A method for online monitoring of the condition of a steam isolation valve system

Abstract

The application discloses a kind of state on-line monitoring methods for steam isolation valve system, belong to valve fault diagnosis technical field.The specific as follows:1) construct steam isolation valve system fault tree and associate corresponding monitoring quantity;2) the data of system factory shipment is as datum and is handled in segments, judge whether it has trend, periodicity and autocorrelation;3) obtain the first threshold value of part monitoring quantity in combination with working condition and technical requirement;for the monitoring quantity with trend, periodicity or autocorrelation, obtain trend threshold value, periodicity threshold value or autocorrelation threshold value;calculate the variance and mean of the multiple groups of datum of all monitoring quantity, obtain second threshold value;4) compare the data in actual operation process and corresponding threshold value, judge whether abnormality appears;5) abnormal monitoring quantity is associated with fault tree event and is indexed, and output fault diagnosis result.The method can quickly detect the specific fault of system by on-line monitoring, guarantee system safe and stable operation.

Description

Technical Field

[0001] The present invention belongs to the technical field of valve fault diagnosis, and in particular relates to an online status monitoring method for a steam isolation valve system. Background Art

[0002] Steam isolation valves are critical safety features in nuclear power plants. In the event of an accident, such as a steam pipe rupture, they must close rapidly within three seconds. Failure of a steam isolation valve during operation can cause a major incident, such as a unit shutdown, and severely threaten the reliability of piping components. Their safe and stable operation significantly impacts the safety and economic benefits of nuclear power plants.

[0003] Steam isolation valve systems involve complex gas-hydraulic actuators, multiple hydraulic devices, complex control systems, multiple operating modes, specialized operating environments, and insufficient data, making fault diagnosis of these systems difficult. Currently, domestic research, fault diagnosis, and specialized testing technologies for steam isolation valve systems are relatively weak. Actual testing typically relies on monthly or quarterly inspections at nuclear power plants, requiring component disassembly and testing. This is time-consuming and labor-intensive, making comprehensive performance evaluation difficult.

[0004] Therefore, giving the steam isolation valve the ability to perceive its own status and fault environment, and then realizing online fault diagnosis, is a problem that needs to be solved in the current operation and maintenance of steam isolation valves. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for online monitoring of the status of a steam isolation valve system in order to address the problem that existing online monitoring and fault diagnosis methods for steam isolation valve systems are imperfect.

[0006] The specific technical solutions adopted in the present invention are as follows:

[0007] The present invention provides a method for online monitoring of the status of a steam isolation valve system, the specific steps of which are as follows:

[0008] S1: Construct a fault tree for the steam isolation valve system and list all potential faults; associate the top event, intermediate event, and bottom event of the fault tree with corresponding monitoring quantities; the monitoring quantities include the monitoring quantities of the gas-liquid linkage actuator and the monitoring quantities of the steam isolation valve body;

[0009] S2: Using the monitoring data of the steam isolation valve system at the time of leaving the factory in step S1 as the baseline data; using the time point of the control signal change as the point of change in the working state of the steam isolation valve system, and segmenting the baseline data according to the time point; and performing feature classification on the segmented baseline data of each monitoring value according to basic characteristics; the basic characteristics include trend, periodicity, and autocorrelation;

[0010] S3: Combine the working condition requirements and technical requirements to obtain fixed thresholds for some monitored quantities as the first thresholds of the monitored quantities; if the benchmark data of the monitored quantity has trend, periodicity or autocorrelation, add a certain margin to the trend index, periodicity peak or autocorrelation index of the benchmark data, and use them as the trend threshold, periodicity threshold or autocorrelation threshold of the monitored quantity respectively; calculate the variance and mean of multiple groups of benchmark data of all monitored quantities, use the mean as the benchmark and the variance as the margin to obtain the second threshold of the monitored quantity;

[0011] S4: pre-processing the monitoring quantity monitored during the actual operation of the steam isolation valve system to obtain pre-processed monitoring quantity data;

[0012] S5: Calculating trend indicators, periodic peak values ​​and autocorrelation indicators of the monitoring data obtained in step S4 after different preprocessing;

[0013] S6: According to different working states of the steam isolation valve system, the pre-processed monitoring data is compared with the first threshold value or the second threshold value, and the trend index, periodic peak value or autocorrelation index calculated in step S5 is compared with the corresponding trend threshold value, periodic threshold value or autocorrelation threshold value; if any data falls outside the corresponding threshold value range, it is determined that the steam isolation valve system is abnormal; otherwise, it is determined that the steam isolation valve system is operating normally;

[0014] S7: Associate each monitoring quantity judged to be abnormal in step S6 with the intermediate events and bottom events in the steam isolation valve system fault tree, and index them in the fault tree according to the coupling relationship between each monitoring quantity, trace them back to the specific fault component, and output the fault diagnosis result.

[0015] Preferably, in step S1, the monitoring quantities of the gas-liquid linkage actuator include the hydraulic oil temperature, the oil pressure in the upper and lower chambers of the hydraulic cylinder, the accumulator air pressure, the accumulator oil pressure, the solenoid valve control current and the thrust of the gas-liquid linkage actuator; the monitoring quantities of the steam isolation valve body include the valve body thrust, the packing bolt force, the valve action time and the valve displacement.

[0016] Preferably, in step S1, the top event of the fault tree is a steam isolation valve system failure event; the intermediate events of the fault tree include valve position change events and valve position non-position events, the valve position change event is associated with the valve action time and valve displacement, and the valve position non-position event is associated with the valve action time, valve displacement, and valve stem thrust; the bottom events of the fault tree include hydraulic cylinder internal leakage events and valve seal failure events, wherein the hydraulic cylinder internal leakage event is associated with the hydraulic cylinder upper chamber oil pressure and the hydraulic cylinder lower chamber oil pressure, and the valve seal failure event is associated with the packing bolt force.

[0017] Preferably, in step S2, the trend of the monitoring quantity benchmark data is calculated using the moving average method, and an appropriate window size is selected. The data trend is judged by calculating the data mean in the window. If the data mean gradually increases, it indicates that the data is on an upward trend; if the data mean gradually decreases, it indicates that the data is on a downward trend; in addition, the trend index is set as the sum of the differences between the data means in the previous and next windows divided by the range of allowable fluctuations of the data. If the trend index is less than 0.5, it is judged that the monitoring quantity does not have a trend, otherwise it has a trend;

[0018] The periodicity of the monitoring quantity benchmark data is measured using the autocorrelation function. If the autocorrelation function graph of the monitoring quantity has regular peaks at different delays, the monitoring quantity is judged to have periodicity, otherwise it does not have periodicity. If the monitoring quantity has periodicity, the periodic peak value of the monitoring quantity is obtained according to the autocorrelation function graph.

[0019] The autocorrelation of the monitoring quantity benchmark data is measured by the autocorrelation index; the autocorrelation coefficient r of multiple groups of benchmark data of the same monitoring quantity is solved k The average value is taken to obtain the autocorrelation index of the benchmark data of the monitoring quantity. If the autocorrelation index is close to 1, it is considered that the monitoring quantity has autocorrelation, otherwise it does not have autocorrelation.

[0020] Furthermore, the autocorrelation function takes the lag period k as the independent variable, and the autocorrelation coefficient r k plotted as the dependent variable; the autocorrelation coefficient r k The calculation formula is as follows:

[0021]

[0022] Where x j represents the jth element of the monitoring quantity; N represents the total number of elements contained in the monitoring quantity x, represents the mean of N monitored quantities; k represents the number of lag periods.

[0023] Preferably, the some monitored quantities include hydraulic oil temperature, hydraulic cylinder upper chamber oil pressure, hydraulic cylinder lower chamber oil pressure, accumulator air pressure, accumulator oil pressure, solenoid valve control current and valve body thrust; wherein the first threshold range of the hydraulic oil temperature is 10℃~55℃, the first threshold range of the hydraulic cylinder upper chamber oil pressure and the hydraulic cylinder lower chamber oil pressure is <25MPa, the first threshold range of the accumulator air pressure is 14MPa~30MPa, the first threshold range of the accumulator oil pressure is 14MPa~30MPa, the first threshold range of the solenoid valve control current is 0~40mA, and the first threshold range of the valve body thrust is 50kN~3000kN.

[0024] Preferably, the preprocessing in step S4 includes data cleaning and noise reduction and data format standardization.

[0025] Preferably, the threshold determination in step S6 is as follows:

[0026] If the monitored quantity has a first threshold, the preprocessed monitored quantity data is compared with the first threshold and the second threshold of the monitored quantity, and the trend index, periodicity peak value or autocorrelation index calculated in step S5 is compared with the trend threshold, periodicity threshold or autocorrelation threshold of the monitored quantity respectively;

[0027] If the first threshold does not exist for the monitored quantity, the preprocessed monitored quantity data is directly compared with the second threshold of the monitored quantity, and the trend index, periodicity peak value or autocorrelation index calculated in step S5 is respectively compared with the trend threshold, periodicity threshold or autocorrelation threshold of the monitored quantity;

[0028] If the pre-processed monitoring data falls outside the first threshold or the second threshold, or the calculated trend index, periodic peak or autocorrelation index falls outside the corresponding trend threshold, periodic threshold, or autocorrelation threshold, it is determined that the steam isolation valve system is abnormal; otherwise, it is determined that the steam isolation valve system is operating normally.

[0029] Preferably, in step S7, the specific faulty component is traced by constructing a seasonal difference autoregressive sliding average model.

[0030] Preferably, the working states of the steam isolation valve system are divided into a stable state, a fast closing state, a slow opening state and a slow closing state;

[0031] If the steam isolation valve system is in a stable state, the monitoring quantities include the oil pressure in the upper chamber of the hydraulic cylinder, the oil pressure in the lower chamber of the hydraulic cylinder, the accumulator air pressure, the accumulator oil pressure, the valve action time and the valve displacement;

[0032] If the steam isolation valve system is in the fast closing state, the monitoring quantities include the solenoid valve control current, accumulator air pressure, accumulator oil pressure, hydraulic cylinder upper chamber oil pressure, hydraulic cylinder lower chamber oil pressure, hydraulic oil temperature, actuator thrust, packing bolt force, valve body thrust, valve action time and valve displacement;

[0033] If the steam isolation valve system is in a slow opening or slow closing state, the monitoring quantities involved include the solenoid valve control current, the oil pressure in the upper chamber of the hydraulic cylinder, the oil pressure in the lower chamber of the hydraulic cylinder, the hydraulic oil temperature, the actuator thrust, the packing bolt force, the valve body thrust, the valve action time and the valve displacement.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] For the steam isolation valve system with complex gas-liquid linkage actuator, by making full use of the factory data and fault tree model of the steam isolation valve system, analyzing multiple monitoring data and their characteristics, selecting the threshold of the valve monitoring quantity, judging the threshold, analyzing the data coupling relationship, and providing timely feedback, the online monitoring of the steam isolation valve system status is realized, and the fault is located to the component event range through diagnosis, which provides a new idea for the online monitoring of the steam isolation valve system status and fault diagnosis, and also provides a basis for the subsequent predictive maintenance of the steam isolation valve system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of the method for online monitoring of the status of the steam isolation valve system;

[0037] Figure 2 Fault tree model for steam isolation valve system;

[0038] Figure 3 : is the autocorrelation function (ACF) diagram of the oil pressure in the upper chamber of the hydraulic cylinder in this embodiment;

[0039] Figure 4 : is the autocorrelation function (ACF) diagram of the oil pressure in the lower chamber of the hydraulic cylinder in this embodiment;

[0040] Figure 5 : is the autocorrelation function (ACF) diagram of the accumulator oil pressure in this embodiment. DETAILED DESCRIPTION

[0041] The present invention will be further described and illustrated below with reference to the accompanying drawings and specific embodiments. The technical features of each embodiment of the present invention may be combined accordingly, provided that there is no conflict between them.

[0042] like Figure 1 FIG. 1 is a flow chart of an implementation of a method for online status monitoring of a steam isolation valve system provided by the present invention.

[0043] The steam isolation valve system consists of two parts: the valve body and the actuator. The actuator is used to provide power for the steam isolation valve system to realize the opening and closing of the valve. The steam isolation valve system has four main working states, namely stable, fast closing, slow opening, and slow closing: (1) The stable state is the state in which the valve maintains the fully open valve position unchanged. At this time, the monitoring quantities that can be collected include the oil pressure in the upper chamber of the hydraulic cylinder, the oil pressure in the lower chamber of the hydraulic cylinder, the accumulator air pressure, the accumulator oil pressure, the valve action time and displacement; (2) The fast closing state is the state in which the valve performs the fast closing function. The fast closing solenoid valve of the actuator receives the fast closing signal and opens, the control circuit is discharged, and then the oil inlet and return logic valve is opened, the accumulator energy is released, and the hydraulic oil is pushed into the upper chamber of the hydraulic cylinder, and finally the valve is pushed to close. The monitoring quantities that can be collected in this process include the solenoid valve control current, the accumulator air pressure, the pressure of the accumulator ... , accumulator oil pressure, hydraulic cylinder upper chamber oil pressure, hydraulic cylinder lower chamber oil pressure, hydraulic oil temperature, actuator thrust, packing bolt force, valve body thrust, valve action time and displacement; (3) Slow opening state and slow closing state are respectively the slow opening and slow closing of the valve. The two share the same circuit. The driving force of the hydraulic cylinder is provided by the motor and pump. When the slow opening signal is received, the motor and pump start working. At this time, the pump rotates forward, the solenoid valve maintains the original valve position, the hydraulic oil enters the upper chamber of the hydraulic cylinder, and pushes the valve to close slowly. When the slow closing signal is received, the motor and pump start working. At this time, the pump reverses and the solenoid valve position changes. The hydraulic oil enters the lower chamber of the hydraulic cylinder, and drives the valve to open slowly. The monitoring quantities that can be collected during the slow opening and slow closing process include the solenoid valve control current, hydraulic cylinder upper chamber oil pressure, hydraulic cylinder lower chamber oil pressure, hydraulic oil temperature, actuator thrust, packing bolt force, valve body thrust, valve action time and displacement.

[0044] This embodiment provides a method for online monitoring of the status of a steam isolation valve system, and the specific steps are as follows:

[0045] (1) Based on the above principles and combined with the typical failure modes of the steam isolation valve system, a fault tree model of the steam isolation valve system is established, such as Figure 2 As shown. The top event, middle event, and bottom event of the fault tree are associated with the corresponding monitoring quantities. By associating events with monitoring quantities, the monitoring quantities that need to be paid attention to when different faults occur can be obtained. It should be noted that the monitoring quantities include the monitoring quantities of the gas-liquid linkage actuator and the monitoring quantities of the steam isolation valve body. Among them, the monitoring quantities of the gas-liquid linkage actuator include the hydraulic oil temperature, the oil pressure in the upper chamber of the hydraulic cylinder, the oil pressure in the lower chamber of the hydraulic cylinder, the accumulator air pressure, the accumulator oil pressure, the solenoid valve control current, and the thrust of the gas-liquid linkage actuator. The monitoring quantities of the steam isolation valve body include the thrust of the valve body, the packing bolt force, the valve action time, and the valve displacement.

[0046] For example, the top event of the fault tree is the failure of the steam isolation valve system; the intermediate events of the fault tree include valve position change events, valve position non-position events, etc., among which the valve position change event is associated with the valve action time and valve displacement, and the valve position non-position event is associated with the valve action time, valve displacement, and valve stem thrust; the bottom events of the fault tree include hydraulic cylinder leakage events, valve seal failure events, etc., among which the hydraulic cylinder leakage event is associated with the hydraulic cylinder upper chamber oil pressure and the hydraulic cylinder lower chamber oil pressure, and the valve seal failure event is associated with the packing bolt force.

[0047] (2) The monitoring data obtained in step (1) when the steam isolation valve system leaves the factory is used as the baseline data; the time point at which the control signal changes is used as the point at which the working state of the steam isolation valve system changes, and the baseline data is segmented according to the time point; the baseline data of each monitoring quantity after segmentation is subjected to feature classification according to basic characteristics, wherein the basic characteristics include trend, periodicity and autocorrelation.

[0048] This embodiment selects the stable state of the steam isolation valve system. First, the stable state segment of the monitored quantity is obtained according to the control signal, and the benchmark data is subjected to feature classification:

[0049] ① The trend of the baseline data is calculated using the moving average method. Select an appropriate window size and determine the data trend by calculating the mean of the data within the window. If the mean gradually increases (the number of times the mean increases exceeds 5), it indicates an upward trend. If the mean gradually decreases (the number of times the mean decreases exceeds 5), it indicates a downward trend. Furthermore, to account for certain fluctuations in the data during actual operation, the trend index is calculated by dividing the sum of the differences between the mean values ​​of the data within the previous and next windows by the range of allowable data fluctuations. If the trend index is less than 0.5, the monitored variable is considered to have no trend; otherwise, it is considered to have a trend.

[0050] In this embodiment, the trend indicators of the hydraulic cylinder upper chamber oil pressure, the hydraulic cylinder lower chamber oil pressure, the accumulator air pressure, the accumulator oil pressure, and the valve displacement are 1.5×10 -5 , 2.4×10 -6 , 0, 0, 0, are all far less than 0.5, so they do not have trends.

[0051] ② The periodicity of the monitoring quantity benchmark data is measured using the autocorrelation function. If the autocorrelation function graph of the monitoring quantity has obvious regular peaks at different delays, the monitoring quantity is considered to have periodicity. If the monitoring quantity has periodicity, the periodic peak of the monitoring quantity can be obtained based on the autocorrelation function graph.

[0052] Autocorrelation coefficient r k The calculation formula is as follows:

[0053]

[0054] Where x j represents the jth element of the monitoring quantity; N represents the total number of elements contained in the monitoring quantity x, represents the mean of N monitored quantities; k represents the number of lag periods.

[0055] Taking the lag period k as the independent variable, the autocorrelation coefficient r k As the dependent variable, draw the autocorrelation function graph of the monitored quantity. Figure 3 is the autocorrelation function diagram of the oil pressure in the upper chamber of the hydraulic cylinder in this embodiment, Figure 4 and Figure 5 The following are the autocorrelation function graphs for the hydraulic cylinder lower chamber oil pressure and the accumulator oil pressure, respectively. It can be seen that the autocorrelation function graph for the hydraulic cylinder upper chamber oil pressure lacks a clear, regular peak, thus lacking periodicity. The autocorrelation function graphs for the hydraulic cylinder lower chamber oil pressure, accumulator air pressure, accumulator oil pressure, and valve displacement show a steady or rapid decrease within the confidence interval, thus also lacking periodicity.

[0056] ③ The autocorrelation of the monitoring quantity benchmark data is measured by the autocorrelation index. Solve the autocorrelation coefficient r of multiple groups of benchmark data of the same monitoring quantity k The average value is taken to obtain the autocorrelation index of the baseline data of the monitoring quantity. If the autocorrelation index is close to 1, the monitoring quantity is judged to have autocorrelation; otherwise, the monitoring quantity is judged to have no autocorrelation.

[0057] In this example, the autocorrelation indices for the hydraulic cylinder upper chamber oil pressure, hydraulic cylinder lower chamber oil pressure, accumulator air pressure, accumulator oil pressure, and valve displacement are 1, 1, 0.1864, 0.1533, and -0.0889, respectively. This indicates that the hydraulic cylinder upper chamber oil pressure and the hydraulic cylinder lower chamber oil pressure both exhibit strong autocorrelation, while the accumulator air pressure, accumulator oil pressure, and valve displacement exhibit no autocorrelation. This completes the solution and analysis of the data characteristics of the steam isolation valve system at the factory.

[0058] (3) According to the working condition requirements and technical requirements of the steam isolation valve system, it can be obtained that under the stable working state, the first threshold values ​​of the oil pressure in the upper chamber of the hydraulic cylinder and the oil pressure in the lower chamber of the hydraulic cylinder are both <25MPa, the first threshold value range of the accumulator air pressure is 14MPa~30MPa, and the first threshold value range of the accumulator oil pressure is 14MPa~30MPa.

[0059] If the benchmark data of the monitored quantity has trend, periodicity or autocorrelation, a certain margin will be added to the trend index, periodicity peak or autocorrelation index of the benchmark data, which will be used as the trend threshold, periodicity threshold or autocorrelation threshold of the monitored quantity respectively; calculate the variance and mean of multiple groups of benchmark data of all monitored quantities, take the mean as the benchmark and the variance as the margin, and obtain the second threshold of the monitored quantity.

[0060] By selecting the above thresholds, each monitoring variable can obtain at least one threshold range (a second threshold must exist). For monitoring variables with two thresholds, once the monitoring variable monitored during actual operation exceeds one of the thresholds, it is considered an abnormality.

[0061] The threshold values ​​of the upper chamber oil pressure of the hydraulic cylinder, the lower chamber oil pressure of the hydraulic cylinder, the accumulator air pressure, the accumulator oil pressure, and the valve displacement of the steam isolation valve system of this embodiment in a stable working state are shown in Table 1.

[0062] Table 1 Thresholds of various monitoring quantities of the fast-closing channel under stable working conditions

[0063]

[0064] In this embodiment, only the upper and lower chamber oil pressures of the hydraulic cylinder have autocorrelation, so there is an autocorrelation threshold. The first threshold, the autocorrelation threshold, and the second threshold together constitute the thresholds under the stable working state of the steam isolation valve system.

[0065] Calculate the cross-correlation statistics between different monitoring quantities and obtain the cross-correlation threshold.

[0066] The Pearson correlation coefficient is used to characterize the statistical value of the mutual correlation between different monitoring quantities, and the coupling relationship between different monitoring quantities is obtained. The Pearson correlation coefficient r xy The calculation formula is as follows:

[0067]

[0068] Where: x j represents the jth element of the monitoring quantity x; y j represents the jth element of the monitoring quantity y; N represents the total number of elements contained in the monitoring quantity x or y; represents the mean of N monitoring quantities x; Represents the mean of N monitoring quantities y.

[0069] In this embodiment, according to the above-mentioned Pearson correlation coefficient r xy The calculation formula is used to obtain the cross-correlation between different monitoring quantities. It can be found that the oil pressure in the upper chamber of the hydraulic cylinder is negatively correlated with the oil pressure in the lower chamber of the hydraulic cylinder (the coefficient is -1), and there is no obvious correlation between the oil pressure in the upper chamber of the hydraulic cylinder and the oil pressure in the lower chamber of the hydraulic cylinder and the accumulator air pressure, accumulator oil pressure, and valve displacement respectively (the coefficient is between [-0.01, 0.01]), the accumulator air pressure and accumulator oil pressure are positively correlated (the coefficient is 1), and there is no obvious correlation between the accumulator air pressure, the accumulator oil pressure and the valve displacement respectively (the coefficient is unstable).

[0070] (4) Preprocess the monitoring data during the actual operation of the steam isolation valve system to obtain preprocessed monitoring data. The preprocessing includes data cleaning and noise reduction and data format standardization.

[0071] (5) Calculate the trend index, periodic peak value and autocorrelation index of the monitoring data after different preprocessing obtained in step (4). The specific calculation method is the same as in step (2).

[0072] (6) In this embodiment, the stable state of the steam isolation valve system is selected. If the monitoring quantity has a first threshold, the pre-processed monitoring quantity data is compared with the first threshold and the second threshold of the monitoring quantity, and the trend index, periodicity peak value or autocorrelation index calculated in step (5) is respectively compared with the trend threshold, periodicity threshold or autocorrelation threshold of the monitoring quantity;

[0073] If the first threshold does not exist for the monitored quantity, the pre-processed monitored quantity data is directly compared with the second threshold of the monitored quantity, and the trend index, periodicity peak value or autocorrelation index calculated in step (5) is respectively compared with the trend threshold, periodicity threshold or autocorrelation threshold of the monitored quantity;

[0074] If the pre-processed monitoring data falls outside the first or second threshold, or if the calculated trend indicator, periodicity peak, or autocorrelation indicator falls outside the corresponding trend threshold, periodicity threshold, or autocorrelation threshold, the steam isolation valve system is determined to be abnormal. Otherwise, the steam isolation valve system is determined to be operating normally. If an abnormality is detected, the next step is to proceed to the fault diagnosis procedure to determine the specific faulty component in the system.

[0075] Specifically, in this embodiment, the data window is taken as 2000, the lag period is 20, and the characteristic values ​​(trend indicators, periodic peaks or autocorrelation indicators) of the preprocessed monitoring data (hydraulic cylinder upper chamber oil pressure, hydraulic cylinder lower chamber oil pressure, accumulator air pressure, accumulator oil pressure and valve displacement) are calculated respectively, and the characteristic values ​​are compared with the threshold range.

[0076] The monitored data for the upper and lower hydraulic cylinder oil pressures are both within the first and second thresholds, but the trend indicators are 0.7058 and 0.7471, respectively, and both are judged to be on a downward trend, thus demonstrating a trend. However, this data does not match the factory data characteristics (the baseline data for the steam isolation valve system at the factory indicates that the upper and lower hydraulic cylinder oil pressures do not exhibit a trend). The output indicates abnormal upper and lower hydraulic cylinder oil pressures.

[0077] Valve displacement was normal initially, but later reached 0.1031mm, exceeding the upper limit of the second threshold. The trend indicator was 1.9046, indicating an upward trend. However, this was inconsistent with factory data (the baseline data for the steam isolation valve system at the factory indicated no trend in valve displacement), resulting in an abnormal valve displacement result. Accumulator air and oil pressure indicators were all within the threshold range, indicating normal accumulator air and oil pressures.

[0078] At this point, the hydraulic cylinder's upper chamber oil pressure is positively correlated with the hydraulic cylinder's lower chamber oil pressure (coefficient is 1), the hydraulic cylinder's upper and lower chamber oil pressures are negatively correlated with the valve displacement (coefficient is -1), the accumulator air pressure is positively correlated with the accumulator oil pressure (coefficient is 1), and the rest of the data has no significant correlation. This completes the anomaly detection for the current data.

[0079] (7) Associate each monitoring quantity judged abnormal in step (6) with the intermediate event and bottom event in the fault tree of the steam isolation valve system, and index them in the fault tree according to the coupling relationship between each monitoring quantity, trace them back to the specific fault component, and output the fault diagnosis result.

[0080] In this embodiment, the currently detected abnormal monitoring quantities, namely, the hydraulic cylinder upper chamber oil pressure, the hydraulic cylinder lower chamber oil pressure, and the valve displacement, are used as inputs. All events that may be related to the current abnormality are retrieved in the fault tree, including valve position change, hydraulic pressure loss, valve gate breakage, valve stud thread failure, valve nut thread failure, pipeline / pipe fitting leakage, relief valve leakage, hydraulic cylinder internal leakage, and hydraulic cylinder external leakage.

[0081] Among them, valve position change is a first-level intermediate event, hydraulic pressure loss is a second-level intermediate event, and the rest are bottom events and can be fully indexed to the top event of steam isolation valve system failure. However, for the bottom events of valve gate fracture, valve stud thread failure, and valve nut thread failure, although they can cause the valve position to exceed the limit, their valve position does not have a trend. At the same time, there is no significant correlation between the oil pressure in the upper and lower chambers of the hydraulic cylinder and the valve displacement.

[0082] For pipeline / fitting leaks, relief valve leakage, and hydraulic cylinder leakage, the constructed SARIMA (Seasonal Autoregressive Moving Average) model can be used to predict the oil pressure in the upper and lower chambers of the hydraulic cylinder. The oil pressure on one side will tend to approach atmospheric pressure, while the oil pressure on both sides of the hydraulic cylinder will tend to be consistent in the event of internal hydraulic cylinder leakage. By indexing the fault tree from the bottom event to the top event, the final diagnostic output is: hydraulic cylinder internal leakage, hydraulic pressure loss, abnormal valve position change, and steam isolation valve system failure. Without the SARIMA model, the final diagnostic output is: hydraulic cylinder internal leakage, pipeline / fitting leakage, relief valve leakage, hydraulic cylinder leakage, hydraulic pressure loss, abnormal valve position change, and steam isolation valve system failure. Manual inspection is recommended. This completes the diagnostic process.

[0083] The embodiment described above is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Persons skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent substitution or equivalent transformation falls within the scope of protection of the present invention.

Claims

1. A method for online monitoring of the status of a steam isolation valve system, characterized in that: The specific steps are as follows: S1: Construct a fault tree for the steam isolation valve system and list all potential faults; associate the top event, intermediate event, and bottom event of the fault tree with corresponding monitoring quantities; the monitoring quantities include the monitoring quantities of the gas-liquid linkage actuator and the monitoring quantities of the steam isolation valve body; S2: Using the monitoring data of the steam isolation valve system at the time of leaving the factory in step S1 as the baseline data; using the time point of the control signal change as the point of change in the working state of the steam isolation valve system, and segmenting the baseline data according to the time point; and performing feature classification on the segmented baseline data of each monitoring value according to basic characteristics; the basic characteristics include trend, periodicity, and autocorrelation; S3: combining the working condition requirements and technical requirements to obtain a fixed threshold value of some monitoring quantities as the first threshold value of the monitoring quantities; If the baseline data of the monitored quantity has trend, periodicity or autocorrelation, a certain margin will be added to the trend index, periodicity peak or autocorrelation index of the baseline data to serve as the trend threshold, periodicity threshold or autocorrelation threshold of the monitored quantity respectively; Calculate the variance and mean of multiple groups of benchmark data for all monitored quantities, use the mean as the benchmark and the variance as the margin to obtain the second threshold value of the monitored quantity; S4: pre-processing the monitoring quantity monitored during the actual operation of the steam isolation valve system to obtain pre-processed monitoring quantity data; S5: Calculating trend indicators, periodic peak values ​​and autocorrelation indicators of the monitoring data obtained in step S4 after different preprocessing; S6: According to different working states of the steam isolation valve system, the pre-processed monitoring data is compared with the first threshold value or the second threshold value, and the trend index, periodic peak value or autocorrelation index calculated in step S5 is compared with the corresponding trend threshold value, periodic threshold value or autocorrelation threshold value; if any data falls outside the corresponding threshold value range, it is determined that the steam isolation valve system is abnormal; otherwise, it is determined that the steam isolation valve system is operating normally; S7: Associate each monitoring quantity judged to be abnormal in step S6 with the intermediate events and bottom events in the steam isolation valve system fault tree, and index them in the fault tree according to the coupling relationship between each monitoring quantity, trace them back to the specific fault component, and output the fault diagnosis result.

2. The method for online monitoring of the status of a steam isolation valve system according to claim 1, characterized in that: In step S1, the monitoring quantities of the gas-liquid linkage actuator include the hydraulic oil temperature, the oil pressure in the upper and lower chambers of the hydraulic cylinder, the accumulator air pressure, the accumulator oil pressure, the solenoid valve control current and the thrust of the gas-liquid linkage actuator; the monitoring quantities of the steam isolation valve body include the valve body thrust, the packing bolt force, the valve action time and the valve displacement.

3. The method for online status monitoring of a steam isolation valve system according to claim 1, characterized in that: In step S1, the top event of the fault tree is the steam isolation valve system failure event; the intermediate events of the fault tree include valve position change events and valve position non-position events, the valve position change event is associated with the valve action time and valve displacement, and the valve position non-position event is associated with the valve action time, valve displacement, and valve stem thrust; the bottom events of the fault tree include hydraulic cylinder internal leakage events and valve seal failure events, among which the hydraulic cylinder internal leakage event is associated with the hydraulic cylinder upper chamber oil pressure and the hydraulic cylinder lower chamber oil pressure, and the valve seal failure event is associated with the packing bolt force.

4. The method for online status monitoring of a steam isolation valve system according to claim 1, characterized in that: In step S2, the trend of the monitoring quantity benchmark data is calculated using the moving average method. An appropriate window size is selected, and the data trend is determined by calculating the data mean within the window. If the data mean gradually increases, it indicates that the data is on an upward trend; if the data mean gradually decreases, it indicates that the data is on a downward trend. In addition, the trend index is set as the sum of the differences between the data means in the previous and next windows divided by the range of data fluctuation allowed. If the trend index is less than 0.5, it is determined that the monitoring quantity does not have a trend; otherwise, it has a trend. The periodicity of the monitoring quantity benchmark data is measured using the autocorrelation function. If the autocorrelation function graph of the monitoring quantity has regular peaks at different delays, the monitoring quantity is judged to have periodicity, otherwise it does not have periodicity. If the monitoring quantity has periodicity, the periodic peak value of the monitoring quantity is obtained according to the autocorrelation function graph. The autocorrelation of the monitoring quantity benchmark data is measured by the autocorrelation index; the autocorrelation coefficient r of multiple groups of benchmark data of the same monitoring quantity is solved k The average value is taken to obtain the autocorrelation index of the benchmark data of the monitoring quantity. If the autocorrelation index is close to 1, it is considered that the monitoring quantity has autocorrelation, otherwise it does not have autocorrelation.

5. The method for online status monitoring of a steam isolation valve system according to claim 4, characterized in that: The autocorrelation function takes the lag period k as the independent variable, and the autocorrelation coefficient r k plotted as the dependent variable; the autocorrelation coefficient r k The calculation formula is as follows: Where x j represents the jth element of the monitoring quantity; N represents the total number of elements contained in the monitoring quantity x, represents the mean of N monitored quantities; k represents the number of lag periods.

6. The method for online status monitoring of a steam isolation valve system according to claim 1, characterized in that: The monitored quantities include hydraulic oil temperature, hydraulic cylinder upper chamber oil pressure, hydraulic cylinder lower chamber oil pressure, accumulator air pressure, accumulator oil pressure, solenoid valve control current and valve body thrust; among them, the first threshold value range of hydraulic oil temperature is 10℃~55℃, the first threshold value of hydraulic cylinder upper chamber oil pressure and hydraulic cylinder lower chamber oil pressure is <25MPa, the first threshold value range of accumulator air pressure is 14MPa~30MPa, the first threshold value range of accumulator oil pressure is 14MPa~30MPa, the first threshold value range of solenoid valve control current is 0~40mA, and the first threshold value range of valve body thrust is 50kN~3000kN.

7. The method for online status monitoring of a steam isolation valve system according to claim 1, characterized in that: The preprocessing in step S4 includes data cleaning and noise reduction and data format standardization.

8. The method for online status monitoring of a steam isolation valve system according to claim 1, characterized in that: The threshold determination in step S6 is specifically as follows: If the monitored quantity has a first threshold, the preprocessed monitored quantity data is compared with the first threshold and the second threshold of the monitored quantity, and the trend index, periodicity peak value or autocorrelation index calculated in step S5 is compared with the trend threshold, periodicity threshold or autocorrelation threshold of the monitored quantity respectively; If the first threshold does not exist for the monitored quantity, the preprocessed monitored quantity data is directly compared with the second threshold of the monitored quantity, and the trend index, periodicity peak value or autocorrelation index calculated in step S5 is respectively compared with the trend threshold, periodicity threshold or autocorrelation threshold of the monitored quantity; If the pre-processed monitoring data falls outside the first threshold or the second threshold, or the calculated trend index, periodic peak or autocorrelation index falls outside the corresponding trend threshold, periodic threshold, or autocorrelation threshold, it is determined that the steam isolation valve system is abnormal; otherwise, it is determined that the steam isolation valve system is operating normally.

9. The method for online status monitoring of a steam isolation valve system according to claim 1, characterized in that: In step S7, the specific faulty component is traced by using the constructed seasonal difference autoregressive sliding average model.

10. The method for online status monitoring of a steam isolation valve system according to claim 1, characterized in that: The working states of the steam isolation valve system are divided into stable state, fast closing state, slow opening state and slow closing state; If the steam isolation valve system is in a stable state, the monitoring quantities include the oil pressure in the upper chamber of the hydraulic cylinder, the oil pressure in the lower chamber of the hydraulic cylinder, the accumulator air pressure, the accumulator oil pressure, the valve action time and the valve displacement; If the steam isolation valve system is in the fast closing state, the monitoring quantities include the solenoid valve control current, accumulator air pressure, accumulator oil pressure, hydraulic cylinder upper chamber oil pressure, hydraulic cylinder lower chamber oil pressure, hydraulic oil temperature, actuator thrust, packing bolt force, valve body thrust, valve action time and valve displacement; If the steam isolation valve system is in a slow opening or slow closing state, the monitoring quantities involved include the solenoid valve control current, hydraulic cylinder oil pressure, hydraulic oil temperature, actuator thrust, packing bolt force, valve body thrust, valve action time and valve displacement.

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