CN113325824B - Regulating valve abnormity identification method and system based on threshold monitoring - Google Patents
Regulating valve abnormity identification method and system based on threshold monitoring Download PDFInfo
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Abstract
The invention relates to a regulating valve abnormity identification method and system based on threshold monitoring, belongs to the field of data analysis and pattern identification, and is used for automatic identification of abnormity and early fault finding in the operation process of a regulating valve. The invention comprises the following steps: s1, a data acquisition module acquires actual valve opening data in corresponding duration; s2, analyzing the actual valve opening data by a data analysis module, counting abnormal characteristic values of the regulating valve and forming an abnormal data file; and S3, the abnormal monitoring module analyzes the abnormal data file, displays the analysis result and forms an abnormal parameter report. The invention is convenient for the staff to check, and can find the information that the actual valve opening data is abnormal in the first time period in time, so as to be convenient for the subsequent maintenance.
Description
Technical Field
The invention relates to a regulating valve abnormity identification method and system based on threshold monitoring, belongs to the field of data analysis and pattern identification, and is used for automatic identification of abnormity and early fault finding in the operation process of a regulating valve.
Background
The regulating valve is an important control device in pipeline equipment in the energy and power industry, and the passing and blocking of a flowing medium in a pipeline can be controlled by giving different valve opening instructions to the regulating valve, so that the running conditions of other devices are directly influenced. If the regulating valve fails, unexpected downtime of the whole system can be caused, and serious economic loss is caused. Therefore, the method has higher practical significance for timely finding and then eliminating the fault of the regulating valve. Normally, the operating parameters of the regulating valve are maintained near the standard values to generate small fluctuation. If the operation parameters of the regulating valve deviate from the normal operation threshold range, the regulating valve is predicted to have certain faults, and a field engineer needs to be prompted to perform corresponding treatment according to actual conditions in time.
At present, with the development of intelligent sensing technology, engineers can conveniently obtain a large amount of operating parameter data of the regulating valve, but the following defects exist in the task of abnormal identification of the regulating valve: some early symptoms of faults of the regulating valve are not obvious, the fault causes are various, the mechanism is relatively complex, and the problems are difficult to find early through manual monitoring and analysis of engineers; the corresponding automatic identification method is lacked in abnormal operation of the regulating valve, an engineer is required to manually analyze and calculate characteristic data in mass data, the work is complicated, the difficulty is high, and the data utilization rate is low; the threshold value of the normal operating range of the regulating valve is determined by engineers according to self experience, corresponding mathematical basis is lacked, and personnel individual difference is large.
Patent document CN110687885A discloses a fault diagnosis method and system for a regulating valve of a first-order constant value control system, which comprises the following steps: different types and different strength faults are set on line for the first-order constant value liquid level control system, and fault characteristic values are analyzed according to discrete data points; acquiring sensor detection data and regulating valve opening data of a normal and stable system on line to obtain a fault detection threshold value, and determining a fault detection algorithm; determining a fault estimation algorithm; establishing a fault qualitative model; example validation failure diagnostic methods and systems. The method has the advantages that faults of different types and intensities are judged and analyzed, different fault qualitative equations need to be set for calculation, the feature extraction requirements of different parameters are high, during data analysis, the faults of different types and different intensities need to be detected so as to extract fault features, the complexity is high, and once a certain parameter is wrong, time and labor are wasted in the aspect of subsequent maintenance.
Disclosure of Invention
The invention aims to provide a regulating valve abnormity identification method and system based on threshold monitoring, belongs to the field of data analysis and pattern identification, and is used for automatic identification and early fault finding of abnormity in the operating process of a regulating valve.
In order to solve the above technical problem, the present application provides a method for identifying an abnormality of a regulating valve based on threshold monitoring, which is characterized by comprising:
s1, a data acquisition module calls actual valve opening data in corresponding duration;
s2, analyzing the actual valve opening data by a data analysis module, counting abnormal characteristic values of the regulating valve and forming an abnormal data file;
and S3, the abnormal monitoring module analyzes the abnormal data file, displays the analysis result and forms an abnormal parameter report.
The data analysis module analyzes the actual valve opening data in the first period of time collected by the data collection module, calculates and judges whether the opening data is abnormal, correspondingly counts the abnormal valve opening data, counts related abnormal characteristic values to form an abnormal data file, wherein the abnormal characteristic values are data information of abnormality in the first period of time in the analysis process, and the abnormal characteristic values are formed into an abnormal data file. At the abnormal monitoring module, the staff can check related information, and the abnormal data file formed by the data analysis module is analyzed to form an abnormal parameter report, so that the staff can check the abnormal information of the actual valve opening data in the first time period in time, and the subsequent maintenance is facilitated. The method has simple process and is easy to realize.
Preferably, before the step S1, the method further includes:
s0, obtaining historical valve opening data in a second time period from the data analysis module to serve as a method training set, and calculating to obtain an abnormal threshold of the historical valve opening data in the second time period.
Preferably, in step S0, a unique time identifier, i.e. a time stamp, is added to each piece of historical valve opening data.
Preferably, the step S0 includes:
s01, calculating the maximum value and the minimum value of historical valve opening data in the second time period;
s02, setting time windows with certain time width and step length, sliding backwards according to the set time width and step length by the initial end of historical valve opening data, and calculating and recording the maximum value, the minimum value and the fluctuation amplitude of the historical valve opening data in each time window;
s03, counting the probability density of the fluctuation amplitude in each time window, drawing a distribution curve of the probability density of the fluctuation amplitude, and taking the abscissa value at the lowest position between normal fluctuation and abnormal fluctuation in the curve as an initial abnormal fluctuation threshold value of historical valve opening data;
s04, drawing a fluctuation amplitude distribution box type graph, verifying whether the initial abnormal fluctuation threshold is appropriate, if so, determining the initial abnormal fluctuation threshold to be an abnormal threshold of historical valve opening data in a second time period, and if not, repeating the step S03.
Preferably, in step S04, if the initial abnormal fluctuation threshold is smaller than the minimum value of the valve fluctuation abnormal value and larger than the maximum value of the normal value of the valve opening, it is determined that the initial adjustment valve abnormal threshold is appropriate.
Preferably, the step S2 includes:
s21, transmitting actual valve opening data, setting a latest time window with a certain time width and step length, and sliding backwards from an initial end of the actual valve opening data according to the set time width and step length to judge whether the regulating valve opening data in the latest time window is abnormal or not;
s22, recording the maximum value, the minimum value, the fluctuation amplitude value, the opening mean value in the latest time window of abnormal opening data of the regulating valve and the starting time and the ending time of the latest time window, and marking the latest time window as a regulating valve operation abnormal window;
s25, counting the number of abnormal operating windows of the regulating valve and abnormal characteristic values including an abnormal maximum value, an abnormal minimum value, an abnormal mean value and an abnormal variance value of actual valve opening data in a first time period, and forming an abnormal data file.
Preferably, in step S21, the maximum value, the minimum value, and the fluctuation amplitude of the actual valve opening data in the latest time window are calculated, and if the fluctuation amplitude is greater than the abnormal threshold, it is determined that the regulating valve opening data in the latest time window is abnormal.
Preferably, after step S22, the method further includes:
and S23, forming a combined time window by taking the middle time of the last latest time window as a starting point and taking the middle time of the current latest time window as an ending point, calculating the maximum value, the minimum value and the fluctuation amplitude of the actual valve opening data in the combined time window, recording the maximum value, the minimum value, the fluctuation amplitude, the opening mean value of the actual valve opening data in the combined window and the starting time and the ending time of the combined time window if the fluctuation amplitude is greater than an abnormal threshold, and marking the combined window as an adjusting valve operation abnormal window.
Preferably, after step S23, the method further includes:
s24, analyzing and checking whether the operation abnormal windows of all the regulating valves are overlapped or not, if the starting time of the operation abnormal window of the current regulating valve is earlier than or equal to the ending time of the operation abnormal window of the previous regulating valve, combining the two times of operation abnormity into one time, modifying the ending time of the operation abnormal window of the previous regulating valve into the ending time of the operation abnormal window of the current regulating valve, and modifying the starting time of the operation abnormal window of the current regulating valve into the starting time of the operation abnormal window of the previous regulating valve.
The application also provides a regulating valve abnormity identification system based on threshold monitoring, and the regulating valve abnormity identification method based on threshold monitoring is used for carrying out regulating valve abnormity identification and comprises the following steps:
the data acquisition module is used for acquiring actual valve opening data;
the data analysis module is connected with the data acquisition module and used for analyzing the actual valve opening data and forming an abnormal data file aiming at abnormal data;
and the abnormal monitoring module is connected with the data analysis module and used for analyzing the abnormal data file to form an abnormal parameter report.
The invention has the following technical effects:
1. and historical valve opening data in the second time period are obtained from the data analysis module, and the abnormal threshold of the historical valve opening data is calculated through analysis of the historical valve opening data and can be used as a basis for judging whether the actual valve opening data is normal or not. The abnormal threshold value is extracted by analyzing historical valve opening data, so that the method is suitable for the service condition of the valve, and the actual applicability is high.
2. And adding a unique time identifier for each historical valve opening data, analyzing according to the time progress, conveniently recording the process of each analysis, and conveniently analyzing and recording.
3. The fluctuation amplitude is analyzed to determine the abnormal threshold of the historical valve opening data, the normal fluctuation frequency of the historical valve opening data is larger than the normal fluctuation frequency during the working period of the ordinary valve, and the corresponding abnormal threshold can be determined through the characteristic. When the abnormal threshold value is calculated, sectional calculation statistics is carried out in a time window mode, data in each time window is independently judged, and finally data in all the time windows are counted to find the abnormal threshold value. The time window can realize that only data in the time window is analyzed, the influence of other data is eliminated, the data analysis is more accurate, the slidability of the time window is realized, the step length of each sliding is consistent with the width of the time window, the continuous analysis can be realized, and the repetition and omission are eliminated.
4. The minimum value of the valve fluctuation abnormal value and the maximum value of the valve opening normal value can be visually determined through the box type graph, the initial abnormal fluctuation threshold value is smaller than the minimum value of the valve fluctuation abnormal value and larger than the maximum value of the valve opening normal value, the initial abnormal fluctuation threshold value can be completely used as a basis for distinguishing whether the fluctuation amplitude of actual valve opening data is normal, and the box type graph is convenient for determining the abnormal threshold value.
5. The actual valve opening data of the first time period is segmented in a time mode in the form of the latest time window, one time segment is analyzed, and the data of each time segment is analyzed independently, so that the analysis accuracy can be improved. And meanwhile, marking the latest time window with abnormal analysis, and finally counting the abnormal characteristic value of the actual valve opening data in the corresponding first time period and the number of the latest time windows with abnormal analysis, so that the abnormal position of the subsequent actual valve opening data can be checked, and the problem of abnormal valve opening can be solved conveniently in the subsequent process.
6. The middle time of the last latest time window is used as a starting point, the middle time of the current latest time window is used as an ending point to form a combined time window, and the actual valve opening data in the combined window is analyzed, so that the problem that fluctuation crosses two latest time windows is avoided, and the problem of omission in analysis is prevented.
7. Whether all the abnormal operation windows of the regulating valves are overlapped or not is checked, the abnormal operation windows of the regulating valves with the overlapping are integrated into one window, repeated analysis and judgment can be prevented, the final statistical result is prevented from being influenced, and the accuracy of the statistical result is improved.
Drawings
Fig. 1 is a flow chart of a method for identifying an abnormality of a regulator valve.
FIG. 2 is a probability density plot of the amplitude of the fluctuations.
FIG. 3 is a boxplot of the amplitude of the undulations.
Fig. 4 is a statistical diagram of anomaly identification.
Detailed Description
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Unless otherwise defined, all terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that the conventional terms should be interpreted as having a meaning that is consistent with their meaning in the relevant art and this disclosure. The present disclosure is to be considered as an example of the invention and is not intended to limit the invention to the particular embodiments.
Example one
A regulating valve abnormity identification method based on threshold monitoring is characterized by comprising the following steps:
s1, a data acquisition module acquires actual valve opening data in a first time period;
s2, analyzing actual valve opening data by a data analysis module, counting abnormal characteristic values of the regulating valve and forming an abnormal data file;
and S3, the abnormal monitoring module analyzes the abnormal data file and forms an abnormal parameter report.
The method is carried out based on the opening data of the regulating valve, whether the opening of the regulating valve exceeds the threshold range or not is judged by judging the opening of the regulating valve, if the opening exceeds the threshold range, the regulating valve is judged to be abnormal, if the opening does not exceed the threshold range, the regulating valve is judged to be normal, and then a parameter report checked by a card is formed for abnormal data and corresponding abnormal information, so that a worker can check the abnormal information in time, corresponding adjustment is carried out according to the abnormal information, and the working efficiency is improved. The threshold range refers to an opening interval of the regulating valve between the minimum value and the maximum value of the opening of the regulating valve, and if the opening is too small, the control quality is poor, and even the control is difficult; if the opening degree is too large, the margin is small, and when the load changes, the control cannot be performed, so that the opening degree data exceeding the threshold range and the information thereof are integrated into a parameter report in time by detecting whether the opening degree data of the regulating valve exceeds the threshold range, and the regulating valve is maintained in time subsequently.
Wherein, still include before step S1:
s0, obtaining historical valve opening data in a second time period from the data analysis module to serve as a method training set, and calculating to obtain an abnormal threshold of the historical valve opening data in the second time period. By comparing the actual valve opening data of the first time period with the abnormal threshold of the second time period, if the corresponding characteristic value (in this embodiment, the corresponding characteristic value is the fluctuation amplitude) of the actual valve opening data of the first time period is greater than the abnormal threshold of the second time period, the actual valve opening data is considered to be abnormal.
When the abnormal threshold value of the second time period is determined, in step S0, a unique time identifier, i.e., a time stamp, is added to each piece of historical valve opening data.
The step S0 includes:
s01, calculating the maximum value and the minimum value of historical valve opening data in a second time period;
s02, setting time windows with certain time width and step length, sliding backwards according to the set time width and step length by the initial end of historical valve opening data, and calculating and recording the maximum value, the minimum value and the fluctuation amplitude of the historical valve opening data in each time window;
s03, counting the probability density of the fluctuation amplitude in each time window, drawing a distribution curve of the probability density of the fluctuation amplitude, and taking the abscissa value at the lowest position between normal fluctuation and abnormal fluctuation in the curve as an initial abnormal fluctuation threshold value of historical valve opening data;
and S04, drawing a fluctuation amplitude distribution box type graph, verifying whether the initial abnormal fluctuation threshold is proper, if so, determining the initial abnormal fluctuation threshold to be an abnormal threshold of historical valve opening data in a second time period, and if not, repeating the step S03.
The fluctuation amplitude value is the difference between the maximum value and the minimum value, the historical valve opening data are analyzed in detail by using a time window (namely a sliding window commonly used in data analysis), only a part of data in the time window is seen at one time, other data can be shielded, and further the data in the time window is analyzed more finely, meanwhile, due to the sliding of the time window, the historical valve opening data in the second time period can be analyzed in a section, omission does not occur, and the accuracy of data analysis is improved. When the time window is set, the width and the step length of the time window are set to be the same, so that each time window moves to be connected with the previous time window, and the analysis result is prevented from being influenced by analyzing or omitting certain historical valve opening data for multiple times. The fluctuation amplitude of historical valve opening data in the time window is analyzed, so that the abnormal threshold value of the regulating valve can be determined conveniently. When the abnormal threshold is determined, the probability density of the fluctuation amplitude in each time window is counted, a probability density distribution curve of the fluctuation amplitude is drawn, as shown in fig. 2, the abscissa is the fluctuation amplitude distribution, the ordinate is the probability density of the amplitude distribution, the probability of the corresponding fluctuation amplitude can be visually checked, according to experience, the higher toggle amplitude probability is determined to be normal fluctuation, and the lower toggle amplitude probability is determined to be abnormal fluctuation, and in fig. 2, according to the conditions of the normal fluctuation and the abnormal fluctuation, the abscissa value 2.3 corresponding to the lowest fluctuation position between the normal fluctuation and the abnormal fluctuation is determined to be an initial abnormal fluctuation threshold of historical valve opening data.
After the initial abnormal fluctuation threshold value is determined, the fluctuation amplitude value distribution box type graph is drawn, the minimum value of the abnormal fluctuation of the valve and the maximum value of the maximum opening degree can be automatically calculated and recorded, as shown in fig. 3, the normal value of the fluctuation of the valve is arranged in the box type graph, the abnormal value of the fluctuation of the valve is arranged beyond the box type graph, the minimum value of the abnormal value of the fluctuation of the valve is arranged at the bottommost part in the box type graph, and the maximum value of the normal value of the opening degree of the valve is arranged at the topmost part in the box type graph. In step S04, if the initial abnormal fluctuation threshold is smaller than the minimum value of the valve fluctuation abnormal value and larger than the maximum value of the normal value of the valve opening, it is determined that the initial regulating valve abnormal threshold is appropriate. And if the selected initial abnormal fluctuation threshold is 2.3, is smaller than the minimum value of the valve fluctuation abnormal value and is larger than the maximum value of the normal value of the valve opening, the selected 2.3 is considered to be the proper abnormal threshold of the historical valve opening data in the second time period.
After the abnormal threshold value is calculated by analyzing the historical valve opening data in the second time period, the actual valve opening data in the first time period is judged:
the step S2 comprises the following steps:
s21, transmitting actual valve opening data, setting a latest time window with a certain time width and step length, and sliding backwards from an initial end of the actual valve opening data according to the set time width and step length to judge whether the regulating valve opening data in the latest time window is abnormal or not;
s22, recording the maximum value, the minimum value, the fluctuation amplitude value, the opening mean value in the latest time window of abnormal opening data of the regulating valve and the starting time and the ending time of the latest time window, and marking the latest time window as a regulating valve operation abnormal window;
s25, counting the number of abnormal operating windows of the regulating valve and abnormal characteristic values including an abnormal maximum value, an abnormal minimum value, an abnormal mean value and an abnormal variance value of actual valve opening data in a first time period, and forming an abnormal data file.
In step S21, the actual valve opening data in the first time period is also finely analyzed by using the sliding window, which is referred to as a latest time window, in step S21, the maximum value, the minimum value, and the fluctuation amplitude of the actual valve opening data in the latest time window are calculated, and if the fluctuation amplitude is greater than the abnormal threshold, it is determined that the regulating valve opening data in the latest time window is abnormal. After the latest time window is judged to be abnormal and marked as the abnormal operation window of the regulating valve, the method further comprises the following steps after the step S22:
and S23, forming a combined time window by taking the middle time of the last latest time window as a starting point and taking the middle time of the current latest time window as an ending point, calculating the maximum value, the minimum value and the fluctuation amplitude of the actual valve opening data in the combined time window, recording the maximum value, the minimum value, the fluctuation amplitude, the opening mean value of the actual valve opening data in the combined window and the starting time and the ending time of the combined time window if the fluctuation amplitude is greater than an abnormal threshold, and marking the combined window as an adjusting valve operation abnormal window. If the fluctuation of the actual valve opening data spans 2 latest time windows, the fluctuation may be ignored, so that a combined time window is formed by taking the middle time of the current latest time window as an end point, and an abnormal judgment is made on the fluctuation amplitude of the actual valve opening data in the combined time window, so as to ensure that the fluctuation span of the actual valve opening data spanning 2 latest time windows is identified and calculated.
After the step S23, the method further includes:
and S24, analyzing and checking whether all the abnormal operation windows of the regulating valves overlap or not, combining the two abnormal operations into one time if the starting time of the abnormal operation window of the current regulating valve is earlier than or equal to the ending time of the abnormal operation window of the previous regulating valve, modifying the ending time of the abnormal operation window of the previous regulating valve into the ending time of the abnormal operation window of the current regulating valve, and modifying the starting time of the abnormal operation window of the current regulating valve into the starting time of the abnormal operation window of the previous regulating valve. The problem that the abnormal operation windows of the multiple regulating valves are overlapped due to the calculation of the combined window in the step S23 is solved, repeated calculation is prevented, and the statistics of subsequent abnormal characteristic values is facilitated.
And finally, counting the number of abnormal operating windows of the regulating valve and abnormal characteristic values including an abnormal maximum value, an abnormal minimum value, an abnormal mean value and an abnormal variance value of the actual valve opening data in the first time period, wherein the counting result is shown in fig. 4.
In this embodiment, the maximum value, the minimum value, the opening average value, and the like of the actual valve opening data may be used as a basis for the abnormality determination, and the corresponding abnormality threshold value may be calculated from the historical valve opening data and compared to determine the abnormality.
Example two
The embodiment further provides a regulating valve abnormality recognition system based on threshold monitoring, and the regulating valve abnormality recognition method based on threshold monitoring according to the first embodiment performs regulating valve abnormality recognition, including:
the data acquisition module is used for acquiring actual valve opening data;
the data analysis module is connected with the data acquisition module and is used for analyzing actual valve opening data and forming an abnormal data file aiming at abnormal data;
and the abnormality monitoring module is connected with the data analysis module and used for analyzing the abnormal data file to form an abnormal parameter report.
The data acquisition module is responsible for receiving the valve opening instruction and controlling the valve opening that the host computer sent, gathers and returns the valve actual operating data of governing valve opening, valve inlet pressure, valve outlet flow etc.. In this embodiment, the data analysis module performs exception judgment on actual valve opening data acquired by the data acquisition module, and counts corresponding data with exception, so as to form an exception data file, and the exception data analysis file acquired by the data analysis module cannot be displayed and checked, so that the exception data analysis file is sent to the exception monitoring module, the exception monitoring module analyzes the exception data file, and then the exception data forms an exception parameter report for a worker to check, and the exception of the regulating valve is found in time.
Although embodiments of the present invention have been described, various changes or modifications may be made by one of ordinary skill in the art within the scope of the appended claims.
Claims (7)
1. A regulating valve abnormity identification method based on threshold monitoring is characterized by comprising the following steps:
s0, obtaining historical valve opening data in a second time period from the data analysis module as a method training set, and calculating to obtain an abnormal threshold of the historical valve opening data in the second time period;
s1, a data acquisition module acquires actual valve opening data in a first time period;
s2, analyzing the actual valve opening data by a data analysis module, counting abnormal characteristic values of the regulating valve and forming an abnormal data file;
s3, an abnormal monitoring module analyzes the abnormal data file and forms an abnormal parameter report;
the step S0 includes:
s01, calculating the maximum value and the minimum value of historical valve opening data in the second time period;
s02, setting time windows with certain time width and step length, sliding backwards according to the set time width and step length by the initial end of historical valve opening data, and calculating and recording the maximum value, the minimum value and the fluctuation amplitude of the historical valve opening data in each time window;
s03, counting the probability density of the fluctuation amplitude in each time window, drawing a distribution curve of the probability density of the fluctuation amplitude, and taking the lowest abscissa value between normal fluctuation and abnormal fluctuation in the curve as an initial abnormal fluctuation threshold of historical valve opening data;
s04, drawing a fluctuation amplitude distribution box type graph, verifying whether the initial abnormal fluctuation threshold is appropriate, if so, determining the initial abnormal fluctuation threshold to be an abnormal threshold of historical valve opening data in a second time period, and if not, repeating the step S03;
the step S2 includes:
s21, transmitting actual valve opening data, setting a latest time window with a certain time width and step length, and sliding backwards from an initial end of the actual valve opening data according to the set time width and step length to judge whether the regulating valve opening data in the latest time window is abnormal or not;
in step S21, the maximum value, the minimum value, and the fluctuation amplitude of the actual valve opening data in the latest time window are calculated, and if the fluctuation amplitude is greater than the abnormal threshold, it is determined that the regulating valve opening data in the combined time window is abnormal.
2. The method for identifying an abnormality of a regulating valve based on threshold monitoring as claimed in claim 1, wherein:
in the step S0, a unique time identifier, i.e., a time stamp, is added to each piece of historical valve opening data.
3. The method for identifying an abnormality of a regulating valve based on threshold monitoring as set forth in claim 1, wherein:
in the step S04, if the initial abnormal fluctuation threshold is smaller than the minimum value of the valve fluctuation abnormal value and larger than the maximum value of the normal value of the valve opening, it is determined that the initial regulating valve abnormal threshold is appropriate.
4. The method for identifying an abnormality of a regulating valve based on threshold monitoring as claimed in claim 1, wherein:
in the step S2, after the step S21, the method further includes:
s22, recording the maximum value, the minimum value, the fluctuation amplitude value, the opening mean value in the latest time window of abnormal opening data of the valve of the regulating valve and the starting time and the ending time of the latest time window, and marking the combined time window as a regulating valve operation abnormal window;
s25, counting the number of abnormal operating windows of the regulating valve and abnormal characteristic values including an abnormal maximum value, an abnormal minimum value, an abnormal mean value and an abnormal variance value of actual valve opening data in a first time period, and forming an abnormal data file.
5. The method for identifying an abnormality of a regulating valve based on threshold monitoring as set forth in claim 4, wherein:
after the step S22, the method further includes:
and S23, forming a combined time window by taking the middle time of the last time window as an initial point and taking the middle time of the current last time window as an end point, calculating the maximum value, the minimum value and the fluctuation amplitude of the actual valve opening data in the combined time window, recording the maximum value, the minimum value, the fluctuation amplitude, the opening mean value of the actual valve opening data in the combined window and the start time and the end time of the combined time window if the fluctuation amplitude is greater than an abnormal threshold, and marking the combined window as an adjusting valve operation abnormal window.
6. The method for identifying an abnormality of a regulating valve based on threshold monitoring as set forth in claim 5, wherein:
after the step S23, the method further includes:
and S24, analyzing and checking whether all the abnormal operation windows of the regulating valves overlap or not, combining the two abnormal operations into one time if the starting time of the abnormal operation window of the current regulating valve is earlier than or equal to the ending time of the abnormal operation window of the previous regulating valve, modifying the ending time of the abnormal operation window of the previous regulating valve into the ending time of the abnormal operation window of the current regulating valve, and modifying the starting time of the abnormal operation window of the current regulating valve into the starting time of the abnormal operation window of the previous regulating valve.
7. A regulating valve abnormality identification system based on threshold monitoring, characterized in that regulating valve abnormality identification is performed based on the regulating valve abnormality identification method based on threshold monitoring as claimed in any one of claims 1 to 6, and comprises:
the data acquisition module is used for acquiring actual valve opening data;
the data analysis module is connected with the data acquisition module and used for analyzing the actual valve opening data and forming an abnormal data file aiming at abnormal data;
and the abnormal monitoring module is connected with the data analysis module and used for analyzing the abnormal data file to form an abnormal parameter report.
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| EP3354885A4 (en) * | 2015-09-25 | 2019-01-02 | Nissan Motor Co., Ltd. | Valve control device |
| JP6660029B2 (en) * | 2017-06-12 | 2020-03-04 | 住友金属鉱山株式会社 | Control valve opening abnormality detection device and opening abnormality detection method |
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| CN112581719B (en) * | 2020-11-05 | 2021-09-03 | 清华大学 | Semiconductor packaging process early warning method and device based on time sequence generation countermeasure network |
| CN112215307B (en) * | 2020-11-19 | 2024-03-19 | 薛蕾 | Method for automatically detecting signal abnormality of earthquake instrument by machine learning |
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