CN-116804567-B - Abnormal state dynamic diagnosis system and method for gas leakage monitoring equipment

CN116804567BCN 116804567 BCN116804567 BCN 116804567BCN-116804567-B

Abstract

The invention discloses a dynamic diagnosis system and a method for abnormal state of gas leakage monitoring equipment, the system comprises a monitoring data acquisition unit, a basic data acquisition unit, a data processing unit and an abnormality diagnosis unit. The method comprises the steps of determining gas leakage monitoring equipment and monitoring gas types, obtaining concentration data, wind direction data and time data of the monitoring equipment, combining equipment detection limit and normal concentration of the monitoring gas in ambient air to establish a monitoring data sequence to be diagnosed, calculating a slope coefficient sequence of the concentration data, a trend coefficient sequence of the concentration data, a concentration time distribution sequence and discrete degree thereof, a concentration wind direction distribution sequence and discrete degree thereof, and completing abnormality diagnosis according to the calculated sequences. The system and the method disclosed by the invention are based on the real-time pollution monitoring data and the real-time state parameter monitoring data, and combine the performance indexes of the equipment to dynamically diagnose the abnormal state of the monitoring equipment, so that the monitoring is sensitive, real-time and accurate.

Inventors

LI BO
ZHANG HE
WANG GUOLONG
DONG RUI
JIA RUNZHONG
FENG YUNXIA
DING DEWU

Assignees

中国石油化工股份有限公司
中石化安全工程研究院有限公司

Dates

Publication Date: 20260508
Application Date: 20220318

Claims (19)

1. The method for dynamically diagnosing the abnormal state of the gas leakage monitoring equipment is characterized by comprising the following steps of: Firstly, determining the type of the monitoring gas of the gas leakage monitoring equipment, acquiring concentration data, wind direction data and time data of the monitoring equipment, and establishing a monitoring data sequence to be diagnosed by combining the detection limit of the equipment and the normal concentration of the monitoring gas in the ambient air; Calculating a slope coefficient sequence of concentration data, a trend coefficient sequence of concentration data, a concentration time distribution sequence of the concentration data on time data and a discrete degree thereof, and a concentration wind direction distribution sequence of the concentration data on wind direction data and a discrete degree thereof according to a monitoring data sequence to be diagnosed; Step three, completing abnormality diagnosis according to each calculated sequence and whether pollution sources exist in the periphery; The method for establishing the trend coefficient sequence comprises the following steps: First, calculating moving average data of j-th to i-th concentration data in a monitoring data sequence to be diagnosed When i > H, the method comprises, when i > H, , When H is more than or equal to i is more than or equal to 1, F 0 =f 1 , H is the length of the moving window; for monitoring the first of the data sequences to be diagnosed Concentration data; Then, a trend coefficient sequence is calculated : , ; Wherein, the For moving average data of j-1 to i-1 th concentration data in the monitoring data sequence to be diagnosed, M is the number of the concentration data of the monitoring data sequence to be diagnosed; the method of the third step is as follows: The method comprises the steps of firstly, respectively carrying out anomaly diagnosis on a slope coefficient sequence and a trend coefficient sequence, wherein any result is abnormal, monitoring equipment is abnormal and ended, otherwise judging whether pollution sources exist around the monitoring equipment, if not, the monitoring equipment is normal and ended, if so, respectively carrying out anomaly diagnosis on a concentration time distribution sequence, a discrete degree thereof, a concentration wind direction distribution sequence and a discrete degree thereof, and if any result is abnormal, the monitoring equipment is abnormal and ended, otherwise, the monitoring equipment is normal and ended.
2. The method for dynamically diagnosing an abnormal state of a gas leakage monitoring apparatus according to claim 1, wherein the construction method of the monitoring data sequence to be diagnosed is as follows: the monitoring data sequence to be diagnosed comprises concentration data, wind direction data and time data when the monitoring data are acquired; if the normal concentration is more than 3 times of the equipment detection limit, directly constructing a monitoring data sequence to be diagnosed, wherein the monitoring data sequence comprises three-dimensional parameters of time data, wind direction data and concentration data; Otherwise, screening the concentration data with the value larger than the normal concentration, and combining the wind direction data and the time data which are at the same time with the concentration data to construct a monitoring data sequence to be diagnosed.
3. The method for dynamically diagnosing an abnormal state of a gas leakage monitoring apparatus according to claim 1, wherein the method for establishing the slope coefficient sequence is as follows: slope coefficient sequence The calculation formula of (2) is as follows: , ; Wherein, the For monitoring the first of the data sequences to be diagnosed The concentration data of the individual cells are collected, For monitoring the first of the data sequences to be diagnosed The concentration data of the individual cells are collected, ; The M is the number of the concentration data of the monitoring data sequence to be diagnosed.
4. The method for dynamically diagnosing an abnormal state of a gas leakage monitoring apparatus according to claim 1, wherein the concentration time distribution sequence of the concentration data on the time data and the degree of dispersion thereof are calculated as follows: Firstly, collecting concentration data of monitoring equipment to be diagnosed for a few days, and establishing a monitoring data sequence to be diagnosed; Then dividing the time of 0-24 into 24 or 12 time periods, dividing the collected concentration data into each time period according to the time data in the monitoring data sequence to be diagnosed, and calculating the average value of a plurality of groups of concentration data in each time period; Finally, calculating the dispersion degree s of the concentration data average value in each time period: ; Wherein, the As the concentration average value of the j-th period, P is the number of divided time periods, For bringing into calculation all Average value of (2).
5. The method for dynamically diagnosing an abnormal state of a gas leakage monitoring apparatus according to claim 1, wherein the concentration wind direction distribution sequence of the concentration data on the wind direction data and the degree of dispersion thereof are calculated as follows: Firstly, collecting concentration data of monitoring equipment to be diagnosed for a few days, and establishing a monitoring data sequence to be diagnosed; Then dividing the wind direction of 0-360 degrees into 8, 12 or 16 wind direction sections, dividing the collected concentration data into each wind direction section according to the wind direction data in the monitoring data sequence to be diagnosed, and calculating the average value of a plurality of groups of concentration data in each section; Finally, calculating the dispersion degree r of the average value of the concentration data in each wind direction interval: ; Wherein, the Is the concentration average value of the v-th wind direction interval, Q is the number of divided wind direction sections, For bringing into calculation all Average value of (2).
6. The method for dynamically diagnosing an abnormal state of a gas leakage monitoring apparatus according to claim 1, wherein the specific method of step three is as follows: Step 1, if in the slope coefficient sequence, The data duty cycle exceeds the threshold A or If the data duty ratio of the data is more than the threshold A, judging that the data is abnormal and ending, otherwise, executing the step 2; Step 2, if the trend coefficient sequence is, The data duty cycle exceeds the threshold B or If the data duty ratio of the data is greater than the threshold B, judging that the data is abnormal and ending, otherwise, executing the step 3; Step 3, judging whether pollution sources exist on the periphery, if not, the instrument is normal and is finished, and if so, executing step 4; Step 4, judging the discrete degree of the concentration time distribution sequence, if the obtained discrete degree s is smaller than the threshold C , If the concentration is the average value of the concentration in all the time periods, the dispersion is insufficient, the monitoring equipment is abnormal and diagnosis is finished, otherwise, the step 5 is executed; step 5, judging the discrete degree of the concentration wind direction distribution sequence, if the obtained discrete degree r is smaller than the threshold value D , And if the concentration is the average value of all the wind direction intervals, the dispersion is insufficient, the monitoring equipment is abnormal and diagnosis is finished, otherwise, the monitoring equipment is judged to be normal and diagnosis is finished.
7. The method for dynamically diagnosing an abnormal state of a gas leakage monitoring apparatus according to claim 6, wherein the threshold value a is in a range of 80% to 100%.
8. The method for dynamically diagnosing an abnormal state of a gas leakage monitoring apparatus according to claim 6, wherein the threshold value B is in a range of 80% to 100%.
9. The method for dynamically diagnosing an abnormal state of a gas leakage monitoring apparatus according to claim 6, wherein the threshold value C is in a range of 5% to 50%.
10. The method for dynamically diagnosing an abnormal state of a gas leakage monitoring apparatus according to claim 6, wherein the threshold value D is in a range of 5% to 50%.
11. A method for dynamic diagnosis of an abnormal state of a gas leakage monitoring apparatus according to claim 3, wherein the number of M is not less than the number of real-time data obtained 6 hours after the monitoring to be diagnosed.
12. The method for dynamically diagnosing an abnormal state of a gas leakage monitoring apparatus according to claim 1, wherein the moving window length H has a value of 3 to 6.
13. The method for dynamically diagnosing an abnormal state of a gas leakage monitoring apparatus according to claim 4 or 5, wherein the number of concentration data of the monitoring data series to be diagnosed is a real-time number of data obtained 3 to 7 days from the monitoring apparatus to be diagnosed.
14. The method for dynamic diagnosis of abnormal state of gas leakage monitoring apparatus according to claim 1, wherein if the monitoring apparatus can monitor a plurality of factors simultaneously, each factor needs to be separately diagnosed for abnormality.
15. The method of claim 1, wherein the critical state parameters of the monitoring device are identified as abnormal if they are outside the normal range, and the critical state parameters include critical component pressure, temperature, flow, current, and voltage.
16. The abnormal state dynamic diagnosis system of a gas leakage monitoring apparatus, employing the abnormal state dynamic diagnosis method of a gas leakage monitoring apparatus according to claim 1, characterized by comprising a monitoring data acquisition unit, a basic data acquisition unit, a data processing unit, and an abnormality diagnosis unit, the monitoring data acquisition unit and the basic data acquisition unit respectively inputting the obtained data to the data processing unit, the data processing unit inputting the processed data to the abnormality diagnosis unit.
17. The system for dynamic diagnosis of an abnormal state of a gas leakage monitoring apparatus according to claim 16, wherein the monitoring data acquisition unit comprises a concentration data acquisition module, a wind direction data acquisition module, and a time data acquisition module.
18. The system for dynamic diagnosis of an abnormal state of a gas leakage monitoring apparatus according to claim 16, wherein said basic data acquisition unit comprises an apparatus detection limit acquisition module and a normal concentration in ambient air acquisition module.
19. The system according to claim 16, wherein the data processing unit comprises a slope coefficient sequence processing module, a trend coefficient sequence processing module, a concentration time distribution sequence and a discrete degree processing module thereof, a concentration wind direction distribution sequence and a discrete degree processing module thereof.

Description

Abnormal state dynamic diagnosis system and method for gas leakage monitoring equipment Technical Field The invention relates to the field of environmental pollution monitoring, in particular to a system and a method for dynamically diagnosing abnormal states of gas leakage monitoring equipment. Background With the comprehensive treatment of volatile organic compounds in key industries and the construction of toxic and harmful gas environment risk early warning systems, chemical industry parks and petrochemical enterprises gradually build a gas pollution monitoring network by arranging environment type online monitoring equipment, and the online monitoring of the concentration of pollutants is realized. However, as the service life of the equipment is prolonged, the problems of pollution of a sampling pipeline, failure of a sensor, circuit faults and the like inevitably lead to performance reduction of the equipment, so that monitoring data are abnormal. At present, equipment faults are mostly diagnosed by depending on running state parameters, for example, the equipment is indicated to be in an abnormal state when sampling flow rate is abnormal, temperature is abnormal and pressure is abnormal, and because small-sized equipment does not have state parameter monitoring capability, in addition, monitoring data is abnormal due to sensor failure, sampling pipeline pollution and other reasons, the equipment running state parameters are difficult to diagnose through monitoring, so that a diagnosis method based on the state parameters is relatively narrow in adaptation scene. Therefore, there is a need to develop a method for diagnosing the operating state of a gas leakage monitoring apparatus based on monitoring data, which diagnoses and identifies whether the apparatus is operating normally and effectively on line. Disclosure of Invention In order to solve the technical problems, the invention provides a system and a method for dynamically diagnosing the abnormal state of gas leakage monitoring equipment, which are based on real-time pollution monitoring data and real-time state parameter monitoring data and are used for dynamically diagnosing the abnormal state of the monitoring equipment by combining equipment performance indexes so as to achieve the purpose of accurately and timely identifying the current running state of the equipment. In order to achieve the above purpose, the technical scheme of the invention is as follows: The abnormal state dynamic diagnosis system of the gas leakage monitoring equipment comprises a monitoring data acquisition unit, a basic data acquisition unit, a data processing unit and an abnormal diagnosis unit, wherein the monitoring data acquisition unit and the basic data acquisition unit respectively input obtained data into the data processing unit, and the data processing unit inputs processed data into the abnormal diagnosis unit. In the above scheme, the monitoring data acquisition unit comprises a concentration data acquisition module, a wind direction data acquisition module and a time data acquisition module. In the above scheme, the basic data acquisition unit comprises an equipment detection limit acquisition module and an ambient air normal concentration acquisition module. In the above scheme, the data processing unit includes a slope coefficient sequence processing module, a trend coefficient sequence processing module, a concentration time distribution sequence and a discrete degree processing module thereof, a concentration wind direction distribution sequence and a discrete degree processing module thereof. A dynamic diagnosis method for abnormal state of gas leakage monitoring equipment comprises the following steps: Firstly, determining the type of the monitoring gas of the gas leakage monitoring equipment, acquiring concentration data, wind direction data and time data of the monitoring equipment, and establishing a monitoring data sequence to be diagnosed by combining the detection limit of the equipment and the normal concentration of the monitoring gas in the ambient air; Calculating a slope coefficient sequence of concentration data, a trend coefficient sequence of concentration data, a concentration time distribution sequence of the concentration data on time data and a discrete degree thereof, and a concentration wind direction distribution sequence of the concentration data on wind direction data and a discrete degree thereof according to a monitoring data sequence to be diagnosed; And thirdly, completing abnormality diagnosis according to the calculated sequences and whether pollution sources exist in the periphery. In the above scheme, the construction method of the monitoring data sequence to be diagnosed is as follows: the monitoring data sequence to be diagnosed comprises concentration data, wind direction data and time data when the monitoring data are acquired; if the normal concentration is more than 3 times of the equipment detection limit, dir