Seal fault detection method for explosion-proof tank of offshore platform

Abstract

The invention discloses an offshore platform explosion-proof box sealing fault detection method, and the method comprises the steps: carrying out the real-time collection of the temperature, humidity and pressure data in a box body after the explosion-proof box is sealed through a temperature and humidity sensor and a pressure sensor, and constructing an explosion-proof box sealing fault monitoring and early warning model based on the temperature, humidity and pressure. And judging the safety and sealing performance of the explosion-proof box. After the explosion-proof box is sealed, low-pressure nitrogen pressure regulation is carried out through air holes, a temperature and humidity sensor and a pressure sensor are arranged in the box, temperature, humidity and pressure data in the box are collected, an explosion-proof box sealing fault model based on temperature, humidity and pressure is constructed, and the sealing performance of the explosion-proof box is monitored in real time. And when at least one of the temperature data, the humidity data and the pressure data exceeds a preset corresponding data threshold value in a specified specific time length, judging that a sealing fault occurs in the explosion-proof box. By establishing a data acquisition and prediction method, real-time monitoring and fault prediction of the sealing performance of the explosion-proof box are ensured, and a guarantee is provided for safe operation of offshore equipment.

Description

Technical Field

[0001] The invention relates to a method for detecting sealing faults of an explosion-proof box of an offshore platform, which is applicable to a method for monitoring the safety and sealing performance of an intelligent explosion-proof box in a marine environment. Background Art

[0002] With the rapid development of offshore oil and gas extraction and other marine engineering projects, the safety and stability of offshore equipment have become key issues. As a key component for protecting electrical equipment, the sealing performance of the explosion-proof box is directly related to its explosion-proof effect. Poor sealing may allow external harmful gases to enter, increasing the risk of explosion. Due to the complexity of the marine environment, which is characterized by high humidity, pressure fluctuations and temperature changes, it is difficult for traditional explosion-proof boxes to achieve real-time safety and sealing performance monitoring. Once the explosion-proof box has a sealing failure or abnormal internal temperature, it may cause equipment damage or safety hazards. With the development of intelligent technology, it is of great practical significance to develop an intelligent explosion-proof box detection method that can monitor temperature, humidity and pressure in real time and judge the equipment status through model analysis. Summary of the invention

[0003] The purpose of the present invention is to provide a method for detecting sealing failures of explosion-proof boxes of offshore platforms, which can realize real-time monitoring of the internal environmental state of the explosion-proof boxes, and timely discover and warn potential failures and sealing performance failures.

[0004] The present invention proposes a new sealing fault detection method, which is suitable for explosion-proof boxes of offshore platforms, and is intended to improve the safety and reliability of explosion-proof boxes in offshore environments. The method specifically includes the following steps: filling low-pressure nitrogen into the explosion-proof box through the air holes, and adjusting the pressure in the box to 0.1MPa to ensure the stability of the internal environment of the explosion-proof box. Multiple sensors in the explosion-proof box transmit the collected data to the data processing system through a wireless network or a wired connection to ensure that the data collection frequency is once per second. According to the regression analysis of the real-time data and historical data of the sensor, an explosion-proof box sealing fault monitoring and early warning model based on temperature, humidity and pressure is constructed. When at least one of the temperature, humidity and pressure data exceeds the corresponding data threshold set in advance for more than a specified specific time length, it is judged that a sealing fault occurs inside the explosion-proof box. When a fault is detected or the sealing performance fails, the fault light of the explosion-proof box lights up and an alarm is sounded to remind the staff to perform maintenance. At the same time, the detection data and fault information are recorded in real time.

[0005] Among them, the explosion-proof box sealing failure monitoring and early warning model based on temperature, humidity and pressure judgment includes:

[0006] S301, due to the complex environment of offshore platforms, in order to improve the accuracy of temperature judgment in the explosion-proof box, considering environmental impact and historical data, the real-time corrected box temperature calculation formula of the sensor is as follows:

[0007] T a (t) = T m (t)-β·(T env (t)-T r )

[0008] Where t is the current time, T a (t) is the temperature inside the box after environmental correction, T m (t) is the real-time temperature in the box, T env (t) is the external ambient temperature, T r is the reference temperature under normal working conditions, β is the influence coefficient of ambient temperature on the temperature inside the box, when T a (t)>T S When the temperature in the box is abnormal, the data processing system issues an alarm signal. S is the set maximum threshold temperature.

[0009] S302, in order to improve the accuracy of the model, multivariate linear regression analysis is performed on the temperature data, and a historical temperature change trend model is established to capture the dynamic change characteristics of temperature and improve the reliability of prediction. The specific formula is as follows:

[0010] T pred (t+1)=α1T m (t)+α2T m (t-1)+α3T env (t)+∈

[0011] Where t is the current time, T pred (t+1) is the predicted temperature at the next moment, α1, α2, α3 are regression coefficients, which are obtained based on the fitting of historical data, and ∈ is the error term.

[0012] The invented sealing model based on humidity and pressure data includes:

[0013]

[0014] Where t is the current time, S(t) is the sealing performance index, H(t) and P(t) are the real-time values ​​of humidity and pressure respectively, and H r is the reference humidity, P r is the reference pressure value, γ1, γ2, γ3 are weight coefficients, δ1, δ2, δ3 are nonlinear adjustment coefficients, when S(t)>S max , it is determined that the sealing performance has deteriorated and the data processing system sends out an alarm signal.

[0015] Data and warning information can be displayed to staff in real time through the data processing system's visual interface to show temperature, humidity, pressure changes and alarm status, facilitating quick decision-making.

[0016] The beneficial effects of the present invention are as follows:

[0017] 1. The present invention proposes a method for detecting sealing failures of explosion-proof boxes on offshore platforms. Through real-time data collection by multiple sensors, the temperature, humidity and pressure inside the explosion-proof box can be monitored at any time to ensure that the equipment operates within a safe range.

[0018] 2. The present invention proposes a method for detecting sealing failure of an offshore platform explosion-proof box, which uses a temperature failure model and a sealing performance model to issue an early warning in time before a failure occurs, thereby reducing the losses caused by equipment failure and improving the safety of the equipment;

[0019] 3. The invention proposes a method for detecting sealing failure of explosion-proof boxes on offshore platforms. By analyzing the relationship between temperature, humidity and pressure, the invention realizes a comprehensive evaluation of sealing performance, provides a strong guarantee for the safe operation of the equipment, increases the service life of the explosion-proof boxes in harsh offshore environments, reduces the equipment failure rate, and provides a reliable technical means for the safety of offshore operations.

[0020] 4. The present invention proposes a method for detecting sealing faults of explosion-proof boxes on offshore platforms. The system has a fault recording function, which can automatically record the time, type and duration of the fault, and display the monitoring results to the staff in real time through a visual interface, which is convenient for rapid decision-making and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of a method for detecting the safety and sealing performance of an offshore platform explosion-proof box according to an embodiment of the present invention.

[0022] Figure 2 Schematic diagram of the temperature fault model structure constructed according to the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be described clearly and completely in conjunction with the drawings in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] The present invention proposes a method for detecting sealing failure of an explosion-proof box of an offshore platform. Figure 1 This is a flow chart of the safety and sealing performance testing method of the offshore platform explosion-proof box. The process includes the following six steps:

[0025] S100, fill low-pressure nitrogen into the explosion-proof box through the air hole and adjust the pressure in the box to 0.1MPa;

[0026] S200, the multiple sensors in the explosion-proof box transmit the collected data to the data processing system through a wireless network or a wired connection, and the temperature sensor collects the internal temperature T of the explosion-proof box. m The humidity sensor collects the internal humidity H m The pressure sensor collects the internal air pressure P m , the collection frequency of each data is 1 time / second;

[0027] S300, constructing a monitoring and early warning model for explosion-proof box sealing failure based on temperature, humidity and pressure according to regression analysis of the real-time data and historical data of the multiple sensors, and determining that a sealing failure occurs inside the explosion-proof box when at least one of the temperature, humidity and pressure data exceeds a preset corresponding data threshold for more than a specified period of time;

[0028] S400, when a fault occurs in the explosion-proof box or the sealing performance fails, the fault light of the explosion-proof box lights up and an alarm sounds to remind the staff to perform maintenance;

[0029] S500 records the real-time collected data and faults or failures in the data processing system and displays them in real time on the web page.

[0030] In step S300, the explosion-proof box sealing failure monitoring and early warning model based on temperature, humidity and pressure judgment is constructed, and the implementation steps are as follows: Figure 2 As shown:

[0031] S301, due to the complex environment of offshore platforms, explosion-proof boxes are often exposed. Changes in external ambient temperature will directly affect the measurement of the temperature inside the explosion-proof box. If no correction is made, the real-time temperature may not accurately reflect the working status of the equipment. In an explosion-proof environment, temperature monitoring of equipment is essential, so correcting the temperature helps ensure that the equipment operates within a safe range and avoids accidents. In order to improve the accuracy of temperature judgment inside the explosion-proof box, considering environmental impact and historical data, the real-time corrected temperature calculation formula of the sensor is as follows:

[0032] T a (t) = T m (t)-β·(T env (t)-T r )

[0033] t is the current time, T a (t) is the temperature inside the box after environmental correction, T m (t) is the real-time temperature in the box, T env (t) is the external ambient temperature, T r is the reference temperature under normal working conditions, β is the influence coefficient of ambient temperature on the temperature inside the box, when T a (t)>TS When the temperature in the box is abnormal, the data processing system issues an alarm signal. S is the set maximum threshold temperature.

[0034] After correction, T a (t) It can more truly reflect the actual temperature changes in the explosion-proof box under the complex environment of the offshore platform, eliminate the interference of external environmental fluctuations on temperature monitoring, provide a more reliable data basis, facilitate subsequent analysis and decision-making, and optimize the operation process;

[0035] S302, in order to improve the accuracy of the model, multivariate linear regression analysis is performed on the temperature data to establish a historical temperature change trend model. The specific formula is as follows:

[0036] T pred (t+1)=α1T m (t)+α2T m (t-1)+α3T env (t)+∈

[0037] t is the current time, T pred (t+1) is the predicted temperature at the next moment, α1, α2, α3 are regression coefficients, which are obtained based on the fitting of historical data, and ∈ is the error term.

[0038] By using multivariate linear regression, the historical temperature change trend model can use the historical temperature data of the current moment and before to predict the temperature change at the next moment. It can comprehensively consider the time series change trend of temperature, help the system identify abnormal temperature change trends in advance, and avoid fluctuation errors caused by single moment data. The early prediction of temperature data can provide the system with early warning time, so that maintenance personnel can take timely intervention measures before sealing failure or temperature abnormality occurs, reducing the impact of failure on equipment and personnel.

[0039] In order to improve the accuracy of sealing assessment, when constructing the explosion-proof box sealing failure monitoring and early warning model based on temperature, humidity and pressure judgment, not only the independent changes of humidity and pressure are considered, but also the coupling relationship between them is analyzed, and the following nonlinear correlation model is adopted:

[0040]

[0041] Where t is the current time, S(t) is the sealing performance index, H(t) and P(t) are the real-time values ​​of humidity and pressure respectively, and H r is the reference humidity, P r is the reference pressure value, γ1, γ2, γ3 are weight coefficients, δ1, δ2, δ3 are nonlinear adjustment coefficients, when S(t)>S max , it is determined that the sealing performance has deteriorated and the data processing system sends out an alarm signal.

[0042] Relying on only one of the temperature, humidity or pressure data may not accurately determine the sealing failure. By combining humidity, pressure and other parameters for comprehensive analysis, the sealing model can be adjusted according to the importance of each parameter through different weight coefficients and nonlinear adjustment coefficients, thereby improving the monitoring accuracy and sensitivity of sealing failures.

[0043] Data and warning information can be displayed to staff in real time through the data processing system's visual interface to show temperature, humidity, pressure changes and alarm status, facilitating quick decision-making.

Claims

1. A method for detecting sealing failure of an explosion-proof box of an offshore platform, characterized in that: A plurality of sensors are installed in the explosion-proof box, and the sensors include a temperature sensor, a humidity sensor, and a pressure sensor. The plurality of sensors are used to monitor the temperature, humidity, and pressure changes inside the explosion-proof box in real time. The method includes the following steps: S100, fill low-pressure nitrogen into the explosion-proof box through the air hole and adjust the pressure in the box to 0.1MPa; S200, the multiple sensors in the explosion-proof box transmit the collected data to the data processing system through a wireless network or a wired connection, and the temperature sensor collects the internal temperature T of the explosion-proof box. m The humidity sensor collects the internal humidity H m The pressure sensor collects the internal air pressure P m , the collection frequency of each data is 1 time / second; S300, constructing a monitoring and early warning model for explosion-proof box sealing failure based on temperature, humidity and pressure according to regression analysis of the real-time data and historical data of the multiple sensors, and determining that a sealing failure occurs inside the explosion-proof box when at least one of the temperature, humidity and pressure data exceeds a preset corresponding data threshold for more than a specified period of time; S400, when a fault occurs in the explosion-proof box or the sealing performance fails, the fault light of the explosion-proof box lights up and an alarm sounds to remind the staff to perform maintenance; S500 records the real-time collected data and faults or failures in the data processing system and displays them in real time on the web page.

2. The method according to claim 1, characterized in that: The regression analysis of the real-time data and historical data based on the sensor is used to construct an explosion-proof box sealing failure monitoring and early warning model based on temperature, humidity and pressure, which specifically includes: S301, calculating the real-time corrected value of the temperature inside the box of the sensor, the calculation formula is as follows: T a (t)=T m (t)-β·(T env (t)-T r ) Where t is the current time, T a (t) is the temperature inside the box after environmental correction, T m (t) is the real-time temperature in the box, T env (t) is the external ambient temperature, T r is the reference temperature under normal working conditions, β is the influence coefficient of ambient temperature on the temperature inside the box, when T a (t)>T S When the temperature in the explosion-proof box is abnormal, the data processing system sends out an alarm signal. S is the set maximum threshold temperature; S302, perform multivariate linear regression analysis on the temperature data to establish a historical temperature change trend model. The specific formula is as follows: T pred (t+1)=α1T m (t)+α2T m (t-1)+α3T env (t)+∈ Where t is the current time, T pred (t+1) is the predicted temperature at the next moment, α1, α2, α3 are the regression coefficients obtained based on the historical data fitting, and ∈ is the error term.

3. The method according to claim 1, characterized in that The explosion-proof box sealing failure monitoring and early warning model based on temperature, humidity and pressure adopts the following nonlinear correlation model: Where t is the current time, S(t) is the sealing performance index, H(t) and P(t) are the real-time values ​​of humidity and pressure respectively, and H r is the reference humidity, P r is the reference pressure value, γ1, γ2, γ3 are weight coefficients, δ1, δ2, δ3 are nonlinear adjustment coefficients, When S(t)>S max , it is determined that the sealing performance has deteriorated and the data processing system sends out an alarm signal.

4. The method according to claim 1, characterized in that The data processing system displays temperature, humidity and pressure changes and alarm status to staff in real time through a visual interface to facilitate quick decision-making.

5. The method according to claim 1, characterized in that The temperature and humidity sensor and the pressure sensor are made of corrosion-resistant materials.

6. The method according to claim 1, characterized in that The data processing system has a fault recording function, which records the time of excessive temperature and the time of sealing performance failure in the database, providing reference data for subsequent maintenance.

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