Sensitive Environmental Stress Analysis Method for the A-frame Deployment and Recovery Device of Deep-sea Mining Vessels

Abstract

The present invention provides a method for analyzing sensitive environmental stresses of the A-frame deployment and recovery device of a deep-sea mining ship. Combining the operation tasks and geographical environment of the deep-sea mining ship, the present invention conducts fault data statistics and fault tree analysis on the A-frame deployment and recovery device, and applies a fuzzy clustering model to determine the correlation between faults and sensitive environmental stresses, so as to achieve the accurate positioning and analysis of the sensitive environmental stresses of the A-frame deployment and recovery device of the deep-sea mining ship. The present invention provides a feasible method for analyzing the sensitive environmental stresses of the A-frame deployment and recovery device of the deep-sea mining ship, and plays an important role in improving the use efficiency, safety and reliability of deep-sea mineral development ships.

Description

Technical Field

[0001] The present invention relates to a method for analyzing sensitive environmental stresses of a A-frame deployment and recovery device of a deep-sea mining ship. Background Art

[0002] An offshore mining ship is a special ship equipped with mining and ore dressing equipment for mining seabed surface sedimentary minerals. Usually, an offshore mining ship with a mining operation water depth greater than 2000m is called a deep-sea mining ship. The A-frame deployment and recovery device is an important deployment and recovery equipment for deep-sea mineral development equipment. It consists of two upright and one horizontal rod and can rotate around its root, with a frame structure similar to the letter A in shape. The A-frame deployment and recovery device can pour out the survey instruments and equipment suspended on it outside the ship or turn them into the ship by means of a hydraulic cylinder or other mechanical devices. The operation production environment of the A-frame deployment and recovery device is generally the deep ocean. The water surface environment is extremely complex, with frequent occurrence of extreme harsh environments, and multiple environmental stress factors such as wind, wave, current, temperature, salinity, and high pressure are coupled, and the change uncertainty is relatively strong. The failure probability of materials, components, and systems induced by extreme harsh environments and uncertain environmental conditions is relatively high, and it needs to operate at a fixed position for up to 25 years. When a failure occurs, onshore real-time support maintenance cannot be carried out, and some serious failures and faults may lead to significant economic losses and even casualties.

[0003] Since 2021, deep-sea mining ships have been put into operation worldwide. Compared with ships such as scientific research ships and exploration ships, the A-frame deployment and recovery device of deep-sea mining ships has a larger lifting capacity, a more complex system, and more frequent operations in the open sea. Therefore, conventional methods cannot grasp the failure modes and characteristics of the A-frame deployment and recovery device of deep-sea mining ships under the action of complex environmental stresses such as vibration, shock, temperature, humidity, salt spray, and tilt and swing, and there are relatively large potential safety hazards. Summary of the Invention

[0004] In order to grasp the failure modes and characteristics of the A-frame deployment and recovery device of deep-sea mining ships under the action of complex environmental stresses, the present invention combines the operation tasks and geographical environment of deep-sea mining ships to establish a method for analyzing sensitive environmental stresses of the A-frame deployment and recovery device of deep-sea mining ships, realizing the accurate positioning and analysis of sensitive environmental stresses of the A-frame deployment and recovery device of deep-sea mining ships, and improving the use efficiency, safety, and reliability of deep-sea mineral development ships.

[0005] The object of the present invention is achieved through the following solutions:

[0006] A method for analyzing sensitive environmental stresses of a A-frame deployment and recovery device of a deep-sea mining ship, comprising the following steps:

[0007] Step 1: Determine the main working area and geographical location of the A-frame deployment and recovery device of the deep-sea mining ship according to the different ocean areas where different types of mined minerals are located.

[0008] Step 2: Determine the main operation task types and operation profiles of the A-frame deployment and recovery device of the deep-sea mining ship according to the main working area and geographical location determined in Step 1:

[0009] The main operation task type is the time statistic of the A-frame deployment and recovery device of the deep-sea mining ship performing different types of tasks in a specific ocean geographical location area.

[0010] The operation profile is the proportion of time that the A-frame deployment and recovery device of the deep-sea mining ship spends performing different types of tasks in a specific ocean geographical location area.

[0011] Among them, the main operation tasks include one or more of dynamic positioning drilling tasks, mooring tasks, cruising tasks, dock maintenance tasks, actual drilling operation tasks, underwater maintenance tasks, dynamic positioning maintenance tasks, and waiting tasks in bad weather.

[0012] Step 3: Determine the main natural environmental stresses and induced environmental stresses of the A-frame deployment and recovery device of the deep-sea mining ship:

[0013] Among them, the natural environmental stresses are determined according to the specific ocean geographical location in Step 1, and include one or more of temperature environmental stress, humidity environmental stress, and salt spray environmental stress.

[0014] The induced environmental stress is the stress induced by the natural environmental stress, and is determined by referring to the operation task type and operation profile in Step 2, and includes vibration environmental stress and shock environmental stress.

[0015] Step 4: Fault data statistics and fault tree analysis of the A-frame deployment and recovery device of the deep-sea mining ship

[0016] Based on the statistical data of the actual fault cases of the A-frame deployment and recovery device, carry out fault data statistics and fault tree analysis of the A-frame deployment and recovery device of the deep-sea mining ship.

[0017] Step 4.1 Fault data statistics: Statistically analyze the specific equipment objects where the A-frame deployment and recovery device fails, the specific failure phenomena, the specific failure reasons, and the associated environmental stresses when the failure occurs.

[0018] The types of fault data statistics of the A-frame deployment and recovery device include mechanical fault, hydraulic fault, and electrical control fault data statistics.

[0019] Step 4.2 Fault Tree Analysis: Using the fault tree analysis method, conduct fault tree analysis on the mechanical faults, hydraulic faults, and electrical control faults of the main equipment of the A-frame deployment and recovery device respectively.

[0020] The subsystems of the A-frame deployment and recovery device include a hydraulic power system, an A-frame system, a winch system for deployment and recovery equipment, and an equipment control and operation system.

[0021] Through fault statistics data and fault tree analysis, determine the types of associated environmental stresses that cause faults.

[0022] Step 5: Fuzzy clustering analysis of fault occurrence frequency and severity, and Apriori association rule analysis of fault-environment stress:

[0023] Based on the fault tree of the deployment and recovery system obtained from Step 4, establish an FCM-AR model to locate the fault stress. The specific steps are as follows:

[0024] Step 5.1: Fuzzy clustering analysis of fault occurrence frequency and severity:

[0025] Obtain the fault information of each device of the A-frame deployment and recovery device, and organize and extract the occurrence frequency and severity of the vibration environmental stress dimension, impact environmental stress dimension, temperature environmental stress dimension, humidity environmental stress dimension, and salt spray environmental stress dimension from it.

[0026] Conduct fuzzy clustering analysis on the fault occurrence frequency and severity to obtain the corresponding membership matrix, and determine the classification of the severity of each fault in the sample.

[0027] Determine the number C of cluster centers, and input the original data of the fault occurrence frequency and fault severity.

[0028] Among them, the number C of cluster centers is a process of continuously iteratively calculating the membership degree u i,j and the cluster center c j until they reach the optimal state, as shown in Equation (1):

[0029]

[0030] For a single sample x i , the sum of its membership degrees to each cluster is 1; where m is the number of membership degrees u i,j , and N is the number of samples x;

[0031] When the iteration satisfies Equation (2), stop the iteration:

[0032]

[0033] Among them, k is the number of iteration steps, maxij is the error threshold, ε is the selected tolerance value, and the process converges to the local minimum or saddle point of the target J m .

[0034] According to the fuzzy clustering algorithm, calculate the membership matrix of the fault degree, and determine the categories corresponding to the occurrence frequency and severity of each sample.

[0035] Step 5.2: Determine the horizontal and vertical correlation relationships of the A-frame deployment and recovery device, and conduct fault-environment stress Apriori correlation rule analysis:

[0036] The horizontal correlation relationship of the A-frame deployment and recovery device is the relationship between subsystems.

[0037] The subsystems of the A-frame deployment and recovery device include a hydraulic power system, an A-frame system, a winch system for deployment and recovery equipment, and an equipment control and operation system.

[0038] The vertical correlation relationship of the A-frame deployment and recovery device is the influence relationship between the equipment in each subsystem.

[0039] The equipment of the A-frame deployment and recovery device includes a telescopic frame, a swing frame, a docking device, a pitch buffer device, a roll buffer device, a hydraulic power station, a hydraulic valve cabinet, a control cabinet beside the machine, a remote control device, a hydraulic motor, a normally closed safety brake, a planetary reducer, a cable dispenser, and an optical fiber slip ring.

[0040] Obtain the horizontal correlation relationship between the subsystems of the A-frame deployment and recovery device and the vertical upstream and downstream correlation coupling relationship between the mutual equipment according to the subsystem classification and equipment classification of the A-frame deployment and recovery device.

[0041] Set the parameter a VR indicating the degree of influence between the equipment in each subsystem of the A-frame deployment and recovery device.

[0042] The equipment included in the deployment and recovery subsystem is a ij , indicating the degree of influence of equipment i on equipment j, and using a ji to indicate the degree of influence of equipment j on equipment i.

[0043] Define the mutual influence degree between the equipment in each subsystem of the A-frame deployment and recovery device through the relationship influence matrix A.

[0044] The initial value of A is shown in Equation (3):

[0045]

[0046] For the influence degree of equipment i on equipment j of the A-frame deployment and recovery device, it can be obtained through the state value X of equipment i iThe influence degree a from device i to device j ij is defined as the product of X i and a ij . Conversely, the influence of device j on device i is expressed as X j and a ji .

[0047] Let the matrix representing the influence relationship between the subsystems of the A-frame laying and recovery device be M. Since a ij and a ji are not equal, the matrix M is an asymmetric matrix.

[0048] Apply the fault-environment stress Apriori association rule to analyze the dependency relationship between the information of each dimension of the fault event, and obtain the influence evaluation result F of each environmental stress factor on the faults of each subsystem.

[0049] The matrix M representing the influence relationship between the subsystems of the A-frame laying and recovery device is obtained by multiplying the influence evaluation result F of each environmental stress factor on the faults of each subsystem and the relationship influence matrix A between the devices in each subsystem of the A-frame laying and recovery device, M = F·A.

[0050] According to the obtained influence relationship between the subsystems of the A-frame laying and recovery device, combined with the fuzzy clustering analysis of the fault occurrence frequency and severity, and the fault-environment stress Apriori association analysis, the association relationship between the fault of a certain subsystem of the A-frame laying and recovery device and the sensitive environmental stress is obtained, so as to obtain which sensitive environmental stress has a high correlation with the occurrence of the fault of this subsystem.

[0051] Step 6: Finite element simulation verification of sensitive environmental stress

[0052] Apply finite element simulation to verify the analysis results obtained in step 5. The loads of the A-frame laying and recovery device are respectively modeled and simulated according to 5 types of environmental stresses, and this kind of environmental stress is verified through finite element calculations such as static, dynamic and temperature. When performing the finite element analysis, the boundary conditions are constrained according to the specific design drawings, and linear simulation or non-linear simulation is selected according to the situation.

[0053] Preferably, in step 3, the environmental stresses of temperature, humidity, salt spray, vibration and shock are determined in the following way: preferably use the data obtained by actual measurement. If there is no actual measurement data, it is also possible to consider using the standard data for analysis. The reference standard data include GJB / 1060.2-1991 "Requirements for Ship Environmental Conditions: Climatic Environment", China Classification Society "Rules for the Classification of Offshore Floating Installations" (2020), GBT13853-1992 "Technical Conditions for Marine Hydraulic Pumps and Hydraulic Motors" and other specifications.

[0054] Preferably, in step 6, the boundary conditions of the finite element analysis are constrained according to the specific design drawings. For the equipment installed on the deck pedestal of the deep-sea mining ship, the boundary effect of the deck is considered, and for the equipment installed on the strong structure of the deep-sea mining ship, the fixed support boundary condition is set.

[0055] Preferably, in step 6, the finite element analysis selects linear or nonlinear simulation for verification according to the intensity of 5 environmental loads, confirms the main deformation and distribution characteristics, verifies the high-risk areas of the equipment response, and clarifies the main failure modes and characteristics of the equipment. When the environmental load is a load with small intensity, linear simulation is used for verification to confirm the main deformation and distribution characteristics and verify the high-risk areas of the equipment response; when the environmental load is a load with large intensity, nonlinear simulation verification is carried out to clarify the main failure modes and characteristics of the equipment.

[0056] Preferably, in step 6, the finite element analysis is carried out using the finite element mesh model. The CAD model of the A-frame deployment and recovery device is consistent with the original design drawings. General CAD modeling software is used for modeling, and the number of triangular and deformed meshes in the finite element mesh is controlled below 5%. The finite element analysis is carried out in the order of first analyzing the coarse mesh model and then the fine mesh model. The coarse mesh model is modeled and analyzed according to the mode of beam element + shell element, and the fine mesh model is modeled and analyzed according to the mode of all shell elements.

[0057] Preferably, in step 6, the measured data load is preferentially selected for loading during the simulation analysis. When there is no measured environmental stress load, the loading is carried out with reference to the specifications given in step 3.

[0058] Preferably, in step 6, when using the method of nonlinear simulation verification, it can be implicit finite element calculation or explicit finite element simulation.

[0059] Advantages of the present invention:

[0060] By statistically analyzing the failure data of the A-frame deployment and recovery device, the present invention quantitatively establishes the correlation between the failure of the A-frame deployment and recovery device and the sensitive environmental stress, and gives a general method and process for the sensitive environmental stress analysis and verification of the A-frame deployment and recovery device of the deep-sea mining ship, which is of great significance for guiding the failure mode of the A-frame deployment and recovery device and mastering its failure law. Description of the drawings

[0061] Figure 1 It is a flowchart of the method for analyzing the sensitive environmental stress of the A-frame deployment and recovery device of a deep-sea mining ship according to the present invention;

[0062] Figure 2 It is a schematic diagram of the steps of the fault tree analysis of the A-frame deployment and recovery device in the present invention;

[0063] Figure 3 Schematic diagram of the specific implementation steps of the FCM-AR model in the present invention;

[0064] Figure 4 Diagram showing the relationship between the main equipment subsystems in the present invention;

[0065] Figure 5 Finite element mesh model diagram in the embodiment of the present invention. Detailed implementation manners

[0066] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. In addition, it should be noted that for the convenience of description, only parts related to the present invention are shown in the drawings, rather than all the structures.

[0067] As Figure 1 shown, a method for analyzing the sensitive environmental stress of the A-frame deployment and recovery device of a deep-sea mining ship provided by the present invention includes the following steps:

[0068] Step 1: Determine the main working area and geographical location of the A-frame deployment and recovery device of the deep-sea mining ship according to the different ocean area positions where different types of minerals are mined, and input the specific operation geographical location into the design calculation book.

[0069] Step 2: Determine the main operation task types and main operation task profiles of the A-frame deployment and recovery device of the deep-sea mining ship:

[0070] Among them, the main operation task type is the time statistic of the A-frame deployment and recovery device of the deep-sea mining ship performing different types of tasks in a specific ocean geographical location area.

[0071] The operation profile is the time ratio of the A-frame deployment and recovery device of the deep-sea mining ship performing different types of tasks in a specific ocean geographical location area.

[0072] Among them, the main operation tasks include one or more of dynamic positioning drilling tasks, mooring tasks, cruising tasks, dock maintenance tasks, actual drilling operation tasks, underwater maintenance tasks, dynamic positioning maintenance tasks, and bad weather waiting tasks.

[0073] It is set that the overall operation time of the deep-sea mining ship is 25 years, and the operation time of each main operation task is determined according to the design task book.

[0074] Step 3: Determine the main natural environmental stress and induced environmental stress of the A-frame deployment and recovery device of the deep-sea mining ship:

[0075] Among them, the natural environmental stress is determined according to the specific marine geographical location in Step 1, including one or several of temperature environmental stress, humidity environmental stress, and salt spray environmental stress.

[0076] The induced environmental stress is the stress induced by the natural environmental stress, and is determined with reference to the operation task type and operation task profile in Step 2, including vibration environmental stress and shock environmental stress.

[0077] Step 4: Fault data statistics and fault tree analysis of the A-frame deployment and recovery device of the deep-sea mining ship

[0078] Based on the statistical data of the fault cases of the actual A-frame deployment and recovery device, carry out fault data statistics and fault tree analysis of the A-frame deployment and recovery device of the deep-sea mining ship:

[0079] Step 4.1 Fault data statistics: Statistically analyze the specific component objects where the A-frame deployment and recovery device fails, the specific phenomena of the failure, the specific causes of the failure, and the associated environmental stress of the failure. Through the fault statistical data and fault tree analysis, determine the types of associated environmental stress that cause the failure:

[0080] The types of fault data statistics include mechanical fault, hydraulic fault, and electrical control fault data statistics.

[0081] Table 1 gives an example of fault data statistics:

[0082] Table 1 Example of Fault Data Statistics

[0083]

[0084] Step 4.2 Fault tree analysis: As Figure 2 shown, adopt the fault tree analysis method to conduct fault tree analysis on the mechanical faults, hydraulic faults, and electrical control faults of the main equipment of the A-frame deployment and recovery device, such as the hydraulic power system, A-frame system, winch system for deployment and recovery equipment, and equipment control and operation system.

[0085] Step 5: Fuzzy clustering analysis of fault occurrence frequency and severity, and Apriori association rule analysis of fault-environment stress

[0086] As Figure 3 shown, according to the fault tree of the deployment and recovery system obtained from the analysis in Step 4, establish an FCM-AR model to locate the fault stress. The specific steps are as follows:

[0087] Step 5.1: Fuzzy clustering analysis of fault occurrence frequency and severity

[0088] Obtain the fault information of each device of the A-frame laying and retrieving device, and sort out and extract the occurrence frequency and occurrence severity of the vibration environmental stress dimension, shock environmental stress dimension, temperature environmental stress dimension, humidity environmental stress dimension, and salt spray environmental stress dimension from it.

[0089] Conduct fuzzy clustering analysis on the fault occurrence frequency and occurrence severity to obtain the corresponding membership matrix, and determine the classification of the severity of each fault in the sample.

[0090] Determine the number C of cluster centers, and input the original data of the fault occurrence frequency and fault occurrence severity.

[0091] Among them, the number C of cluster centers is a process of continuously iteratively calculating the membership degree u i,j and the cluster center c j until they reach the optimum, as shown in Equation (1):

[0092]

[0093] For a single sample x i , the sum of its membership degrees for each cluster is 1; where m is the number of membership degrees u i,j , and N is the number of samples x;

[0094] When the iteration satisfies Equation (2), stop the iteration:

[0095]

[0096] Among them, k is the number of iteration steps, max ij is the error threshold, ε is the selected tolerance value, and this process converges to the local minimum or saddle point of the target J m .

[0097] According to the fuzzy clustering algorithm, calculate the membership matrix of the fault degree, and determine the categories corresponding to the occurrence frequency and occurrence severity of each sample.

[0098] Step 5.2: Determine the horizontal and vertical correlation relationships of the A-frame laying and retrieving device, and conduct fault-environmental stress Apriori association rule analysis:

[0099] The horizontal correlation relationship of the A-frame laying and retrieving device is the relationship between subsystems.

[0100] The subsystems of the A-frame laying and retrieving device include a hydraulic power system, an A-gantry system, a winch system for laying and retrieving equipment, and an equipment control and operation system.

[0101] The vertical correlation relationship of the A-frame laying and retrieving device is the influence relationship between the devices in each subsystem.

[0102] The equipment of the A-frame laying and retrieving device includes a telescopic frame, a swing frame, a docking device, a pitch buffer device, a roll buffer device, a hydraulic power station, a hydraulic valve cabinet, a local control cabinet, a remote control device, a hydraulic motor, a normally closed safety brake, a planetary reducer, a cable dispenser, and an optical-electrical slip ring.

[0103] As Figure 4 shown, according to the subsystem classification and equipment classification of the A-frame laying and retrieving device, the horizontal correlation relationship between subsystems of the A-frame laying and retrieving device and the vertical upstream and downstream correlation coupling relationship between mutual equipment are obtained.

[0104] Set parameter a VR to represent the degree of influence between the equipment in each subsystem of the A-frame laying and retrieving device.

[0105] The equipment included in the laying and retrieving subsystem is a ij , representing the degree of influence of equipment i on equipment j, and using a ji to represent the degree of influence of equipment j on equipment i.

[0106] Define the mutual influence degree between the equipment in each subsystem of the A-frame laying and retrieving device through the relationship influence matrix A.

[0107] The initial value of A is as shown in Equation (3):

[0108]

[0109] For the influence degree of equipment i on equipment j of the A-frame laying and retrieving device, it can be defined by the product of the state value X i of equipment i and the degree of influence a ij between equipment i→j, that is, X i ×a ij . Conversely, the influence of equipment j on equipment i is expressed as X j ×a ji .

[0110] Let the matrix representing the influence relationship between each subsystem of the A-frame laying and retrieving device be M. Since there is an unequal situation between a ij and a ji , the matrix M is an asymmetric matrix.

[0111] Apply the fault-environment stress Apriori association rule to analyze the dependency relationship between the information of each dimension of the fault event, and obtain the influence evaluation result F of each environmental stress factor on the fault of each subsystem.

[0112] The matrix M representing the influence relationship between the subsystems of the A-frame laying and retrieving device is obtained by multiplying the influence evaluation result F of each environmental stress factor on the faults of each subsystem by the relationship influence matrix A between the devices in each subsystem of the A-frame laying and retrieving device, i.e., M = F·A.

[0113] Based on the obtained influence relationship between the subsystems of the A-frame laying and retrieving device, combined with the fuzzy clustering analysis of the fault occurrence frequency and severity, as well as the fault-environment stress Apriori correlation analysis, the correlation relationship between the fault of a certain subsystem of the A-frame laying and retrieving device and the sensitive environmental stress is obtained, so as to obtain which sensitive environmental stress has a high correlation with the occurrence of the fault of this subsystem.

[0114] Step 6: Finite element simulation verification of sensitive environmental stress:

[0115] Apply finite element simulation to verify the analysis results obtained in Step 5. The loads of the A-frame laying and retrieving device are respectively modeled and simulated according to 5 types of environmental stresses, and this type of environmental stress is verified through finite element calculations such as static, dynamic, and temperature simulations. When performing finite element analysis, the boundary conditions are constrained according to the specific design drawings, and linear simulation or nonlinear simulation is selected according to the situation.

[0116] In some embodiments of the present invention, in Step 6, the boundary conditions of the finite element analysis are constrained according to the specific design drawings. For the equipment installed on the deck base of the deep-sea mining ship, the boundary effect of the deck is considered, and for the equipment installed on the strong structure of the deep-sea mining ship, the fixed support boundary condition is set.

[0117] In some embodiments of the present invention, in Step 6, the finite element analysis selects linear or nonlinear simulation for verification according to the intensity of 5 types of environmental loads, confirms the main deformation and distribution characteristics, verifies the high-risk areas of the equipment response, and clarifies the main failure modes and characteristics of the equipment. When the environmental load is a load with a small intensity, linear simulation is used for verification to confirm the main deformation and distribution characteristics and verify the high-risk areas of the equipment response; when the environmental load is a load with a large intensity, nonlinear simulation verification is performed to clarify the main failure modes and characteristics of the equipment.

[0118] In some embodiments of the present invention, in Step 6, finite element analysis is performed using a finite element mesh model. The CAD model of the A-frame laying and retrieving device is consistent with the original design drawings, and general CAD modeling software is used for modeling. The number of triangular and deformed meshes in the finite element mesh is controlled below 5%. The finite element analysis is carried out in the order of first analyzing the coarse mesh model and then the fine mesh model. The coarse mesh model is modeled and analyzed according to the mode of beam element + shell element, and the fine mesh model is modeled and analyzed according to the mode of all shell elements.

[0119] In some embodiments of the present invention, in step 6, measured data loads are preferentially selected for loading during simulation analysis. When there is no measured environmental stress load, loading is performed with reference to the specifications given in step 3.

[0120] In some embodiments of the present invention, in step 6, when using the method of non-linear simulation verification, it can be implicit finite element calculation or explicit finite element simulation.

[0121] In some embodiments of the present invention, the process of sensitive environmental stress analysis of the hydraulic power subsystem is as follows:

[0122] Through statistical analysis of historical failure case data, it is found that the frequently occurring failures of the hydraulic power subsystem include failure types such as friction crack failure, oil leakage failure, and excessive vibration sound failure. The transmission shaft of the hydraulic power station is one of the key components of the hydraulic motor equipment in the hydraulic power subsystem and is the main part and component where the above failure types occur. Since the environmental stresses received by the hydraulic power subsystem are relatively complex, including temperature stress, vibration stress, impact stress, etc. Through quality problem report data and literature statistical materials, the occurrence frequency of the hydraulic power subsystem is statistically analyzed, and then the clustering results of the severity of each failure of the hydraulic power subsystem are obtained according to the fuzzy clustering calculation model and method in step 5.1, including the types of failures (such as oil leakage, short circuit, fracture, friction failure, structural plastic deformation, severe heating, etc.) and the severity of failures (four levels: general failure, relatively severe failure, severe failure, and fatal failure). According to step 5.2, fault location analysis is carried out to obtain that the main component where friction failure occurs is the transmission shaft component of the hydraulic power subsystem. Then, through the Apriori association analysis of fault-environmental stress, it is found that the fatal and severe friction fault events are most highly correlated with the action of temperature stress. Therefore, it is judged that the axial temperature acting force is the main sensitive environmental stress of the transmission shaft component of the hydraulic power station.

[0123] The above conclusion is verified through finite element simulation:

[0124] With reference to GBT13853-1992 "Technical Conditions for Marine Hydraulic Pumps and Hydraulic Motors" and the geometric dimensions, material properties, etc. of the transmission shaft component of the hydraulic power station as the initial input conditions, a corresponding finite element simulation model is established. According to GBT13853-1992 "Technical Conditions for Marine Hydraulic Pumps and Hydraulic Motors", the axial temperature load F = 20KN is estimated, and the estimated axial temperature load is applied to the end face of the transmission shaft component of the hydraulic power station. After calculation by the solver, the deformation nephogram of the transmission shaft component of the hydraulic power station is as Figure 5 shown, and it can be judged that the deformation of the axial contact part is relatively large and is prone to friction failure, verifying the analysis results obtained by the analysis method in the technical solution of the present invention.

[0125] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for analyzing sensitive environmental stresses of the A-frame deployment and recovery device of a deep-sea mining ship, characterized in that: The sensitive environmental stress analysis method includes the following steps: Step 1: Determine the main working area and geographical location of the A-frame deployment and recovery device of the deep-sea mining ship according to the different ocean area positions where different mineral types are mined by the deep-sea mining ship; Step 2: Determine the main operation task types and operation profiles of the A-frame deployment and recovery device of the deep-sea mining ship according to the main working area and geographical location determined in Step 1: The main operation task type is the time statistic of the A-frame deployment and recovery device of the deep-sea mining ship performing different types of tasks in a specific ocean geographical location area; The operation profile is the time ratio of the A-frame deployment and recovery device of the deep-sea mining ship performing different types of tasks in a specific ocean geographical location area; Among them, the main operation tasks include one or several of the following: dynamic positioning drilling task, mooring task, cruising task, dock maintenance task, actual drilling operation task, underwater maintenance task, dynamic positioning maintenance task, and bad weather waiting task; Step 3: Determine the main natural environmental stress and induced environmental stress of the A-frame deployment and recovery device of the deep-sea mining ship: Among them, the natural environmental stress is determined according to the specific ocean geographical location in Step 1, and includes one or several of temperature environmental stress, humidity environmental stress, and salt spray environmental stress; The induced environmental stress is the stress induced by the natural environmental stress, and is determined with reference to the operation task type and operation profile in Step 2, and includes vibration environmental stress and impact environmental stress; Step 4: Fault data statistics and fault tree analysis of the A-frame deployment and recovery device of the deep-sea mining ship: Based on the actual fault case statistical data of the A-frame deployment and recovery device, carry out the fault data statistics and fault tree analysis; Step 4.1 Fault data statistics: Statistically analyze the specific equipment object where the A-frame deployment and recovery device fails, the specific phenomenon of the failure, the specific cause of the failure, and the associated environmental stress of the failure; The types of fault data statistics include mechanical fault, hydraulic fault, and electrical control fault data statistics; Step 4.2 Fault tree analysis: Adopt the fault tree analysis method to carry out the fault tree analysis on the mechanical faults, hydraulic faults, and electrical control faults of the main equipment of the A-frame deployment and recovery device respectively; The subsystems of the A-frame deployment and recovery device include a hydraulic power system, an A-frame system, a deployment and recovery equipment winch system, and an equipment control and operation system; Through the fault statistical data and fault tree analysis, determine the types of associated environmental stress that cause the failure; Step 5: Fuzzy clustering analysis of fault occurrence frequency and severity, and Apriori association rule analysis of fault-environment stress: According to the fault tree of the A-frame deployment and recovery system analyzed in Step 4, establish an FCM-AR model to locate the fault stress. The specific steps are as follows: Step 5.1: Fuzzy clustering analysis of fault occurrence frequency and severity: Obtain the fault information of each device of the A-frame laying and retrieving device, and sort out and extract the occurrence frequency and occurrence severity of the vibration environmental stress dimension, shock environmental stress dimension, temperature environmental stress dimension, humidity environmental stress dimension, and salt spray environmental stress dimension from it; Conduct fuzzy clustering analysis on the fault occurrence frequency and occurrence severity to obtain the corresponding membership matrix, and determine the classification of the severity of each fault in the sample; Determine the number C of cluster centers, and input the original data of the fault occurrence frequency and fault occurrence severity; Among them, the number of clustering centers C is a process of continuously iteratively calculating the membership degree u i,j and the cluster center c j until they reach the optimal state, as shown in Equation (1): For a single sample x i , the sum of its membership degrees for each cluster is 1; where m is the number of membership degrees u i,j ; N is the number of samples x When the iteration satisfies Equation (2), stop the iteration: where k is the number of iteration steps, max ij is the error threshold; ε is the selected tolerance value; the process converges to a local minimum or saddle point of the objective J m ; Calculate the membership matrix of the fault degree according to the fuzzy clustering algorithm; Determine the categories corresponding to the occurrence frequency and occurrence severity of each sample; Step 5.2: Determine the horizontal and vertical correlation relationships of the A-frame laying and retrieving device, and conduct fault-environment stress Apriori correlation rule analysis: The horizontal correlation relationship of the A-frame laying and retrieving device is the relationship between subsystems; The subsystems of the A-frame laying and retrieving device include the hydraulic power system, A-frame system, winch system for laying and retrieving equipment, and equipment control and operation system; The vertical correlation relationship of the A-frame laying and retrieving device is the influence relationship between the devices in each subsystem; The devices of the A-frame laying and retrieving device include telescopic frames, swing frames, docking devices, longitudinal roll buffering devices, transverse roll buffering devices, hydraulic power stations, hydraulic valve cabinets, local control cabinets, remote control devices, hydraulic motors, normally closed safety brakes, planetary speed reducers, cable arrangers, and optical fiber slip rings; Obtain the horizontal correlation relationship between the subsystems of the A-frame laying and retrieving device and the longitudinal upstream and downstream correlation coupling relationship between the mutual devices according to the subsystem classification and device classification of the A-frame laying and retrieving device; Set parameter a VR Indicates the degree of interaction between the devices in each subsystem of the A-frame laying and retrieving device; The equipment included in the A-frame laying and recycling subsystem is a ij , indicating the degree of influence of equipment i on equipment j, and using a ji to indicate the degree of influence of equipment j on equipment i; Define the mutual influence degree between the devices in each subsystem of the A-frame laying and retrieving device through the relationship influence matrix A; The initial value of A is as shown in Equation (3): The influence degree of equipment i on equipment j of the A-frame laying and recycling device is defined by the product of the state value X of equipment i i and the action degree a between equipment i→j ij , that is, X i ×a ij . Conversely, the action of equipment j on equipment i is expressed as X j ×a ji ; Let the matrix M representing the influence relationship between the subsystems of the A-frame laying and retrieving device be an asymmetric matrix; Apply the fault-environment stress Apriori correlation rule to analyze the dependency relationship between the information of each dimension of the fault event, and obtain the influence evaluation result F of each environmental stress factor on the faults of each subsystem; The matrix M representing the influence relationship between the subsystems of the A-frame laying and retrieving device is obtained by multiplying the influence evaluation result F of each environmental stress factor on the faults of each subsystem by the relationship influence matrix A between the devices in each subsystem of the A-frame laying and retrieving device, M = F·A; According to the obtained influence relationship between the subsystems of the A-frame laying and retrieving device, combined with the fuzzy clustering analysis of the fault occurrence frequency and severity and the fault-environment stress Apriori correlation analysis, obtain the correlation relationship between the fault of a certain subsystem of the A-frame laying and retrieving device and the sensitive environmental stress, so as to obtain which sensitive environmental stress has a high correlation with the occurrence of the fault of this subsystem; Step 6: Finite element simulation verification of sensitive environmental stress: Apply finite element simulation to verify the analysis results obtained in step 5. The loads of the A-frame deployment and recovery device are respectively modeled and simulated according to 5 types of environmental stresses, and this type of environmental stress is verified through static, dynamic, and thermal element calculation simulations. When performing finite element analysis, linear or nonlinear simulation is adopted, and the boundary conditions of the finite element analysis are constrained according to the specific design drawings.

2. The sensitive environmental stress analysis method of the A-frame deployment and recovery device of a deep-sea mining ship according to claim 1, characterized in that: In step 3, the environmental stresses of temperature, humidity, salt spray, vibration, and shock preferably use the data obtained from actual measurements. If there is no actual measurement data, the specification data is used for analysis.

3. The sensitive environmental stress analysis method for the A-frame deployment and recovery device of a deep-sea mining ship according to claim 1, characterized in that: In step 6, the boundary conditions are specifically determined in the following manner: The equipment installed on the deck base of the deep-sea mining ship is set as the deck boundary condition, and the equipment installed on the strong structure of the deep-sea mining ship is set as the fixed support boundary condition.

4. The sensitive environmental stress analysis method of the A-frame deployment and recovery device of a deep-sea mining ship according to claim 1, characterized in that: In step 6, the finite element analysis selects linear or nonlinear simulation for verification according to the intensity of 5 environmental loads, confirms the main deformation and distribution characteristics, verifies the high-risk areas of the equipment response, and clarifies the main failure modes and characteristics of the equipment.

5. The sensitive environmental stress analysis method for the A-frame deployment and recovery device of a deep-sea mining ship according to claim 1, characterized in that: In step 6, the finite element analysis uses the finite element mesh model for finite element analysis; the CAD model of the A-frame deployment and recovery device is consistent with the original design drawings, and the number of triangular and deformed meshes in the finite element mesh is controlled below 5%. The finite element analysis is carried out in the order of first analyzing the coarse mesh model and then the fine mesh model. The coarse mesh model is modeled and analyzed according to the mode of beam element + shell element, and the fine mesh model is modeled and analyzed according to the mode of all shell elements.

6. The sensitive environmental stress analysis method for the A-frame deployment and recovery device of a deep-sea mining ship according to claim 1, wherein: In step 6, the measured data load is preferably selected for loading during the simulation analysis. When there is no measured environmental stress load, the loading is carried out with reference to the specification given in step 3.

7. A method for analyzing the sensitive environmental stress of the A-frame deployment and recovery device of a deep-sea mining ship according to claim 1, characterized in that: In step 6, the nonlinear simulation verification method is implicit finite element calculation or explicit finite element simulation.

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