specifically as follows:

[0025] S311: Analyze the pressure difference offset data, collect the pressure difference value at each time point from the data source, and calculate the pressure difference change rate between adjacent time points;

[0026] S312: Based on the pressure difference change rate, analyze the fluctuation frequency and fluctuation amplitude within a continuous time period, using the formula:

[0027] ;

[0028] Calculate the standard deviation of fluctuations , get the key time interval of pressure difference fluctuation, where Representative The pressure difference value at a time point, represents the average value of the pressure difference at a given time point, Represents the total number of time points;

[0029] S313: Based on the pressure differential fluctuation key time interval, gradient analysis is performed on the pressure differential data within the range to determine whether there is an abnormal trend, and trend features are extracted to obtain the pressure differential fluctuation trend.

[0030] The present invention is improved in that the steps of acquiring the acoustic abnormality diagnosis data are specifically as follows:

[0031] S411: Based on the abnormal sound wave index, analyze the abnormal signal strength and duration, determine the signal interval that meets the abnormal acoustic characteristic pattern, calculate the abnormal signal frequency within the interval, and use the formula:

[0032] ;

[0033] Calculate the distribution ratio of abnormal signals ,in, Indicates the The frequency of abnormal signals in a time period, Indicates the total number of measurements during the total operation period;

[0034] S412: Based on the abnormal signal distribution ratio, combined with the abnormal signal strength and duration, the distribution characteristics of the acoustic abnormal signal are analyzed to determine the existing control valve structure problem and obtain acoustic abnormality diagnosis data.

[0035] The present invention is improved in that the steps further include:

[0036] S5: Based on the pressure difference fluctuation trend and acoustic anomaly diagnosis data, analyze data correlation, identify the current state of the control valve circuit, calculate the degree of matching with the known fault mode, and determine the warning level corresponding to the current state to obtain the current state of the control valve circuit;

[0037] The current status of the control valve circuit includes fault mode consistency and warning level index.

[0038] The present invention is improved in that the steps of obtaining the current state of the control valve circuit are specifically as follows:

[0039] S511: Based on the pressure difference fluctuation trend and acoustic anomaly diagnosis data, analyzing the time series characteristics of each parameter, screening the characteristic data of the time corresponding interval, and obtaining the time corresponding characteristic data;

[0040] S512: Call the time-corresponding characteristic data to identify the current state of the control valve circuit, compare the characteristic data of the known fault mode, and use the formula:

[0041] ;

[0042] Calculate the matching degree with the known fault mode to obtain the current state fault similarity ,in, Represents the current state Dimensional time corresponds to characteristic data, Represents the first dimensional eigenvalues, Represents the total number of feature dimensions;

[0043] S513: Compare the current state fault similarity with the warning level of the fault mode, determine the warning level corresponding to the control valve circuit, and obtain the current state of the control valve circuit.

[0044] A new energy high-stability control valve intelligent detection system, the system comprising:

[0045] The differential pressure monitoring module collects data from the pressure sensors at the inlet and outlet of the control valve, monitors the current inlet and outlet pressures of the valve, calculates the pressure difference between the two, analyzes whether the difference exceeds the range of normal operating data, and records any deviations in the data to obtain differential pressure offset data;

[0046] The acoustic wave analysis module collects acoustic wave data during the operation of the control valve through an acoustic sensor, continuously monitors the frequency and intensity of the acoustic waves, performs acoustic spectrum analysis on the collected acoustic wave data, identifies abnormal points that are inconsistent with conventional acoustic wave characteristics, and obtains abnormal acoustic wave indicators;

[0047] The pressure differential fluctuation analysis module analyzes the pressure differential offset data, evaluates the frequency and amplitude of the pressure differential fluctuations within a continuous period, identifies the key time intervals of the pressure differential fluctuations, determines whether there is an abnormal pressure differential change trend, and obtains the pressure differential fluctuation trend;

[0048] The acoustic anomaly diagnosis module analyzes the abnormal signal strength and duration based on the abnormal sound wave indicators, determines the signal interval that meets the abnormal acoustic characteristic pattern, calculates the abnormal signal frequency within the interval and its distribution ratio during operation, determines potential control valve structural problems, and obtains acoustic anomaly diagnosis data;

[0049] The state assessment module identifies the current state of the control valve circuit based on the pressure difference fluctuation trend and acoustic anomaly diagnosis data, calculates the degree of matching with the known fault mode, and determines the warning level corresponding to the current state to obtain the current state of the control valve circuit.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] In the present invention, by analyzing subtle changes in pressure differences and sound frequencies, earlier fault diagnosis and early warning are achieved, thereby reducing energy loss and improving the operating efficiency of the valve circuit system. The intelligent processing of comprehensive pressure differences and sound data can not only quickly respond to control valve problems, but also accurately judge the working status of the control valve, thereby optimizing the gas supply efficiency and safety of hydrogen energy vehicles. By continuously monitoring the functional status of the control valve, faults can be predicted and prevented, thereby extending the life of the control valve circuit and improving the working efficiency of the valve circuit. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of the intelligent detection method for a new energy high-stability control valve circuit proposed by the present invention;

[0053] Figure 2 Flowchart of the steps for obtaining pressure difference offset data in the present invention;

[0054] Figure 3 Flowchart of the steps for obtaining abnormal sound wave indicators in the present invention;

[0055] Figure 4 Flowchart of the steps for obtaining the pressure difference fluctuation trend in the present invention;

[0056] Figure 5 Flowchart of the steps for obtaining acoustic anomaly diagnosis data in the present invention;

[0057] Figure 6 This is a flow chart of the steps for obtaining the current status of the control valve circuit in the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined. Example

[0060] See also Figure 1The present invention provides a technical solution: a new energy high-stability control valve intelligent detection method, comprising the following steps:

[0061] S1: Collect pressure sensor data at the inlet and outlet of the control valve, monitor the current inlet pressure and outlet pressure of the valve, calculate the pressure difference between the two through continuous data acquisition, analyze whether the difference exceeds the range of normal operating data, and record the deviation in the data to obtain pressure difference offset data;

[0062] S2: Acoustic sensors are used to collect acoustic wave data during the operation of the control valve, continuously monitoring the frequency and intensity of the acoustic waves. Spectral analysis is performed on the collected acoustic wave data to identify abnormal points that are inconsistent with conventional acoustic wave characteristics and obtain abnormal acoustic wave indicators.

[0063] S3: Analyze the pressure differential offset data, calculate the pressure differential change rate, evaluate the frequency and amplitude of the pressure differential fluctuation in a continuous period, identify the key time intervals of pressure differential fluctuation, and perform gradient analysis on the data in the time interval to determine whether there is an abnormal pressure differential change trend and obtain the pressure differential fluctuation trend;

[0064] S4: Based on the abnormal sound wave indicators, analyze the abnormal signal strength and duration, determine the signal interval that meets the abnormal acoustic characteristic pattern, calculate the abnormal signal frequency within the interval and its distribution ratio during the operation process, determine the potential control valve structure problem, and obtain acoustic abnormality diagnostic data;

[0065] S5: Based on the pressure difference fluctuation trend and acoustic anomaly diagnosis data, analyze the data correlation, identify the current state of the control valve circuit, calculate the degree of matching with the known fault mode, and determine the warning level corresponding to the current state to obtain the current state of the control valve circuit.

[0066] The pressure difference deviation data includes inlet pressure deviation, outlet pressure deviation, and pressure deviation index. The abnormal sound wave indicators include frequency deviation value, intensity deviation value, and abnormal spectrum identification. The pressure difference fluctuation trend includes the fluctuation rate index, frequency fluctuation index, and amplitude fluctuation index. The acoustic abnormality diagnosis data includes the acoustic signal strength index, acoustic duration index, and acoustic frequency distribution. The current status of the control valve circuit includes fault mode consistency and warning level index.

[0067] See also Figure 2 , the specific steps for obtaining the pressure difference offset data are:

[0068] S111: Collecting pressure sensor data at the inlet and outlet of the control valve, continuously collecting inlet pressure values ​​and outlet pressure values ​​within a certain period of time, and recording the corresponding time series, then calculating the instantaneous pressure difference at each time point to generate an instantaneous pressure difference series;

[0069] Collect the pressure sensor data at the inlet and outlet of the control valve. The pressure sensor of the control valve can be installed at the inlet and outlet of the pipeline to measure the fluid pressure entering the valve and the fluid pressure flowing out of the valve respectively. The sensor will continuously collect pressure data within a certain period of time and record the inlet pressure and outlet pressure at each moment according to the timestamp. The time interval of data collection depends on the requirements of the control system, such as recording in seconds or milliseconds. The inlet pressure at a certain moment and outlet pressure Through the data recording system storage, obtain the complete time series data set, for each time point , calculate the corresponding instantaneous pressure difference, that is, ,in, Represents the instantaneous pressure difference at a certain point in time. After calculating the instantaneous pressure difference values ​​at all time points, they are organized into a continuous pressure difference data sequence. For example, if the inlet pressure and outlet pressure of a valve are recorded as 5.2MPa and 4.8MPa respectively, the instantaneous pressure difference is 0.4MPa. As time goes by, multiple instantaneous pressure difference values ​​will form a time series, which can be used to further analyze the valve operation and generate an instantaneous pressure difference sequence.

[0070] S112: Based on the instantaneous pressure difference sequence, the sequence is standardized using the formula:

[0071] ;

[0072] Calculate the normalized pressure difference at each time point , get the standardized pressure difference trend value, where, Representative The inlet pressure at a given time point, Representative The outlet pressure at a certain time point, and are the mean and standard deviation of the instantaneous pressure difference series respectively;

[0073] Based on the instantaneous pressure difference series, it is standardized. The purpose of standardization is to eliminate the fluctuation influence of pressure data at different time points and make the data more comparable. First, the mean of the instantaneous pressure difference series is calculated. and standard deviation, the mean is calculated as follows: , the standard deviation is calculated as follows: ,in, represents the total number of data points, Representative The instantaneous pressure difference at three time points is obtained. After obtaining the mean and standard deviation, the standardized pressure difference at each time point is calculated. The instantaneous pressure difference collected by a control valve at three time points is as follows (unit: MPa):

[0074] ;

[0075] Calculate the mean :

[0076] ;

[0077] Calculating standard deviation :

[0078] ;

[0079] Compute the sum of squared differences:

[0080] ;

[0081] ;

[0082] ;

[0083] Sum of squared differences:

[0084] ;

[0085] ;

[0086] Calculate the normalized pressure difference at each time point:

[0087] ;

[0088] ;

[0089] ;

[0090] After calculating the normalized pressure difference values ​​of all data points, the complete normalized pressure difference trend value is obtained:

[0091] ;

[0092] This trend value can reflect the stability of the control valve pressure change and obtain the standardized pressure difference trend value.

[0093] S113: calling the standardized pressure difference trend value to determine whether it exceeds the normal operating range, filtering the data that exceeds the normal range, and recording the deviation in the data to obtain pressure difference offset data;

[0094] Call the standardized pressure difference trend value to determine whether it exceeds the normal operating range and set the threshold of the normal operating range and , if the standardized pressure difference trend value Out of range , then the pressure difference at that time point is abnormal, filter out the time points that are beyond the normal range, and record the data deviation at that time point. For example, set the normal range to , if the standardized pressure difference calculated at a certain moment , then the data point exceeds the normal range and is recorded as an abnormal deviation point. After screening all data points that exceed the threshold, the number of deviation time points and the deviation amplitude are counted, the overall deviation degree is calculated, and the pressure difference deviation data is obtained.

[0095] See also Figure 3 ,The specific steps for obtaining abnormal sound wave indicators are:

[0096] S211: collecting acoustic wave data during the operation of the control valve through an acoustic sensor, continuously monitoring the frequency and intensity of the acoustic waves, and arranging them into time series data to generate an acoustic wave time series;

[0097] The acoustic sensor is installed near the control valve to capture the acoustic wave data generated during operation in real time. The sampling frequency of the sensor should be set according to the normal vibration frequency range of the control valve. For example, if the valve vibration frequency is mainly concentrated in the range of 100Hz-5kHz, the sampling rate of the sensor should be at least 10kHz to ensure data integrity. The collected data includes the acoustic wave frequency and intensity and is stored in a time series format. The data storage time interval is set to 1 millisecond to ensure sufficient time resolution. For example, if 1000 data points are recorded in a certain period of time, a corresponding acoustic wave time series of 1 second in length can be obtained. The specific data format of this time series is usually a two-dimensional array. The first column records the timestamp, the second column records the sound wave frequency, and the third column records the sound wave intensity. After collecting the data, preprocessing is required. First, the data is denoised. For example, the sliding average method is used to eliminate instantaneous mutation values. Assuming that the sound wave frequency measured at a certain moment is 1200Hz, but its adjacent data points are all around 1100Hz, the average value of the five data points around it can be calculated and used to replace the abnormal point. Subsequently, the processed data is sorted into a time series to ensure that the timestamps are continuous and arranged in chronological order, and finally a complete sound wave time series is obtained.

[0098] S212: Based on the acoustic wave time series, analyze the frequency offset and intensity change using the formula:

[0099] ;

[0100] Calculate the acoustic wave characteristic deviation at each time point ,in, Representative The frequency of the sound wave at a point in time, Representative The intensity of the sound wave at a certain time point, and are the mean and standard deviation of the sound wave frequency, and are the mean and standard deviation of the sound wave intensity;

[0101] Analyze the frequency offset and intensity change at each time point. The frequency offset refers to the deviation of the frequency at a certain time point from the overall frequency mean, while the intensity change refers to the degree of deviation of the intensity at that time point from the overall mean. First, calculate the mean and standard deviation of the overall data. The calculation method is to average the frequencies of all time points to obtain , find the standard deviation of the frequency at all time points to get , Similarly, perform the same operation on the intensity data and get and , and then calculate the standardized offset for each time point. The collected sound wave data is:

[0102] Time point 1: frequency 1000Hz, intensity 78dB;

[0103] Time point 2: frequency 1100 Hz, intensity 82 dB;

[0104] Time point 3: frequency 1200 Hz, intensity 85 dB;

[0105] Calculate the mean:

[0106] ;

[0107] ;

[0108] Calculate the standard deviation:

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] Calculate the acoustic wave characteristic deviation at each time point:

[0114] Time point :

[0115] ;

[0116] ;

[0117] Time point :

[0118] ;

[0119] Time point :

[0120] ;

[0121] The results show that among the three time points, the acoustic wave characteristic deviation degree at the first time point is 1.77, and the acoustic wave characteristic deviation degree at the third time point is 1.69, both of which are higher than 0.11 at the second time point, indicating that the acoustic wave frequency and intensity at the first and third time points deviate from the overall mean to a greater extent, while the data at the second time point are close to the overall mean and the deviation is smaller, which indicates that at the first and third time points, the acoustic wave characteristics of the control valve have abnormal fluctuations, while the acoustic state at the second time point is relatively stable.

[0122] S213: Identify abnormal points whose deviations exceed the normal range based on the acoustic wave characteristic deviation, filter abnormal acoustic wave data, and extract corresponding key acoustic parameters to obtain abnormal acoustic wave indicators;

[0123] Based on the deviation of the acoustic wave characteristics, it is necessary to identify abnormal acoustic wave data. First, set the deviation threshold The threshold is set based on historical data statistics or engineering experience values, for example, , that is, the points with a deviation of more than 3 are considered abnormal points. In actual operation, if the The calculated value is greater than , then the data at that time point is marked as abnormal, and the corresponding acoustic parameters are recorded, including its sound wave frequency, intensity and timestamp. For example, if If the frequency is 1300 Hz and the intensity is 90 dB, the point is marked as an abnormal point and stored in the abnormal data list. After all the abnormal point data are aggregated, their key parameters are extracted to obtain the abnormal sound wave index.

[0124] See also Figure 4 , the specific steps for obtaining the pressure difference fluctuation trend are:

[0125] S311: Analyze the pressure difference offset data, collect the pressure difference value at each time point from the data source, and calculate the pressure difference change rate between adjacent time points;

[0126] The pressure difference value at each time point is collected from the data source. During the collection process, data points in a specific time range are selected, and the integrity and continuity of the data are ensured. The data sampling interval is set, the pressure value at each time point is determined, and the missing data is filled by interpolation. After the data collection is completed, the data is sorted in time to ensure the continuity of the time points, and the pressure difference change between adjacent time points is calculated. The calculation method is to take the difference between the pressure difference value at each time point and the pressure difference value at the previous time point to obtain the instantaneous pressure difference change at each time point. On this basis, the pressure difference change rate is calculated, that is, the pressure difference change is divided by the time interval between adjacent time points. If the pressure difference value at a certain time point is 5.3kPa, the pressure difference value at the previous time point is 5.0kPa, and the time interval between the two time points is 2 seconds, then the pressure difference change rate at that time point is kPa / s, calculate the data of all time points in sequence to generate the pressure difference change rate.

[0127] S312: Based on the pressure difference change rate, analyze the fluctuation frequency and fluctuation amplitude within a continuous time period, using the formula:

[0128] ;

[0129] Calculate the standard deviation of fluctuations , used to measure the fluctuation amplitude of the pressure difference value within a certain period of time and obtain the key time interval of pressure difference fluctuation, where, Representative The pressure difference value at a time point, represents the average value of the pressure difference at a given time point, Represents the total number of time points;

[0130] First, determine the number of data points in the statistical time window. Assume that the time window contains 3 data points, which are kPa, kPa, kPa, calculate the average pressure difference during this time period, the formula is:

[0131] ;

[0132] Calculate the squared deviation for each data point and sum them:

[0133] ;

[0134] Calculate the standard deviation of the fluctuations:

[0135] ;

[0136] This result indicates that the pressure differential fluctuation within this time window is 0.2 kPa. The standard deviation measures the dispersion of the pressure differential values; a larger value indicates more severe pressure differential fluctuations within this time window. If this value exceeds the set fluctuation threshold (for example, 0.15 kPa), it indicates that the pressure differential fluctuation within this time period is abnormal, and this time window is marked as a critical pressure differential fluctuation time period.

[0137] S313: Based on the key time interval of pressure differential fluctuation, gradient analysis is performed on the pressure differential data within the range to determine whether there is an abnormal trend, and trend features are extracted to obtain the pressure differential fluctuation trend;

[0138] According to the key time interval of pressure difference fluctuation, the pressure difference data within its range is subjected to gradient analysis, and the pressure difference gradient between adjacent time points is calculated, that is, the pressure difference change rate at each time point compared with the rate change value at the previous time point. The gradient calculation method is: ,in For the The rate of change of pressure difference at a time point, is the pressure difference change rate at the previous time point. If the pressure difference change rate at a certain time point is 0.12 kPa / s, and the pressure difference change rate at the previous time point is 0.09 kPa / s, then the gradient value at that time point is kPa / s². After calculating the gradient values ​​at all time points, data points that exceed the preset gradient threshold are screened out as abnormal data points. The set gradient threshold is calculated based on historical data or experimental test results. For example, under normal operating conditions, the gradient is usually between 0.01kPa / s² and 0.05kPa / s². If the gradient of a data point exceeds 0.06kPa / s², it is marked as an abnormal point, generating a pressure difference fluctuation trend.

[0139] See also Figure 5 ,The specific steps for obtaining acoustic anomaly diagnosis data are as follows:

[0140] S411: Based on the abnormal sound wave indicators, analyze the abnormal signal strength and duration, determine the signal interval that meets the abnormal acoustic characteristic pattern, calculate the abnormal signal frequency within the interval, and use the formula:

[0141] ;

[0142] Calculate the distribution ratio of abnormal signals ,in, Indicates the The frequency of abnormal signals in a time period, that is, the number of abnormal signals detected in this time period, Indicates the total number of measurements during the total operation period;

[0143] Acquire and record the signal strength at each time point. The signal strength can be quantified by detecting the amplitude of the acoustic signal through the acoustic wave sensor. For example, the signal amplitude range within a monitoring period is set to to , and convert the amplitude signal into numerical data storage, calculate the intensity change rate between adjacent time points, that is, for two consecutive time points and , calculate the rate of change of its signal amplitude This calculation method can be used to quantify the change in signal strength and further calculate the signal duration, that is, when the signal exceeds a certain threshold The time period is accumulated to obtain the total duration of the abnormal signal. For example, if an abnormal signal lasts for 2 seconds and the threshold is set to 1.5 seconds, the signal is identified as a long-term abnormal signal. Then the signal interval that meets the abnormal acoustic characteristic pattern is determined and the signal that meets the amplitude characteristic threshold is screened. and duration The time period is taken as the abnormal signal interval, and the abnormal signal frequency in the interval is calculated. The number of occurrences of abnormal signals per unit time is calculated using statistical methods. For example, if the duration of a certain interval is 10s and 5 abnormal signals are detected, then the abnormal signal frequency is 0.5Hz. The abnormal signal distribution ratio is obtained, and the measurement duration is taken as the total duration of the entire monitoring period. This duration represents the time range of data collection during the entire measurement process. For example, set , and divide it into 6 time periods, and count the frequency of abnormal signals in each time period , which represents the ratio of the number of abnormal signals detected in the time period to the length of the time period. If the measured abnormal signal frequency data is as follows:

[0144] , , , , , ;

[0145] , substitute the measured data into the calculation:

[0146] ;

[0147] ;

[0148] Calculate the distribution ratio of abnormal signals , which represents the average frequency of abnormal signals during the entire measurement period, that is, approximately 0.3833 abnormal signals occur per second. This result can be used to further analyze the distribution characteristics of abnormal signals and, combined with other acoustic data, to determine the existence of control valve structural problems.

[0149] S412: Analyze the distribution characteristics of the abnormal acoustic signal based on the abnormal signal distribution ratio, combined with the abnormal signal strength and duration, determine the existing control valve structure problem, and obtain acoustic abnormality diagnosis data;

[0150] Obtain abnormal signal strength statistics, including the peak amplitude of the abnormal signal and mean amplitude For example, if the amplitude of the abnormal signal ranges from 5dB to 20dB, its peak amplitude is 20dB and its mean amplitude is 12dB. Further obtain the statistical data of the signal duration, including the shortest duration of the abnormal signal. and maximum duration For example, if the duration of the detected abnormal signal is between 0.5s and 3s, then and , combined with the distribution ratio of abnormal signals, the data is trend analyzed to determine whether the abnormal signal shows periodic or sudden changes. For periodic changes, it is determined whether the signal recurs at fixed intervals. For example, if the abnormal signal is detected to appear once every 30 seconds, the signal is judged to be periodic. On the contrary, if the abnormal signal distribution is random, it is judged to be a sudden abnormal signal, and the acoustic abnormality diagnosis data is obtained.

[0151] See also Figure 6 , the steps for obtaining the current status of the control valve are as follows:

[0152] S511: Based on the pressure difference fluctuation trend and acoustic anomaly diagnosis data, analyze the time series characteristics of each parameter, filter the characteristic data of the time corresponding interval, and obtain the time corresponding characteristic data;

[0153] The pressure difference fluctuation data of the control valve circuit is collected, including the recorded values ​​of the pressure sensor at different time points. The data is collected by the equipment monitoring system at fixed time intervals, such as once per second. The acquired data points form a pressure fluctuation curve. The acoustic abnormality diagnosis data comes from the acoustic sensor installed on the equipment. The sensor records the sound wave signal generated by the air flow vibration inside the valve. The signal is spectrally analyzed by fast Fourier transform (FFT) to extract the intensity information of different frequency components. Subsequently, the sliding window method is used to calculate the time series characteristics of the collected pressure difference fluctuation data, such as the mean, variance, skewness, kurtosis and other characteristic values. Among them, the sliding window size can be adjusted according to the valve operation. The period is adjusted, for example, it is set to a 10-second window, sliding for 1 second each time. At the same time, the acoustic signal is feature extracted, the power spectral density of the signal in different frequency ranges is calculated, and its energy distribution in a specific frequency range is analyzed. For example, the signal power from 100Hz to 1kHz can reflect the airflow disturbance inside the valve. Next, the characteristic data of the time corresponding interval is screened, that is, the pressure difference fluctuation data and the acoustic signal data are time-aligned. If an abnormal event occurs at time T, it is necessary to select the pressure difference fluctuation characteristic value and acoustic characteristic value 5 seconds before and after T, and match the timestamps of the two data sets to ensure that the two correspond to the same operating state and obtain the time-corresponding characteristic data.

[0154] S512: Call the characteristic data corresponding to the time, identify the current state of the control valve circuit, compare the characteristic data of the known fault mode, and use the formula:

[0155] ;

[0156] Calculate the matching degree with the known fault mode to obtain the current state fault similarity ,in, Represents the current state Dimensional time corresponds to characteristic data, Represents the first dimensional eigenvalues, Represents the total number of feature dimensions;

[0157] Fault mode data is read, such as valve stuck fault, seal leakage fault, and gas source fault. Each fault mode contains a set of typical pressure difference fluctuation characteristics and acoustic characteristic values. For example, a valve stuck fault is manifested as an increase in the acoustic signal power at a specific frequency (300Hz), while a seal leakage fault is manifested as an increase in the high-frequency (above 5kHz) signal power. Subsequently, the similarity between the current state and each fault mode is calculated. The typical characteristic values ​​of a certain fault mode are: average pressure fluctuation amplitude of 2.5kPa, acoustic signal 300Hz power spectrum density of 0.8dB, while the measured values ​​of the current state are 2.0kPa and 0.6dB, respectively. The characteristic ratios are:

[0158] , ;

[0159] set up , then the similarity is calculated as follows:

[0160] ;

[0161] This value indicates that the current state has a high degree of matching with the fault mode. It is compared with the similarities calculated for different fault modes, and the mode with the highest similarity is selected to obtain the current state fault similarity.

[0162] S513: Compare the current state fault similarity with the warning level of the fault mode to determine the warning level corresponding to the control valve circuit and obtain the current state of the control valve circuit;

[0163] According to the current state fault similarity, it is compared with the warning level of the fault mode. First, the warning level threshold is set. For example, if the current state fault similarity is greater than 0.8, it is judged as a serious fault. If it is between 0.5 and 0.8, it is judged as a minor fault. If it is lower than 0.5, it is judged as a normal state. Assuming that the current state fault similarity calculated above is 0.60125, which falls into the minor fault range, the warning level of the control valve circuit is judged to be a minor fault, and the current state of the control valve circuit is obtained.

[0164] A new energy high-stability control valve intelligent detection system, the system includes:

[0165] The differential pressure monitoring module collects data from the pressure sensors at the inlet and outlet of the control valve, monitors the current inlet and outlet pressures of the valve, calculates the pressure difference between the two, analyzes whether the difference exceeds the range of normal operating data, and records any deviations in the data to obtain differential pressure offset data;

[0166] The acoustic wave analysis module collects acoustic wave data during the operation of the control valve through an acoustic sensor, continuously monitors the frequency and intensity of the acoustic waves, performs acoustic spectrum analysis on the collected acoustic wave data, identifies abnormal points that are inconsistent with conventional acoustic wave characteristics, and obtains abnormal acoustic wave indicators;

[0167] The pressure differential fluctuation analysis module analyzes the pressure differential offset data, evaluates the frequency and amplitude of pressure differential fluctuations within a continuous period, identifies the key time intervals of pressure differential fluctuations, determines whether there is an abnormal pressure differential change trend, and obtains the pressure differential fluctuation trend;

[0168] The acoustic anomaly diagnosis module analyzes the abnormal signal strength and duration based on abnormal sound wave indicators, determines the signal interval that meets the abnormal acoustic characteristic pattern, calculates the abnormal signal frequency within the interval and its distribution ratio during operation, determines potential control valve structural problems, and obtains acoustic anomaly diagnosis data;

[0169] The status assessment module identifies the current status of the control valve circuit based on the pressure difference fluctuation trend and acoustic anomaly diagnosis data, calculates the degree of matching with the known fault mode, and determines the warning level corresponding to the current status to obtain the current status of the control valve circuit.

[0170] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A new energy high stability control valve intelligent detection method, characterized in that: The following steps are involved: S1: Collect pressure sensor data at the inlet and outlet of the control valve, monitor the current inlet pressure and outlet pressure of the valve, calculate the pressure difference between the two through continuous data acquisition, analyze whether the difference exceeds the range of normal operating data, and record the deviation in the data to obtain pressure difference offset data; S2: Acoustic sensors are used to collect acoustic wave data during the operation of the control valve, continuously monitoring the frequency and intensity of the acoustic waves. Spectral analysis is performed on the collected acoustic wave data to identify abnormal points that are inconsistent with conventional acoustic wave characteristics and obtain abnormal acoustic wave indicators. S3: Analyze the pressure differential offset data, calculate the pressure differential change rate, evaluate the frequency and amplitude of the pressure differential fluctuations in continuous time periods, identify the key time intervals of pressure differential fluctuations, and perform gradient analysis on the data of the time intervals to determine whether there is an abnormal pressure differential change trend, thereby obtaining the pressure differential fluctuation trend; S4: Based on the abnormal sound wave indicators, analyze the abnormal signal strength and duration, determine the signal interval that meets the abnormal acoustic characteristic pattern, calculate the abnormal signal frequency within the interval and its distribution ratio during the operation process, determine the potential control valve structure problem, and obtain acoustic abnormality diagnostic data; S5: Based on the pressure difference fluctuation trend and acoustic anomaly diagnosis data, analyze data correlation, identify the current state of the control valve circuit, calculate the degree of matching with the known fault mode, and determine the warning level corresponding to the current state to obtain the current state of the control valve circuit; The current status of the control valve circuit includes fault mode consistency and warning level index.

2. The intelligent detection method for new energy high stability control valve circuit according to claim 1 is characterized in that: The pressure difference deviation data includes inlet pressure deviation, outlet pressure deviation, and pressure deviation index; the abnormal sound wave indicators include frequency deviation value, intensity deviation value, and abnormal spectrum identification; the pressure difference fluctuation trend includes fluctuation rate index, frequency fluctuation index, and amplitude fluctuation index; the acoustic abnormality diagnostic data includes acoustic signal strength index, acoustic duration index, and acoustic frequency distribution.

3. The intelligent detection method for new energy high stability control valve circuit according to claim 1 is characterized in that: The steps for obtaining the pressure difference offset data are specifically as follows: S111: Collecting pressure sensor data at the inlet and outlet of the control valve, continuously collecting inlet pressure values ​​and outlet pressure values ​​within a certain period of time, and recording the corresponding time series, then calculating the instantaneous pressure difference at each time point to generate an instantaneous pressure difference series; S112: Based on the instantaneous pressure difference sequence, the sequence is normalized using the formula: ； Calculate the normalized pressure difference at each time point , get the standardized pressure difference trend value, where, Representative The inlet pressure at a given time point, Representative The outlet pressure at a certain time point, and are the mean and standard deviation of the instantaneous pressure difference series respectively; S113: calling the standardized pressure difference trend value to determine whether it exceeds the normal operating range, filtering data exceeding the normal range, and recording deviations in the data to obtain pressure difference offset data.

4. The intelligent detection method for new energy high stability control valve circuit according to claim 1 is characterized in that: The steps for obtaining the abnormal sound wave index are specifically as follows: S211: collecting acoustic wave data during the operation of the control valve through an acoustic sensor, continuously monitoring the frequency and intensity of the acoustic waves, and arranging them into time series data to generate an acoustic wave time series; S212: Analyze the frequency offset and intensity change based on the sound wave time series using the formula: ； Calculate the acoustic wave characteristic deviation at each time point ,in, Representative The frequency of the sound wave at a point in time, Representative The intensity of the sound wave at a certain time point, and are the mean and standard deviation of the sound wave frequency, and are the mean and standard deviation of the sound wave intensity; S213: Based on the acoustic wave characteristic deviation, identify abnormal points whose deviation exceeds the normal range, filter abnormal acoustic wave data, and extract corresponding key acoustic parameters to obtain abnormal acoustic wave indicators.

5. The intelligent detection method for new energy high stability control valve circuit according to claim 1 is characterized in that: The steps for obtaining the pressure difference fluctuation trend are specifically as follows: S311: Analyze the pressure difference offset data, collect the pressure difference value at each time point from the data source, and calculate the pressure difference change rate between adjacent time points; S312: Based on the pressure difference change rate, analyze the fluctuation frequency and fluctuation amplitude within a continuous time period, using the formula: ； Calculate the standard deviation of fluctuations , get the key time interval of pressure difference fluctuation, where Representative The pressure difference value at a time point, represents the average value of the pressure difference at a given time point, Represents the total number of time points; S313: Based on the pressure differential fluctuation key time interval, gradient analysis is performed on the pressure differential data within the range to determine whether there is an abnormal trend, and trend features are extracted to obtain the pressure differential fluctuation trend.

6. The intelligent detection method for new energy high stability control valve circuit according to claim 1 is characterized in that: The steps for obtaining the acoustic abnormality diagnosis data are specifically as follows: S411: Based on the abnormal sound wave index, analyze the abnormal signal strength and duration, determine the signal interval that meets the abnormal acoustic characteristic pattern, calculate the abnormal signal frequency within the interval, and use the formula: ； Calculate the distribution ratio of abnormal signals ,in, Indicates the The frequency of abnormal signals in a time period, Indicates the total number of measurements during the total operation period; S412: Based on the abnormal signal distribution ratio, combined with the abnormal signal strength and duration, the distribution characteristics of the acoustic abnormal signal are analyzed to determine the existing control valve structure problem and obtain acoustic abnormality diagnosis data.

7. The intelligent detection method for new energy high stability control valve circuit according to claim 1 is characterized in that: The steps for obtaining the current state of the control valve circuit are specifically as follows: S511: Based on the pressure difference fluctuation trend and acoustic anomaly diagnosis data, analyzing the time series characteristics of each parameter, screening the characteristic data of the time corresponding interval, and obtaining the time corresponding characteristic data; S512: Call the time-corresponding characteristic data to identify the current state of the control valve circuit, compare it with the characteristic data of the known fault mode, and use the formula: ； Calculate the matching degree with the known fault mode to obtain the current state fault similarity ,in, Represents the current state Dimensional time corresponds to characteristic data, Represents the first dimensional eigenvalues, Represents the total number of feature dimensions; S513: Compare the current state fault similarity with the warning level of the fault mode, determine the warning level corresponding to the control valve circuit, and obtain the current state of the control valve circuit.

8. A new energy high stability control valve intelligent detection system, characterized in that: According to the intelligent detection method of the new energy high-stability control valve circuit according to any one of claims 1 to 7, the system includes: The differential pressure monitoring module collects data from the pressure sensors at the inlet and outlet of the control valve, monitors the current inlet and outlet pressures of the valve, calculates the pressure difference between the two, analyzes whether the difference exceeds the range of normal operating data, and records any deviations in the data to obtain differential pressure offset data; The acoustic wave analysis module collects acoustic wave data during the operation of the control valve through an acoustic sensor, continuously monitors the frequency and intensity of the acoustic waves, performs acoustic spectrum analysis on the collected acoustic wave data, identifies abnormal points that are inconsistent with conventional acoustic wave characteristics, and obtains abnormal acoustic wave indicators; The pressure differential fluctuation analysis module analyzes the pressure differential offset data, evaluates the frequency and amplitude of the pressure differential fluctuations within a continuous period, identifies the key time intervals of the pressure differential fluctuations, determines whether there is an abnormal pressure differential change trend, and obtains the pressure differential fluctuation trend; The acoustic anomaly diagnosis module analyzes the abnormal signal strength and duration based on the abnormal sound wave indicators, determines the signal interval that meets the abnormal acoustic characteristic pattern, calculates the abnormal signal frequency within the interval and its distribution ratio during operation, determines potential control valve structural problems, and obtains acoustic anomaly diagnosis data; The state assessment module identifies the current state of the control valve circuit based on the pressure difference fluctuation trend and acoustic anomaly diagnosis data, calculates the degree of matching with the known fault mode, and determines the warning level corresponding to the current state to obtain the current state of the control valve circuit.

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