FPSO hydraulic immersed pump system health assessment method

Abstract

The invention discloses a health assessment method for an FPSO hydraulic immersed pump system. The health assessment method comprises the following steps that firstly, a monitoring point is determined; 2, selecting the data according to the current working condition to obtain an independent variable weighted vector; 3, obtaining a score vector; 4, obtaining a score matrix; 5, obtaining a system-level weighted vector; 6, obtaining a system-level score vector; 7, obtaining a system-level load vector; 8, obtaining a modeling value and an independent variable residual error; 9, collecting data to obtain a system-level weighted vector and an independent variable residual error; tenthly, a square error and a control upper limit value are obtained; eleventhly, judging that the FPSO hydraulic immersed pump system is in a healthy state, a sub-healthy state or a fault state; and the importance of the data is adjusted by using the weighted vector, so that the evaluation is accurate, the participation of unimportant data is reduced, and the operation efficiency is high. Therefore, according to the design, the importance of the monitoring data can be judged, and the operation efficiency is high.

Description

Technical Field

[0001] The invention relates to an FPSO liquid pump system evaluation method, belongs to the field of FPSO liquid pump system evaluation, and in particular to an FPSO hydraulic submersible pump system health evaluation method. Background Art

[0002] FPSO is an offshore floating storage production unit, which is used to centrally process the crude oil produced by each oil well in the oil field, store the processed crude oil in the crude oil tank, and transport the crude oil in the crude oil tank to the oil tanker through the hydraulic submersible pump, and then transport it to the onshore terminal. Therefore, for the entire FPSO hydraulic submersible pump system, it is of great significance to conduct a health status assessment on the equipment.

[0003] A Chinese patent application with application number 202110914545.3 and application date of August 10, 2021 discloses an FPSO intelligent loading monitoring system, which is characterized by including an intelligent monitoring system, an intelligent loading decision system and an autonomous control system. The present invention can realize real-time dynamic monitoring of the liquid cargo status during the operation of FPSO on a unified platform, and use platform data to realize the system's visual operation of multiple different monitoring equipment data, ensuring that FPSO meets various safety indicators such as floating state, stability, and strength during the entire loading and unloading process. Although this design can realize health monitoring of FPSO, it still has the following defects: This design makes numerical judgments on various monitoring data. However, during the operation of the FPSO hydraulic submersible pump system, the importance of various monitoring data is different under different working conditions. This design cannot make key judgments on important monitoring data. Therefore, this design is inaccurate in assessing the system health status under different working conditions.

[0004] The information disclosed in this background technology section is only intended to increase the understanding of the overall background of the application, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects and problems existing in the prior art that the importance of monitoring data cannot be judged, and to provide a FPSO hydraulic submersible pump system health assessment method that can judge the importance of monitoring data.

[0006] To achieve the above objectives, the technical solution of the present invention is: A method for health assessment of a FPSO hydraulic submersible pump system, the method comprising the following steps: Step 1: First, select monitoring points in the submersible pump system, hydraulic system, and electrical system of the FPSO hydraulic submersible pump system, and then collect the independent variable data A and the dependent variable data B of each monitoring point to obtain multiple sets of independent variable data A and dependent variable data B, and then transmit the multiple sets of independent variable data A and dependent variable data B to the server; Step 2: The server first determines the actual working condition of the FPSO hydraulic submersible pump system to determine the current working condition, and then selects the above multiple sets of independent variable data A and dependent variable data B according to the current working condition to obtain independent variable data A1 and dependent variable data B1 that match the current working condition, and then sets the initial value of the weighted vector of the independent variable data A1 by the server, and then multiplies the initial value by the corresponding independent variable data A1 to obtain the independent variable weighted vector; Step 3: The server first multiplies the aforementioned independent variable weight vector with the corresponding independent variable data A1 to obtain a score vector containing maximum effective information, and then the server normalizes the score vector to obtain a standard score vector; Step 4: The server first combines all the aforementioned standard score vectors into a score matrix, and then multiplies the aforementioned score matrix with the corresponding weight vector to obtain a system-level weight vector of the FPSO hydraulic submersible pump system level after integrating the independent variable data A1 under the current working condition; Step 5: The server first multiplies the aforementioned system-level weighted vector by the corresponding score matrix to obtain a system-level score vector reflecting the maximum effective information of the FPSO hydraulic submersible pump system level; Step 6: The server first multiplies the system-level score vector by the corresponding dependent variable data B1 to obtain the load vector reflecting the FPSO hydraulic submersible pump system level after the independent variable data A1 and the dependent variable data B1 are integrated, and then the server multiplies the load vector by the corresponding dependent variable data B1 to obtain an iteration vector, and then the server uses the iteration vector to update the weighted vector to make the initial value of the corresponding weighted vector accurate; Step 7: First, perform steps 1 to 5, and then the server multiplies the system-level score vector, the transposed vector of the system-level score vector, and the corresponding independent variable data A1 to obtain the modeling value. Then, the server uses the corresponding independent variable data A1 to subtract the modeling value to obtain the residual of the independent variable data A1, which is used to reflect the difference between the independent variable data A1 and the modeling value, that is, the degree of fit of the modeling to the independent variable data A1. Step 8: The server first selects the collected independent variable data A to obtain independent variable data A1 matching the current working condition, and then brings the independent variable data A1 into the third step to obtain the standard score variable; then the server brings the new score variable into the fourth step to obtain the system-level weighted vector, and then the server brings the new system-level weighted vector into the seventh step to obtain the residual of the independent variable data A1; Step 9: The server first uses the residual of the independent variable data A1 to subtract the residual of the modeling value, and then squares the result to obtain the square error; then the server sets the inspection level to a specified value, and then selects the data feature evaluation weight value under each working condition, and then the server calculates the upper control limit value. The upper control limit value is: first multiply the data feature evaluation weight value by the mean of the square error, and then divide the result by twice the variance of the square error; Step 10: The server first makes a judgment based on the control upper limit value and square error, including the following three situations: The first one: within a certain period of time, if the number of times the square error is greater than the upper control limit is zero, the FPSO hydraulic submersible pump system is in a healthy state; The second type: within a certain period of time, if the number of times the square error is greater than the control upper limit is greater than zero and less than the specified number, the FPSO hydraulic submersible pump system is in a sub-healthy state and needs attention. The FPSO hydraulic submersible pump system will be repaired and maintained when the shutdown conditions are met; The third type: within a certain period of time, if the number of times the square error is greater than the control upper limit is greater than the specified number of times, or the cumulative time that the square error is greater than the control upper limit is greater than the specified time, the FPSO hydraulic submersible pump system is in a fault state and requires maintenance.

[0007] In the sixth step, the server uses the iteration vector to update the weighted vector by looping the first step to the sixth step multiple times to update the weighted vector multiple times.

[0008] In the first step, the monitoring points selected in the submersible pump system, hydraulic system and electrical system in the FPSO hydraulic submersible pump system are: main pump housing temperature, main pump vibration, main pump wear, submersible pump control pressure, submersible pump outlet pressure, submersible pump outlet valve opening, submersible pump cargo hold temperature and submersible pump outlet flow as monitoring points in the submersible pump system in the FPSO hydraulic submersible pump system; hydraulic system pressure, hydraulic system return oil pressure, hydraulic system oil temperature, hydraulic system oil cleanliness and hydraulic system oil water content are selected as monitoring points in the hydraulic system; and motor front bearing temperature, motor rear bearing temperature, motor winding temperature, motor current and motor bearing vibration are selected as monitoring points in the electrical system.

[0009] In the first step, the independent variable data A and the dependent variable data B of the monitoring point are collected by using a collection card with a certain sampling frequency; In the first step, transmitting the multiple sets of independent variable data A and dependent variable data B to the server is to package the multiple sets of independent variable data and dependent variable data and transmit them to the server in an OPC UA communication manner.

[0010] In the second step, the server selects the above-mentioned multiple groups of independent variable data A and dependent variable data B according to the current working conditions, which includes the server selecting the independent variable data A and dependent variable data B according to the hydraulic system operating condition data, or the server selecting the independent variable data A and dependent variable data B according to the hydraulic submersible pump system operating condition data, or the server selecting the independent variable data A and dependent variable data B according to the hydraulic main pump motor operating condition data, or the server selecting the independent variable data A and dependent variable data B according to the hydraulic submersible pump operating condition data, or the server selecting the independent variable data A and dependent variable data B according to the hydraulic main pump operating condition data.

[0011] In the second step, the server selects the independent variable data A and the dependent variable data B according to the hydraulic system operating condition data, and the server selects the operating status of multiple main pumps, the hydraulic system pressure, the hydraulic system return oil pressure, and the hydraulic system loading operating time as the independent variable data A1, and selects the hydraulic system oil temperature, oil cleanliness, and oil water content as the dependent variable data B1.

[0012] In the second step, the server selects the independent variable data A and the dependent variable data B according to the operating data of the hydraulic submersible pump system. The server selects the submersible pump control pressure, the submersible pump cargo hold liquid level, the submersible pump system pressure, and the submersible pump outlet pressure as the independent variable data A1, and selects the total flow of the hydraulic system and the submersible pump outlet flow as the dependent variable data B1.

[0013] In the second step, the server selects the independent variable data A and the dependent variable data B according to the operating condition data of the hydraulic main pump motor. The server selects the motor load current, the motor front bearing vibration speed, and the motor rear bearing vibration speed as the independent variable data A1, and selects multiple motor winding temperatures, motor front bearing temperatures, and motor rear bearing temperatures as the dependent variable data B1.

[0014] In the second step, the server selects the independent variable data A and the dependent variable data B according to the operating condition data of the hydraulic submersible pump, and the server selects the submersible pump set pressure, the submersible pump cargo hold liquid level, the submersible pump cargo hold temperature, the number of main pump motor operations, and the submersible pump system pressure as the independent variable data A1, and selects the submersible pump control pressure, the submersible pump outlet pressure, and the submersible pump outlet flow as the dependent variable data B1.

[0015] In the second step, the server selects the independent variable data A and the dependent variable data B according to the operating condition data of the hydraulic main pump. The server selects the main pump flow and the hydraulic main pump system pressure as the independent variable data A1, and selects the main pump motor current and the main pump wear monitoring as the dependent variable data B1.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. In a method for health assessment of a FPSO hydraulic submersible pump system of the present invention, the method comprises the following steps: the first step: firstly selecting monitoring points in the submersible pump system, hydraulic system and electrical system of the FPSO hydraulic submersible pump system, and then collecting multiple sets of independent variable data A and dependent variable data B of the monitoring points, and then transmitting them to the server; the second step: firstly selecting the independent variable data A and the dependent variable data B by the server according to the current working condition of the FPSO hydraulic submersible pump system to obtain the independent variable data A1 and the dependent variable data B1 matching the current working condition, and then setting the initial value of the weighted vector, and then multiplying the initial value by the independent variable data A1 to obtain the independent variable weighted vector; the third step: firstly the server weights the independent variable to The server multiplies the independent variable data by the system-level weighted vector to obtain the score vector containing the maximum useful information; the fourth step: the server first merges all the score vectors into a score matrix, and then multiplies the score matrix by the weighted vector to obtain the system-level weighted vector after the independent variable data under all working conditions are integrated; the fifth step: the server multiplies the system-level weighted vector by the score matrix to obtain the system-level score vector reflecting the FPSO hydraulic submersible pump system; the sixth step: the server multiplies the system-level score vector by the matrix of all dependent variable data to obtain the load vector reflecting the FPSO hydraulic submersible pump system level after the independent variable data and the dependent variable data are integrated, and then the server multiplies the load vector by the dependent variable data B1 to obtain the load vector reflecting the FPSO hydraulic submersible pump system level after the independent variable data and the dependent variable data are integrated. Get the iteration vector to update the weighted vector; Step 7: The server first multiplies the system-level score vector, the transposed vector of the system-level score vector, and the independent variable data to obtain the modeling value, and then uses the independent variable data A1 to subtract the modeling value to obtain the residual of the independent variable data A1, which reflects the degree of fit of the modeling to the independent variable data; Step 9: The server first brings the newly collected independent variable data A into the third to fifth steps, and then obtains a new system-level score vector, and then brings the system-level score vector into the eighth step by the server to obtain the residual of the independent variable data A1; Step 10: The server first subtracts the residual of the modeling value in the eighth step from the residual of the independent variable data A1, squares it to obtain the square error, and then sets Determine the inspection level, data feature evaluation weight, and control upper limit value; Step 11: The server first makes a judgment based on the square error and the control upper limit value, including the following three situations: First, if the number of times the square error is greater than the control upper limit value is zero within one day, the FPSO hydraulic submersible pump system is in a healthy state; Second, if the number of times the square error is greater than the control upper limit value is greater than zero and less than the specified number within one day, the FPSO hydraulic submersible pump system is in a sub-healthy state; Third, if the number of times the square error is greater than the control upper limit value is greater than the specified number within one day, or the cumulative time that the square error is greater than the control upper limit value is greater than the specified time, the FPSO hydraulic submersible pump system is in a fault state. The advantages of the present invention also include: First point: the characteristics and features of each working condition are different. For example, the working condition data of the submersible pump system include hydraulic system pressure, submersible pump control pressure, outlet pressure, and cargo hold liquid level, but the important data are the submersible pump control pressure and outlet pressure, and the hydraulic system pressure and cargo hold liquid level are less important data; while in the hydraulic system operating condition data, there are data such as the main pump operating status, hydraulic system pressure, system return oil pressure, and system loading operation time, but the important data in the working condition are the hydraulic system pressure and system return oil pressure, and the others are less important data; therefore, a weighted vector is used to adjust the importance of different independent variable data A1, focusing on judging the more important independent variable data A1. The present invention can judge the importance of the monitoring data, so as to accurately evaluate the system health status under different working conditions or different combined working conditions; Second point: multiple sets of independent variable data A and dependent variable data B are selected according to the working conditions, that is, the independent variable data A1 and dependent variable data B1 related to the current working conditions are selected, thereby reducing the participation of unnecessary data and improving the operating efficiency; Third point: In the first to eighth steps, virtual simulation modeling is performed based on the operating condition characteristics to obtain the normal operation data of the equipment under various operating conditions, and then the actual operation data is judged to be healthy based on the normal operation data; using modeling can obtain more normal operation data without judging the actual operation data based on experience, thus improving the accuracy of judgment; Fourth point: In the sixth step, the load vector carries the maximum amount of information of the FPSO hydraulic submersible pump system, so a more accurate iteration vector can be obtained according to the load vector, and then the iteration vector is used to update the weighted vector to make the weighted vector more accurate, thereby improving the accuracy of modeling; Therefore, the present invention can accurately evaluate the health status of the system under different working conditions and has high operating efficiency.

[0017] 2. In the health assessment method of a FPSO hydraulic submersible pump system of the present invention, in the sixth step, the server uses the iteration vector to update the weighted vector, and the server cycles through the first step to the sixth step multiple times to update the weighted vector multiple times. When applied, the weighted vector is updated multiple times to make the weighted vector gradually more accurate, thereby gradually improving the accuracy of modeling. Therefore, the accuracy of the present invention is high.

[0018] 3. In a FPSO hydraulic submersible pump system health assessment method of the present invention, in the first step, the submersible pump system, hydraulic system, and electrical system in the FPSO hydraulic submersible pump system determine multiple monitoring points, namely, the main pump housing temperature, main pump vibration, main pump wear, submersible pump control pressure, submersible pump outlet pressure, submersible pump outlet valve opening, submersible pump cargo hold temperature, and submersible pump outlet flow rate in the submersible pump system of the FPSO hydraulic submersible pump system are determined as monitoring points; the hydraulic system pressure, hydraulic system return oil pressure, hydraulic system oil temperature, hydraulic system oil cleanliness, and hydraulic system oil water content are determined as monitoring points in the hydraulic system; the motor front bearing temperature, motor rear bearing temperature, motor winding temperature, motor current, and motor bearing vibration are determined as monitoring points in the electrical system; when applied, analyze the faults with higher severity levels to obtain the aforementioned monitoring points, and then judge the faults with higher severity levels through the actual operation data of the monitoring points. Therefore, the present invention has strong pertinence.

[0019] 4. In a FPSO hydraulic submersible pump system health assessment method of the present invention, in the second step, the selection of independent variable data A and dependent variable data B according to the current working condition is to select independent variable data A and dependent variable data B according to the hydraulic system operating condition data, or to select independent variable data A and dependent variable data B according to the hydraulic submersible pump system working condition data, or to select independent variable data A and dependent variable data B according to the hydraulic main pump motor operating condition data, or to select independent variable data A and dependent variable data B according to the hydraulic submersible pump operating condition data, or to select independent variable data A and dependent variable data B according to the hydraulic main pump operating condition data. When applied, the number of independent variable data A1 and dependent variable data B1 is reduced by selection, and the maximum information of system failure under various working conditions can be carried, so the calculation burden of the server is reduced and the judgment speed is accelerated. Therefore, the judgment speed of the present invention is faster. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a structural schematic diagram of the present invention.

[0021] Figure 2 It is a preliminary preparation schematic diagram of the present invention.

[0022] Figure 3 yes Figure 1 Schematic diagram of the structure of the monitoring points.

[0023] Figure 4 yes Figure 3 Schematic diagram of monitoring points for failure of hydraulic submersible pump system in FPSO.

[0024] Figure 5 yes Figure 3Schematic diagram of the monitoring points for main pump motor failure in the FPSO hydraulic submersible pump system.

[0025] Figure 6 yes Figure 3 Schematic diagram of the monitoring points for main pump failure in the FPSO hydraulic submersible pump system.

[0026] Figure 7 yes Figure 3 Schematic diagram of the structure of the monitoring points for submersible pump failure in the FPSO hydraulic submersible pump system.

[0027] Figure 8 yes Figure 3 Schematic diagram of the structure of the main monitoring points.

[0028] Fig. 9 yes Figure 1 Schematic diagram of selection of hydraulic system operating condition data (system level).

[0029] Fig.10 yes Figure 1 Schematic diagram of the selection of operating data (system level) of the hydraulic submersible pump system.

[0030] Fig.11 yes Figure 1 Schematic diagram of the selection of operating condition data (component level) of the hydraulic main pump motor.

[0031] Fig.12 yes Figure 1 Schematic diagram of the selection of operating data (component level) of the hydraulic submersible pump.

[0032] Fig.13 yes Figure 1 Schematic diagram of the selection of operating condition data (component level) of the hydraulic main pump.

[0033] Fig.14 yes Figure 1 Schematic diagram of the structure of the FPSO hydraulic submersible pump system.

[0034] Fig.15 It is a schematic diagram of the application results of the present invention.

[0035] In the figure: hydraulic pump station 1, remote control valve group 11, first emergency stop button 12, cooling water 13, refueling pipe 14, safety zone 15, oil replenishment unit 16, central control room 2, PLC control box 21, danger zone 1 22, safety zone 23, second emergency stop button 24, danger zone zero 3, heating vent valve 4. DETAILED DESCRIPTION

[0036] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0037] See also Figure 1— Fig.15 , a FPSO hydraulic submersible pump system health assessment method, the method comprising the following steps: Step 1: First, select monitoring points in the submersible pump system, hydraulic system, and electrical system of the FPSO hydraulic submersible pump system, and then collect the independent variable data A and the dependent variable data B of each monitoring point to obtain multiple sets of independent variable data A and dependent variable data B, and then transmit the multiple sets of independent variable data A and dependent variable data B to the server; Step 2: The server first determines the actual working condition of the FPSO hydraulic submersible pump system to determine the current working condition, and then selects the above multiple sets of independent variable data A and dependent variable data B according to the current working condition to obtain independent variable data A1 and dependent variable data B1 that match the current working condition, and then sets the initial value of the weighted vector of the independent variable data A1 by the server, and then multiplies the initial value by the corresponding independent variable data A1 to obtain the independent variable weighted vector; Step 3: The server first multiplies the aforementioned independent variable weight vector with the corresponding independent variable data A1 to obtain a score vector containing maximum effective information, and then the server normalizes the score vector to obtain a standard score vector; Step 4: The server first combines all the aforementioned standard score vectors into a score matrix, and then multiplies the aforementioned score matrix with the corresponding weight vector to obtain a system-level weight vector of the FPSO hydraulic submersible pump system level after integrating the independent variable data A1 under the current working condition; Step 5: The server first multiplies the aforementioned system-level weighted vector by the corresponding score matrix to obtain a system-level score vector reflecting the maximum effective information of the FPSO hydraulic submersible pump system level; Step 6: The server first multiplies the system-level score vector by the corresponding dependent variable data B1 to obtain the load vector reflecting the FPSO hydraulic submersible pump system level after the independent variable data A1 and the dependent variable data B1 are integrated, and then the server multiplies the load vector by the corresponding dependent variable data B1 to obtain an iteration vector, and then the server uses the iteration vector to update the weighted vector to make the initial value of the corresponding weighted vector accurate; Step 7: First, perform steps 1 to 5, and then the server multiplies the system-level score vector, the transposed vector of the system-level score vector, and the corresponding independent variable data A1 to obtain the modeling value. Then, the server uses the corresponding independent variable data A1 to subtract the modeling value to obtain the residual of the independent variable data A1, which is used to reflect the difference between the independent variable data A1 and the modeling value, that is, the degree of fit of the modeling to the independent variable data A1. Step 8: The server first selects the collected independent variable data A to obtain independent variable data A1 matching the current working condition, and then brings the independent variable data A1 into the third step to obtain the standard score variable; then the server brings the new score variable into the fourth step to obtain the system-level weighted vector, and then the server brings the new system-level weighted vector into the seventh step to obtain the residual of the independent variable data A1; Step 9: The server first uses the residual of the independent variable data A1 to subtract the residual of the modeling value, and then squares the result to obtain the square error; then the server sets the inspection level to a specified value, and then selects the data feature evaluation weight value under each working condition, and then the server calculates the upper control limit value. The upper control limit value is: first multiply the data feature evaluation weight value by the mean of the square error, and then divide the result by twice the variance of the square error; Step 10: The server first makes a judgment based on the control upper limit value and square error, including the following three situations: The first one: within a certain period of time, if the number of times the square error is greater than the upper control limit is zero, the FPSO hydraulic submersible pump system is in a healthy state; The second type: within a certain period of time, if the number of times the square error is greater than the control upper limit is greater than zero and less than the specified number, the FPSO hydraulic submersible pump system is in a sub-healthy state and needs attention. The FPSO hydraulic submersible pump system will be repaired and maintained when the shutdown conditions are met; The third type: within a certain period of time, if the number of times the square error is greater than the control upper limit is greater than the specified number of times, or the cumulative time that the square error is greater than the control upper limit is greater than the specified time, the FPSO hydraulic submersible pump system is in a fault state and requires maintenance.

[0038] In the sixth step, the server uses the iteration vector to update the weighted vector by looping the first step to the sixth step multiple times to update the weighted vector multiple times.

[0039] In the first step, the monitoring points selected in the submersible pump system, hydraulic system and electrical system in the FPSO hydraulic submersible pump system are: main pump housing temperature, main pump vibration, main pump wear, submersible pump control pressure, submersible pump outlet pressure, submersible pump outlet valve opening, submersible pump cargo hold temperature and submersible pump outlet flow as monitoring points in the submersible pump system in the FPSO hydraulic submersible pump system; hydraulic system pressure, hydraulic system return oil pressure, hydraulic system oil temperature, hydraulic system oil cleanliness and hydraulic system oil water content are selected as monitoring points in the hydraulic system; and motor front bearing temperature, motor rear bearing temperature, motor winding temperature, motor current and motor bearing vibration are selected as monitoring points in the electrical system.

[0040] In the first step, the independent variable data A and the dependent variable data B of the monitoring point are collected by using a collection card with a certain sampling frequency; In the first step, transmitting the multiple sets of independent variable data A and dependent variable data B to the server is to package the multiple sets of independent variable data and dependent variable data and transmit them to the server in an OPC UA communication manner.

[0041] In the second step, the server selects the above-mentioned multiple groups of independent variable data A and dependent variable data B according to the current working conditions, which includes the server selecting the independent variable data A and dependent variable data B according to the hydraulic system operating condition data, or the server selecting the independent variable data A and dependent variable data B according to the hydraulic submersible pump system operating condition data, or the server selecting the independent variable data A and dependent variable data B according to the hydraulic main pump motor operating condition data, or the server selecting the independent variable data A and dependent variable data B according to the hydraulic submersible pump operating condition data, or the server selecting the independent variable data A and dependent variable data B according to the hydraulic main pump operating condition data.

[0042] In the second step, the server selects the independent variable data A and the dependent variable data B according to the hydraulic system operating condition data, and the server selects the operating status of multiple main pumps, the hydraulic system pressure, the hydraulic system return oil pressure, and the hydraulic system loading operating time as the independent variable data A1, and selects the hydraulic system oil temperature, oil cleanliness, and oil water content as the dependent variable data B1.

[0043] In the second step, the server selects the independent variable data A and the dependent variable data B according to the operating data of the hydraulic submersible pump system. The server selects the submersible pump control pressure, the submersible pump cargo hold liquid level, the submersible pump system pressure, and the submersible pump outlet pressure as the independent variable data A1, and selects the total flow of the hydraulic system and the submersible pump outlet flow as the dependent variable data B1.

[0044] In the second step, the server selects the independent variable data A and the dependent variable data B according to the operating condition data of the hydraulic main pump motor. The server selects the motor load current, the motor front bearing vibration speed, and the motor rear bearing vibration speed as the independent variable data A1, and selects multiple motor winding temperatures, motor front bearing temperatures, and motor rear bearing temperatures as the dependent variable data B1.

[0045] In the second step, the server selects the independent variable data A and the dependent variable data B according to the operating condition data of the hydraulic submersible pump, and the server selects the submersible pump set pressure, the submersible pump cargo hold liquid level, the submersible pump cargo hold temperature, the number of main pump motor operations, and the submersible pump system pressure as the independent variable data A1, and selects the submersible pump control pressure, the submersible pump outlet pressure, and the submersible pump outlet flow as the dependent variable data B1.

[0046] In the second step, the server selects the independent variable data A and the dependent variable data B according to the operating condition data of the hydraulic main pump. The server selects the main pump flow and the hydraulic main pump system pressure as the independent variable data A1, and selects the main pump motor current and the main pump wear monitoring as the dependent variable data B1.

[0047] The supplementary description of the present invention is as follows: The first to eighth steps in the present invention are: using digital twin technology, based on the working characteristics of the equipment under various working conditions, to perform virtual simulation modeling on each key sub-equipment or control object, to obtain data on the normal operation of the equipment under various working conditions, and to use the data for PCA modeling; after the equipment completes the initial operation, according to the steps in Example 2, the PCA modeling data is iteratively upgraded, and online or offline analysis of the equipment based on the PCA model is implemented to further improve the stability of the evaluation.

[0048] The preliminary preparation work of the present invention is as follows: Figure 2 First, the FMECA method is used to analyze the FPSO hydraulic submersible pump equipment, and the failure modes of the FPSO hydraulic submersible pump equipment at each level of structure from the whole machine, subsystem, to parts and components are determined. The failure modes of the products caused by any reasons and their undesirable effects and event sequences on the defined analysis system are comprehensively identified and evaluated; the degree of hazard of each failure mode is qualitatively or preliminarily quantitatively analyzed, the degree of hazard of each failure mode related to the normal function or performance of the system is determined, and the priority of locating or mitigating each failure mode and its impact on related processes is determined.

[0049] Embodiment 1: See also Figure 1 — Fig.15 , a FPSO hydraulic submersible pump system health assessment method, the method comprising the following steps: Step 1: First, select monitoring points in the submersible pump system, hydraulic system, and electrical system of the FPSO hydraulic submersible pump system, and then collect the independent variable data A and the dependent variable data B of each monitoring point to obtain multiple sets of independent variable data A and dependent variable data B, and then transmit the multiple sets of independent variable data A and dependent variable data B to the server; Step 2: The server first determines the actual working condition of the FPSO hydraulic submersible pump system to determine the current working condition, and then selects the above multiple sets of independent variable data A and dependent variable data B according to the current working condition to obtain independent variable data A1 and dependent variable data B1 that match the current working condition, and then sets the initial value of the weighted vector of the independent variable data A1 by the server, and then multiplies the initial value by the corresponding independent variable data A1 to obtain the independent variable weighted vector; Step 3: The server first multiplies the aforementioned independent variable weight vector with the corresponding independent variable data A1 to obtain a score vector containing maximum effective information, and then the server normalizes the score vector to obtain a standard score vector; Step 4: The server first combines all the aforementioned standard score vectors into a score matrix, and then multiplies the aforementioned score matrix with the corresponding weight vector to obtain a system-level weight vector of the FPSO hydraulic submersible pump system level after integrating the independent variable data A1 under the current working condition; Step 5: The server first multiplies the aforementioned system-level weighted vector by the corresponding score matrix to obtain a system-level score vector reflecting the maximum effective information of the FPSO hydraulic submersible pump system level; Step 6: The server first multiplies the system-level score vector by the corresponding dependent variable data B1 to obtain the load vector reflecting the FPSO hydraulic submersible pump system level after the independent variable data A1 and the dependent variable data B1 are integrated, and then the server multiplies the load vector by the corresponding dependent variable data B1 to obtain an iteration vector, and then the server uses the iteration vector to update the weighted vector to make the initial value of the corresponding weighted vector accurate; Step 7: First, perform steps 1 to 5, and then the server multiplies the system-level score vector, the transposed vector of the system-level score vector, and the corresponding independent variable data A1 to obtain the modeling value. Then, the server uses the corresponding independent variable data A1 to subtract the modeling value to obtain the residual of the independent variable data A1, which is used to reflect the difference between the independent variable data A1 and the modeling value, that is, the degree of fit of the modeling to the independent variable data A1. Step 8: The server first selects the collected independent variable data A to obtain independent variable data A1 matching the current working condition, and then brings the independent variable data A1 into the third step to obtain the standard score variable; then the server brings the new score variable into the fourth step to obtain the system-level weighted vector, and then the server brings the new system-level weighted vector into the seventh step to obtain the residual of the independent variable data A1; Step 9: The server first uses the residual of the independent variable data A1 to subtract the residual of the modeling value, and then squares the result to obtain the square error; then the server sets the inspection level to a specified value, and then selects the data feature evaluation weight value under each working condition, and then the server calculates the upper control limit value. The upper control limit value is: first multiply the data feature evaluation weight value by the mean of the square error, and then divide the result by twice the variance of the square error; Step 10: The server first makes a judgment based on the control upper limit value and square error, including the following three situations: The first one: within a certain period of time, if the number of times the square error is greater than the upper control limit is zero, the FPSO hydraulic submersible pump system is in a healthy state; The second type: within a certain period of time, if the number of times the square error is greater than the control upper limit is greater than zero and less than the specified number, the FPSO hydraulic submersible pump system is in a sub-healthy state and needs attention. The FPSO hydraulic submersible pump system will be repaired and maintained when the shutdown conditions are met; The third type: within a certain period of time, if the number of times the square error is greater than the control upper limit is greater than the specified number of times, or the cumulative time that the square error is greater than the control upper limit is greater than the specified time, the FPSO hydraulic submersible pump system is in a fault state and requires maintenance.

[0050] Embodiment 2: The basic content is the same as that of Example 1, except that: See also Figure 1 — Fig.15 In the sixth step, the server uses the iteration vector to update the weighted vector by looping the first step to the sixth step multiple times to update the weighted vector multiple times.

[0051] When applied, the server will loop the first to sixth steps multiple times to iterate and update the weighted vector multiple times to make it converge, so as to improve the accuracy of the weighted vector.

[0052] Embodiment 3: The basic content is the same as that of Example 1, except that: See also Figure 1 — Figure 8In the first step, the selection of monitoring points in the submersible pump system, hydraulic system, and electrical system of the FPSO hydraulic submersible pump system is to select the main pump housing temperature, main pump vibration, main pump wear, submersible pump control pressure, submersible pump outlet pressure, submersible pump outlet valve opening, submersible pump cargo hold temperature, and submersible pump outlet flow rate as monitoring points in the submersible pump system of the FPSO hydraulic submersible pump system; the hydraulic system pressure, hydraulic system return oil pressure, hydraulic system oil temperature, hydraulic system oil cleanliness, and hydraulic system oil water content are selected as monitoring points in the hydraulic system; the motor front bearing temperature, motor rear bearing temperature, motor winding temperature, motor current, and motor bearing vibration are selected as monitoring points in the electrical system. In the first step, the acquisition of the independent variable data A and dependent variable data B of the monitoring point is to acquire the independent variable data A and dependent variable data B of the monitoring point through an acquisition card with a certain sampling frequency; in the first step, the transmission of multiple sets of independent variable data A and dependent variable data B to the server is to package multiple sets of independent variable data and dependent variable data and transmit them to the server in OPC UA communication mode.

[0053] When applying, there are four main failure points in the preliminary preparation work. The first failure point is the hydraulic system failure (system level), and its main monitoring points are: hydraulic system pressure monitoring, hydraulic system return oil pressure monitoring, hydraulic system oil temperature monitoring, hydraulic system oil cleanliness monitoring, hydraulic system oil water content monitoring, main pump operating status, main pump continuous operating time; the second failure point is the hydraulic system main pump motor failure (component level), and its main monitoring points are: motor front bearing temperature monitoring, motor rear bearing temperature monitoring, motor current monitoring, motor bearing vibration monitoring; the third failure point is the hydraulic system main pump failure (component level), and its main monitoring points are: main pump housing temperature monitoring, main pump vibration monitoring, motor current monitoring, main pump wear monitoring, main pump flow; the fourth failure point is the hydraulic submersible pump failure (component level), and its main monitoring points are: submersible pump outlet pressure monitoring, submersible pump outlet valve opening monitoring, submersible pump control pressure monitoring , submersible pump cargo hold temperature monitoring, submersible pump outlet flow monitoring; after determining the fault with a higher severity level, the main monitoring points are sorted out as: hydraulic system pressure, hydraulic system return oil pressure, hydraulic system oil temperature, hydraulic system oil cleanliness, hydraulic system oil water content, motor front bearing temperature, motor rear bearing temperature, motor winding temperature, motor current, motor bearing vibration, main pump housing temperature, main pump vibration, main pump wear, submersible pump control pressure, submersible pump outlet pressure, submersible pump outlet valve opening, submersible pump cargo hold temperature, submersible pump outlet flow as monitoring points; among them, a high-frequency acquisition scheme is adopted for dynamic changing signals such as vibration velocity and acceleration, and the sampling frequency of sensors and acquisition cards is not less than 1000 Hz; a low-frequency acquisition scheme is adopted for continuously changing signals such as temperature, torque, speed, tension, etc., and the acquisition frequency is 10 Hz; the collected independent variable data A and dependent variable data B are packaged and processed by the controller and sent to the server via OPC UA communication.

[0054] Embodiment 4: The basic content is the same as that of Example 1, except that: See also Figure 1 — Fig.15, in the second step, the server selects the above multiple groups of independent variable data A and dependent variable data B according to the current working conditions, or the server selects the independent variable data A and dependent variable data B according to the hydraulic system operating condition data, or the server selects the independent variable data A and dependent variable data B according to the hydraulic submersible pump system working condition data, or the server selects the independent variable data A and dependent variable data B according to the hydraulic main pump motor operating condition data, or the server selects the independent variable data A and dependent variable data B according to the hydraulic submersible pump operating condition data, or the server selects the independent variable data A and dependent variable data B according to the hydraulic main pump operating condition data. In the second step, the server selects the independent variable data A and dependent variable data B according to the hydraulic system operating condition data, and the server selects the operating status of multiple main pumps, hydraulic system pressure, hydraulic system return oil pressure, and hydraulic system loading operation time as independent variable data A1, and selects the hydraulic system oil temperature, oil cleanliness, and oil water content as dependent variable data B1. In the second step, the server selects the independent variable data A and the dependent variable data B according to the hydraulic submersible pump system operating data, and selects the submersible pump control pressure, the submersible pump cargo tank liquid level, the submersible pump system pressure, and the submersible pump outlet pressure as the independent variable data A1, and selects the total flow of the hydraulic system and the submersible pump outlet flow as the dependent variable data B1. In the second step, the server selects the independent variable data A and the dependent variable data B according to the hydraulic main pump motor operating data, and selects the motor load current, the motor front bearing vibration speed, and the motor rear bearing vibration speed as the independent variable data A1, and selects multiple motor winding temperatures, motor front bearing temperatures, and motor rear bearing temperatures as the dependent variable data B1. In the second step, the server selects the independent variable data A and the dependent variable data B according to the hydraulic submersible pump operating condition data, and the server selects the submersible pump set pressure, the submersible pump cargo hold liquid level, the submersible pump cargo hold temperature, the main pump motor running number, and the submersible pump system pressure as the independent variable data A1, and the submersible pump control pressure, the submersible pump outlet pressure, and the submersible pump outlet flow as the dependent variable data B1. In the second step, the server selects the independent variable data A and the dependent variable data B according to the hydraulic main pump operating condition data, and the server selects the main pump flow and the hydraulic main pump system pressure as the independent variable data A1, and the main pump motor current and main pump wear monitoring as the dependent variable data B1.

[0055] When applied, by selecting independent variable data A and dependent variable data B, the number of independent variable data A1 and dependent variable data B1 is reduced, and the maximum information of FPSO hydraulic submersible pump system failure can be carried; in the hydraulic system operating condition data, usually, the longer the hydraulic system runs, the greater the pressure of the hydraulic system, and the corresponding state monitoring parameters will have potential changes, so the hydraulic system oil temperature, hydraulic system oil cleanliness and hydraulic system oil water content are defined as dependent variable data A1, and the operating status of multiple main pumps, hydraulic system pressure, hydraulic system return oil pressure and hydraulic system loading operation time are defined as independent variable data B1. These data can reflect abnormal system oil temperature or cooling system failure, etc. Information; In the hydraulic submersible pump system operating data, the FPSO hydraulic submersible pump system includes external transmission conditions, heating conditions, production conditions, fuel conditions and load conditions. There will be multiple operating condition combinations during normal operation. Since each submersible pump has different control pressures and back pressures, the flow required by the motor driving the hydraulic submersible pump is also different, but it is always related to the total flow of the hydraulic system and the outlet flow of the submersible pump. Therefore, the submersible pump control pressure, the liquid level of the submersible pump cargo tank, the submersible pump system pressure, and the submersible pump outlet pressure are defined as independent variable data A1, and the total flow of the hydraulic system and the outlet flow of the submersible pump are defined as dependent variable data B1; In the main pump motor operating condition data, under normal circumstances, the greater the load on the main pump motor, the longer the working time. The longer the motor is, the more likely it is to fail, including abnormal temperature rise caused by reduced insulation of the motor winding, excessive temperature rise or over-high temperature caused by failure of lubrication of the motor bearing. In view of this, multiple motor winding temperatures, motor front bearing temperatures, and motor rear bearing temperatures are used as dependent variable data A1, and motor load current, motor front bearing vibration speed, and motor rear bearing vibration speed are used as independent variable data B1; in the submersible pump operating condition data, there are 6 types of FPSO submersible pumps, including WHP250, WHP200, WHP100P, WHP100, WHP50, and WSP200, which are all core components of the FPSO hydraulic submersible pump system. It is directly related to whether FPSO production and unloading can be normal. It is usually carried out that for each type of single hydraulic submersible pump, the monitoring data directly associated with it include submersible pump control pressure, submersible pump set pressure, submersible pump outlet pressure, submersible pump outlet flow, submersible pump cargo hold liquid level and submersible pump cargo hold temperature, etc., and the indirectly associated data also include submersible pump system pressure and the number of main pump operations, etc.; Under normal circumstances, submersible pumps may have faults such as reduced efficiency as the operating time increases. According to the information obtained in the preliminary preparation, the submersible pump set pressure, submersible pump cargo hold liquid level, submersible pump cargo hold temperature, the number of main pump motor operations, and submersible pump system pressure are used as independent variable data A1, and the submersible pump control pressure, submersible pump outlet pressure, and submersible pump outlet flow are used as dependent variable data B1;In the operating condition data of the hydraulic main pump, the hydraulic main pump is connected to the main pump motor bearing and is the center of the hydraulic system power drive. The monitoring variables that affect the hydraulic main pump include the main pump motor current, system pressure, main pump flow, main pump wear monitoring, etc. The greater the system pressure and main pump flow, the greater the motor output current. Therefore, the hydraulic main pump system pressure and main pump flow are used as independent variable data A1, and the motor current and main pump wear monitoring are used as dependent variable data B1. ;

[0056] Embodiment 5: The basic content is the same as that of Example 1, except that: See also Figure 1 — Fig.15 , the health assessment process for the WHP250 submersible pump operation example is as follows: first obtain the normal data of the WHP250 submersible pump when it is running. The data requirements are: the outlet valve is fully open and the temperature is around 55°C, and the control pressures are 40bar, 50bar, 60bar, 70bar, 80bar, 90bar, 100bar, 110bar, 120bar, 130bar, 140bar, 150bar, 160bar, 170bar, 180bar, 190bar, 200bar, 210bar, 220bar, 230bar, 240bar, 250bar, and 260bar respectively. The above data are calibrated as normal data, and the first to eighth steps are performed to obtain the submersible pump outlet pressure and outlet flow rate. Modeling is performed; then the newly collected data is brought into steps 9 to 10, and then the eleventh step is performed: the server makes a judgment based on the square error and the upper control limit, including the following three situations: First: If the number of times the square error is greater than the upper control limit is zero in a day, the submersible pump is in a healthy state; Second: If the number of times the square error is greater than the upper control limit is greater than zero and less than five times in a day, the FPSO hydraulic submersible pump system is in a sub-healthy state and requires attention. The FPSO hydraulic submersible pump system should be repaired and maintained when the shutdown conditions are met; Third: If the number of times the square error is greater than the upper control limit is greater than five times in a day, or the cumulative time that the square error is greater than the upper control limit is greater than ten minutes, the FPSO hydraulic submersible pump system is in a fault state and requires maintenance; please refer to Fig.15 For example, before the 180th time point, the square error value fluctuates between 20-80 (the center value of the fluctuation is about 50), and at this time the square error value exceeds the upper control limit once, and the submersible pump is in a sub-healthy state; after the 180th time point, the square error value begins to jump, that is, it fluctuates between 80-150 (the center value of the fluctuation is about 115), and the square error value is greater than the upper control limit for more than ten minutes. In this case, the submersible pump is judged to be in a fault state.

[0057] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed by the present invention should be included in the protection scope recorded in the claims.

Claims

1. A method for health assessment of a FPSO hydraulic submersible pump system, characterized in that: The method comprises the following steps: Step 1: First, select monitoring points in the submersible pump system, hydraulic system, and electrical system of the FPSO hydraulic submersible pump system, and then collect the independent variable data A and the dependent variable data B of each monitoring point to obtain multiple sets of independent variable data A and dependent variable data B, and then transmit the multiple sets of independent variable data A and dependent variable data B to the server; Step 2: The server first determines the actual working condition of the FPSO hydraulic submersible pump system to determine the current working condition, and then selects the above multiple sets of independent variable data A and dependent variable data B according to the current working condition to obtain independent variable data A1 and dependent variable data B1 that match the current working condition, and then sets the initial value of the weighted vector of the independent variable data A1 by the server, and then multiplies the initial value by the corresponding independent variable data A1 to obtain the independent variable weighted vector; Step 3: The server first multiplies the aforementioned independent variable weight vector with the corresponding independent variable data A1 to obtain a score vector containing maximum effective information, and then the server normalizes the score vector to obtain a standard score vector; Step 4: The server first combines all the aforementioned standard score vectors into a score matrix, and then multiplies the aforementioned score matrix with the corresponding weight vector to obtain a system-level weight vector of the FPSO hydraulic submersible pump system level after integrating the independent variable data A1 under the current working condition; Step 5: The server first multiplies the aforementioned system-level weighted vector by the corresponding score matrix to obtain a system-level score vector reflecting the maximum effective information of the FPSO hydraulic submersible pump system level; Step 6: The server first multiplies the system-level score vector by the corresponding dependent variable data B1 to obtain the load vector reflecting the FPSO hydraulic submersible pump system level after the independent variable data A1 and the dependent variable data B1 are integrated, and then the server multiplies the load vector by the corresponding dependent variable data B1 to obtain an iteration vector, and then the server uses the iteration vector to update the weighted vector to make the initial value of the corresponding weighted vector accurate; Step 7: First, perform steps 1 to 5, and then the server multiplies the system-level score vector, the transposed vector of the system-level score vector, and the corresponding independent variable data A1 to obtain the modeling value. Then, the server uses the corresponding independent variable data A1 to subtract the modeling value to obtain the residual of the independent variable data A1, which is used to reflect the difference between the independent variable data A1 and the modeling value, that is, the degree of fit of the modeling to the independent variable data A1. Step 8: The server first selects the collected independent variable data A to obtain independent variable data A1 matching the current working condition, and then brings the independent variable data A1 into the third step to obtain the standard score variable; then the server brings the new score variable into the fourth step to obtain the system-level weighted vector, and then the server brings the new system-level weighted vector into the seventh step to obtain the residual of the independent variable data A1; Step 9: The server first uses the residual of the independent variable data A1 to subtract the residual of the modeling value, and then squares the result to obtain the square error; then the server sets the inspection level to a specified value, and then selects the data feature evaluation weight value under each working condition, and then the server calculates the upper control limit value. The upper control limit value is: first multiply the data feature evaluation weight value by the mean of the square error, and then divide the result by twice the variance of the square error; Step 10: The server first makes a judgment based on the control upper limit value and square error, including the following three situations: The first one: within a certain period of time, if the number of times the square error is greater than the upper control limit is zero, the FPSO hydraulic submersible pump system is in a healthy state; The second type: within a certain period of time, if the number of times the square error is greater than the control upper limit is greater than zero and less than the specified number, the FPSO hydraulic submersible pump system is in a sub-healthy state and needs attention. The FPSO hydraulic submersible pump system will be repaired and maintained when the shutdown conditions are met; The third type: within a certain period of time, if the number of times the square error is greater than the control upper limit is greater than the specified number of times, or the cumulative time that the square error is greater than the control upper limit is greater than the specified time, the FPSO hydraulic submersible pump system is in a fault state and requires maintenance.

2. A FPSO hydraulic submersible pump system health assessment method according to claim 1, characterized in that: In the sixth step, the server uses the iteration vector to update the weighted vector by looping the first step to the sixth step multiple times to update the weighted vector multiple times.

3. A FPSO hydraulic submersible pump system health assessment method according to claim 1, characterized in that: In the first step, the monitoring points selected in the submersible pump system, hydraulic system and electrical system in the FPSO hydraulic submersible pump system are: main pump housing temperature, main pump vibration, main pump wear, submersible pump control pressure, submersible pump outlet pressure, submersible pump outlet valve opening, submersible pump cargo hold temperature and submersible pump outlet flow as monitoring points in the submersible pump system in the FPSO hydraulic submersible pump system; hydraulic system pressure, hydraulic system return oil pressure, hydraulic system oil temperature, hydraulic system oil cleanliness and hydraulic system oil water content are selected as monitoring points in the hydraulic system; and motor front bearing temperature, motor rear bearing temperature, motor winding temperature, motor current and motor bearing vibration are selected as monitoring points in the electrical system.

4. A FPSO hydraulic submersible pump system health assessment method according to claim 3, characterized in that: In the first step, the independent variable data A and the dependent variable data B of the monitoring point are collected by using a collection card with a certain sampling frequency; In the first step, transmitting the multiple sets of independent variable data A and dependent variable data B to the server is to package the multiple sets of independent variable data and dependent variable data and transmit them to the server in an OPC UA communication manner.

5. The FPSO hydraulic submersible pump system health assessment method according to claim 1, characterized in that: In the second step, the server selects the above-mentioned multiple groups of independent variable data A and dependent variable data B according to the current working conditions, which includes the server selecting the independent variable data A and dependent variable data B according to the hydraulic system operating condition data, or the server selecting the independent variable data A and dependent variable data B according to the hydraulic submersible pump system operating condition data, or the server selecting the independent variable data A and dependent variable data B according to the hydraulic main pump motor operating condition data, or the server selecting the independent variable data A and dependent variable data B according to the hydraulic submersible pump operating condition data, or the server selecting the independent variable data A and dependent variable data B according to the hydraulic main pump operating condition data.

6. A FPSO hydraulic submersible pump system health assessment method according to claim 5, characterized in that: In the second step, the server selects the independent variable data A and the dependent variable data B according to the hydraulic system operating condition data, and the server selects the operating status of multiple main pumps, the hydraulic system pressure, the hydraulic system return oil pressure, and the hydraulic system loading operating time as the independent variable data A1, and selects the hydraulic system oil temperature, the hydraulic system oil cleanliness, and the hydraulic system oil water content as the dependent variable data B1.

7. A FPSO hydraulic submersible pump system health assessment method according to claim 6, characterized in that: In the second step, the server selects the independent variable data A and the dependent variable data B according to the operating data of the hydraulic submersible pump system. The server selects the submersible pump control pressure, the submersible pump cargo hold liquid level, the submersible pump system pressure, and the submersible pump outlet pressure as the independent variable data A1, and selects the total flow of the hydraulic system and the submersible pump outlet flow as the dependent variable data B1.

8. A FPSO hydraulic submersible pump system health assessment method according to claim 7, characterized in that: In the second step, the server selects the independent variable data A and the dependent variable data B according to the operating condition data of the hydraulic main pump motor. The server selects the motor load current, the motor front bearing vibration speed, and the motor rear bearing vibration speed as the independent variable data A1, and selects multiple motor winding temperatures, motor front bearing temperatures, and motor rear bearing temperatures as the dependent variable data B1.

9. A FPSO hydraulic submersible pump system health assessment method according to claim 8, characterized in that: In the second step, the server selects the independent variable data A and the dependent variable data B according to the operating condition data of the hydraulic submersible pump, and the server selects the submersible pump set pressure, the submersible pump cargo hold liquid level, the submersible pump cargo hold temperature, the number of main pump motor operations, and the submersible pump system pressure as the independent variable data A1, and selects the submersible pump control pressure, the submersible pump outlet pressure, and the submersible pump outlet flow as the dependent variable data B1.

10. A FPSO hydraulic submersible pump system health assessment method according to claim 9, characterized in that: In the second step, the server selects the independent variable data A and the dependent variable data B according to the operating condition data of the hydraulic main pump. The server selects the main pump flow and the hydraulic main pump system pressure as the independent variable data A1, and selects the main pump motor current and the main pump wear monitoring as the dependent variable data B1.

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