Fault prediction and maintenance decision-making method for offshore oil well control equipment

Abstract

The invention belongs to the technical field of fault detection and maintenance decision of offshore oil well control equipment, and particularly relates to a fault prediction and maintenance decision method of the offshore oil well control equipment. According to the fault prediction and maintenance decision-making method, more accurate fault prediction and more reasonable maintenance optimization are realized by combining the operation data, the health assessment model and the system toughness index of the offshore oil well control equipment, and powerful technical support is provided for improving the safety of the well control equipment, reducing the operation and maintenance cost and prolonging the service life. The fault prediction and maintenance decision-making method comprises the following steps: collecting multi-source data of offshore oil well control equipment, and preprocessing the multi-source data; determining a toughness evaluation mechanism of the offshore oil well control equipment; constructing a fault prediction model of the offshore oil well control equipment; determining a condition-based maintenance decision of the offshore oil well control equipment; executing a condition-based maintenance decision of the offshore oil well control equipment; and finely adjusting the fault prediction model parameters of the offshore oil well control equipment.

Description

Technical Field

[0001] This invention belongs to the field of fault detection and maintenance decision-making technology for offshore oil well control equipment, and particularly relates to a fault prediction and maintenance decision-making method for offshore oil well control equipment. Background Technology

[0002] Offshore oil well control equipment, as the core equipment for controlling wellhead pressure and preventing wellhead overflow, is subject to numerous limitations such as complex marine environments, high-pressure and high-temperature operating conditions, and high operation and maintenance costs. Therefore, offshore oil well control equipment is highly susceptible to performance degradation and even sudden failures. Further research revealed that current maintenance decisions for offshore oil well control equipment mostly adopt periodic or reactive maintenance models. These models not only fail to adequately assess the real-time operating status and fault evolution process of the equipment but also struggle to effectively prevent major safety accidents.

[0003] In recent years, with the rapid development of sensor technology and intelligent algorithms, Condition-Based Maintenance (CBM) based on equipment health status has gradually become the mainstream approach. However, CBM technology often overlooks the "resilience" characteristic of the system, resulting in the inability of offshore oil well control equipment to respond quickly and proactively when encountering external shocks, extreme environments, or sudden failures. System resilience, as the ability to absorb shocks, maintain core functions, and achieve rapid recovery, is crucial for the safe and stable operation of well control equipment. Therefore, it is imperative for those skilled in the art to design a fault prediction and maintenance decision-making method based on a resilience-driven mechanism to achieve proactive health management and optimal maintenance decisions for offshore oil well control equipment. Summary of the Invention

[0004] This invention provides a method for fault prediction and maintenance decision-making for offshore oil well control equipment. This method, by combining operational data, health assessment models, and system resilience indicators of the offshore oil well control equipment, achieves more accurate fault prediction and more reasonable maintenance optimization, providing strong technical support for improving the safety of offshore oil well control equipment, reducing its operation and maintenance costs, and extending its service life.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A method for fault prediction and maintenance decision-making of offshore oil well control equipment includes the following steps: Step S1: Collect multi-source data from offshore oil well control equipment and preprocess it; Step S2: Determine the resilience evaluation mechanism for offshore oil well control equipment; Step S3: Construct a fault prediction model for offshore oil well control equipment; Step S4: Determine condition-based maintenance decisions for offshore oil well control equipment; Step S5: Execute condition-based maintenance decisions for offshore oil well control equipment; The multi-source data of the repaired offshore oil well control equipment is updated, error assessment indicators are calculated, and the parameters of the fault prediction model of the offshore oil well control equipment are fine-tuned according to the magnitude of the error.

[0006] Preferably, the process of collecting multi-source data from offshore oil well control equipment and preprocessing it in step S1 specifically includes the following steps: Step S101: Collect operational status data of key components of offshore oil well control equipment and environmental parameters of offshore oil well control equipment to construct a time-series dataset of multi-source data for offshore oil well control equipment. ; Step S102: Perform noise filtering and signal enhancement processing on the raw data in the time series dataset of multi-source data for offshore oil well control equipment; The noise filtering and signal enhancement process satisfies: ; in, For wavelet basis functions, These are the corresponding wavelet coefficients; Step S103: Standardize the data of each channel in the original data; The standardization process satisfies: ; Where, μ i and σ i denoted as the mean and standard deviation of channel i, respectively.

[0007] Preferably, the process of determining the toughness evaluation mechanism of offshore oil well control equipment in step S2 specifically includes the following steps: Step S201: Construct a resilience index system for offshore oil well control equipment; The resilience index system of the offshore oil well control equipment is represented as R={Ra,Rs,Rr}; Ra is the disturbance absorption capacity, which represents the decrease in the function of the offshore oil well control equipment after the disturbance; Rs is the function maintenance capacity, which represents whether the key functions are maintained during the disturbance; Rr is the recovery capacity, which represents the time required for the offshore oil well control equipment to recover to a steady state after the disturbance is removed. Step S202: Determine the degradation path of offshore oil well control equipment; Step S203: Determine the logical relationship between the toughness factors of the offshore oil well control equipment, and construct the toughness causal network diagram G=(V,E) of the offshore oil well control equipment; Wherein, the node set V represents the influence factors of various resilience in offshore oil well control equipment, satisfying: V={v1,v2,…,v k The edge set E represents the directed causal dependency between the various resilience influencing factors.

[0008] Preferably, the process of constructing a fault prediction model for offshore oil well control equipment in step S3 specifically includes the following steps: Step S301: Construct a multi-label sample set of faults in offshore oil well control equipment; use the SMOTE method to form training sample pairs (X(t),y) of faults in offshore oil well control equipment; Step S302: Use a long short-term memory network to extract long-term dependencies in the time series and predict the degradation trend of offshore oil well control equipment; Step S303: Employ a multi-task learning strategy to predict the remaining service life of offshore oil well control equipment.

[0009] Preferably, the process of using a Long Short-Term Memory (LSTM) network to extract long-term dependencies in the time series and predict the degradation trend of offshore oil well control equipment in step S302 is specifically described as follows: Assume the input time series is Xt={xt-τ,…,xt}; Where τ is the length of the time window; Its input gate update process satisfies: ; Where σ() represents the sigmoid activation function, W i and U i These are the weight matrices for the input and the hidden state at the previous time step, respectively, b. i For the bias term, i t To control the degree to which the current input affects the state; Forgotten Gate The decision of how much of the previous moment's memory state to retain satisfies: ; The state update process satisfies: ; Where tanh() represents the hyperbolic tangent activation function, c t It integrates past information and the influence of current input to represent the current cell state; The output gate update process satisfies: ; The hidden state update process satisfies: ; Among them, the calculated network output h t The input is fed into the subsequent regression layer to calculate degradation metrics or predict failure time.

[0010] Preferably, the process of determining the condition-based maintenance decision for offshore oil well control equipment in step S4 specifically includes the following steps: Step S401: Assess the health status and risk of offshore oil well control equipment; Step S402: Construct a comprehensive maintenance value function; The comprehensive maintenance value function satisfies: ; Where E() represents the expectation, Represents the delay fault loss function, Indicates the first Step-by-step maintenance costs, This is the discount factor.

[0011] This invention provides a method for fault prediction and maintenance decision-making of offshore oil well control equipment. The method includes the following steps: Step S1: Collecting multi-source data from the offshore oil well control equipment and preprocessing it; Step S2: Determining the resilience evaluation mechanism of the offshore oil well control equipment; Step S3: Constructing a fault prediction model for the offshore oil well control equipment; Step S4: Determining condition-based maintenance decisions for the offshore oil well control equipment; Step S5: Executing the condition-based maintenance decisions for the offshore oil well control equipment; updating the multi-source data of the repaired offshore oil well control equipment, calculating error assessment indicators, and fine-tuning the parameters of the fault prediction model based on the error magnitude.

[0012] The fault prediction and maintenance decision-making method for offshore oil well control equipment with the above-mentioned steps has at least the following technical advantages compared with existing technologies: 1) The present invention provides a fault prediction and maintenance decision-making method for offshore oil well control equipment. By constructing a systematic method framework of resilience assessment, causal modeling, degradation prediction and maintenance optimization, it effectively solves the technical bottlenecks of existing methods such as insufficient identification of the health status of offshore oil well control equipment under complex working conditions and lagging maintenance strategies.

[0013] 2) The present invention provides a fault prediction and maintenance decision-making method for offshore oil well control equipment. By quantifying the functional maintenance capability of the well control equipment in stages, a multi-dimensional and evolvable resilience evaluation mechanism is constructed. This clarifies the anti-disturbance, recoverability and adaptability characteristics of offshore oil well control equipment under disturbance conditions, and enhances the ability to identify and measure risk factors under complex working conditions. In addition, by establishing a resilience causal graph model, the structured logical relationship between resilience influencing factors is clarified, and the performance evolution path under the coupling effect of multiple factors is accurately reflected with the system function maintenance probability as the objective.

[0014] 3) This invention provides a fault prediction and maintenance decision-making method for offshore oil well control equipment. By comprehensively analyzing the fault evolution process and health index-driven prediction of offshore oil well control equipment, it achieves real-time tracking of the life degradation trajectory of key components of offshore oil well control equipment. Furthermore, by constructing an optimization function that includes fault delay losses and maintenance costs, it provides effective guidance for selecting maintenance timing. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the following drawings: Figure 1 This is a flowchart illustrating a fault prediction and maintenance decision-making method for offshore oil well control equipment provided by the present invention. Detailed Implementation

[0016] This invention provides a method for fault prediction and maintenance decision-making for offshore oil well control equipment. This method, by combining operational data, health assessment models, and system resilience indicators of the offshore oil well control equipment, achieves more accurate fault prediction and more reasonable maintenance optimization, providing strong technical support for improving the safety of offshore oil well control equipment, reducing its operation and maintenance costs, and extending its service life.

[0017] like Figure 1 As shown, the present invention provides a method for fault prediction and maintenance decision-making of offshore oil well control equipment, comprising the following steps: Step S1: Collect multi-source data from offshore oil well control equipment and preprocess it.

[0018] In a preferred embodiment of the present invention, the process of collecting multi-source data from offshore oil well control equipment and preprocessing it in step S1 specifically includes the following steps: Step S101: Collect operational status data of key components of offshore oil well control equipment and environmental parameters of offshore oil well control equipment to construct a time-series dataset of multi-source data for offshore oil well control equipment. D ={ x t 1 , x t 2 ,…, x t n}

[0019] The operational status data collected for key components of offshore oil well control equipment includes at least pressure, temperature, vibration acceleration, current, voltage, and control command frequency. Environmental parameters for offshore oil well control equipment include at least water depth and external impact intensity, which will not be listed here.

[0020] Step S102: Perform noise filtering and signal enhancement processing on the raw data in the time series dataset of multi-source data of offshore oil well control equipment.

[0021] It is worth noting that the purpose of noise filtering and signal enhancement processing is to remove non-stationary interference components from high-frequency noise and abrupt changes in the original data. Specifically, wavelet transform can be used for the aforementioned multi-scale decomposition. The noise filtering and signal enhancement processing process satisfies the following: ; in, For wavelet basis functions, These are the corresponding wavelet coefficients.

[0022] Step S103: Standardize the data from each channel in the original data. The standardization process satisfies the following: ; Where, μ i and σ i denoted as the mean and standard deviation of channel i, respectively.

[0023] Step S2: Determine the toughness evaluation mechanism for offshore oil well control equipment.

[0024] Based on the completion of step S1, step S2 is further implemented. It is worth noting that, as a preferred embodiment of the present invention, the process of determining the toughness evaluation mechanism of offshore oil well control equipment in step S2 specifically includes the following steps: Step S201: Construct a resilience index system for offshore oil well control equipment.

[0025] This resilience index system refers to the comprehensive reflection of the functional maintenance and recovery capabilities of offshore oil well control equipment under disturbance conditions. The constructed resilience index system for offshore oil well control equipment is represented as R={Ra,Rs,Rr}; Ra represents the disturbance absorption capacity, indicating the degree of functional decline of the offshore oil well control equipment after a disturbance; Rs represents the functional maintenance capacity, indicating whether key functions remain operational during the disturbance; and Rr represents the recovery capacity, indicating the time required for the offshore oil well control equipment to recover to a steady state after the disturbance is resolved.

[0026] Step S202: Determine the degradation path of offshore oil well control equipment.

[0027] Based on the completion of step S201, further implement step S202. In this step, a continuous-time Markov chain is used to model the state sequence of the offshore oil well control equipment, and the state sequence satisfies the following equation: Where p(t) is the state probability distribution, Q is the transition rate matrix, and the state set includes normal, mildly degraded, severely degraded, and failed states.

[0028] Step S203: Determine the logical relationships among the toughness factors of offshore oil well control equipment, and construct a toughness causal network graph G=(V,E) for the offshore oil well control equipment; where the node set V represents the influencing factors of each toughness in the offshore oil well control equipment, satisfying: V={v1,v2,…,v k The edge set E represents the directed causal dependency between the various resilience influencing factors.

[0029] It is worth noting that, for the convenience of those skilled in the art, examples of various toughness-influencing factors in offshore oil well control equipment are provided here, such as the vibration amplitude of key components, temperature rise level, oil contamination degree, and control signal fluctuation amplitude. The edge set E specifically represents the directed causal dependence between each toughness-influencing factor. For example, abnormal vibration may cause local temperature rise, which may in turn accelerate lubricant deterioration, thereby leading to component degradation. This causal chain can be represented sequentially by directed edges.

[0030] Based on this, we further define the system function level F(t) to represent the structure function of the system's ability to maintain function under the aforementioned multiple resilience factor states. F(t) satisfies: .

[0031] Step S3: Construct a fault prediction model for offshore oil well control equipment.

[0032] Based on completing step S2, step S3 is further implemented. As a preferred embodiment of the present invention, the process of constructing a fault prediction model for offshore oil well control equipment in step S3 specifically includes the following steps: Step S301: Construct a multi-label sample set of faults in offshore oil well control equipment; use the SMOTE method to form training sample pairs (X(t),y) of faults in offshore oil well control equipment.

[0033] Step S301 is specifically used to complete the construction and sample expansion of fault labels for offshore oil well control equipment. The process of constructing a multi-label sample set of faults for offshore oil well control equipment can be accomplished by classifying and labeling various fault modes using historical operation records and failure logs.

[0034] Step S302: Use a long short-term memory network to extract long-term dependencies in the time series and predict the degradation trend of offshore oil well control equipment.

[0035] Based on the completion of step S301, step S302 is further implemented. The purpose of implementing step S302 is to complete the time series modeling and degradation trend extraction of offshore oil well control equipment.

[0036] Specifically, as a preferred implementation, a Long Short-Term Memory (LSTM) network is used to extract long-term dependencies in the time series and predict system degradation trends. First, assume the input time series is Xt={xt-τ,…,xt}; Where τ is the length of the time window.

[0037] Its input gate update process satisfies: ; Where σ() represents the sigmoid activation function, W i and U i These are the weight matrices for the input and the hidden state at the previous time step, respectively, b. i For the bias term, i t To control the degree to which the current input affects the state.

[0038] Forgotten Gate The decision of how much of the previous moment's memory state to retain satisfies: .

[0039] The state update process satisfies: ; Where tanh() represents the hyperbolic tangent activation function, c t It represents the current cell state, integrating past information and the impact of current inputs.

[0040] The output gate update process satisfies: .

[0041] The hidden state update process satisfies: ; The calculated network output ht is input into the subsequent regression layer to calculate degradation indicators or predict failure time.

[0042] Step S303: Employ a multi-task learning strategy to predict the remaining service life of offshore oil well control equipment.

[0043] Based on step S302, step S303 is further implemented. Specifically, based on the Long Short-Term Memory network, a multi-task learning strategy is adopted to output the probability distribution of failure modes, thereby predicting the remaining service life of the offshore oil well control equipment.

[0044] The loss function is defined as a weighted combination: ; In the above loss function calculation formula, L RUL L represents the mean squared error loss. class λ1 and λ2 represent the cross-entropy loss, and λ1 and λ2 are weighting factors.

[0045] Step S4: Determine condition-based maintenance decisions for offshore oil well control equipment.

[0046] Based on completing step S3, step S4 is further implemented. In a preferred embodiment of the present invention, step S4, which determines the condition-based maintenance decision for offshore oil well control equipment, specifically includes the following steps: Step S401: Assess the health status and risk of offshore oil well control equipment.

[0047] Specifically, the evaluation process further incorporates the health index HI∈[0,1] of offshore oil well control equipment, and combines it with the predicted probability distribution of multiple types of faults to evaluate various risk values ​​R in offshore oil well control equipment. j =p j ·C j Among them, C j Costs associated with the consequences of failures.

[0048] Step S402: Construct the comprehensive maintenance value function. The comprehensive maintenance value function satisfies: E() represents expectation. Represents the delay fault loss function, Indicates the first Step-by-step maintenance costs, This is the discount factor.

[0049] The health status and risk assessment results mentioned above will serve as the input basis for subsequent maintenance strategy optimization. For example, the health status assessment results can be used to determine whether the current status is within an acceptable range, while the risk assessment results can be used for prioritization and identification of high-risk patterns.

[0050] Step S5: Execute condition-based maintenance decisions for offshore oil well control equipment.

[0051] The multi-source data of the repaired offshore oil well control equipment is updated, error assessment indicators are calculated, and the parameters of the fault prediction model of the offshore oil well control equipment are fine-tuned according to the magnitude of the error.

[0052] Based on completing step S4, further implement step S5. First, execute the condition-based maintenance decision for the offshore oil well control equipment determined in step S4, and record the corresponding maintenance decision information: including component replacement, lubrication, cleaning or structural repair, as well as record the actual maintenance items, component aging status and fault type, etc.

[0053] Then, the multi-source data of the repaired offshore oil well control equipment is updated, and an error assessment index is calculated. The error assessment index can be expressed as ε(t) = HI(t) - HI actmal (t). The HI actmal (t) represents the health index of the offshore oil well control equipment after maintenance. Finally, by fine-tuning the parameters of the fault prediction model using backpropagation based on the error magnitude, a self-learning closed-loop iteration can be achieved.

[0054] This invention provides a method for fault prediction and maintenance decision-making of offshore oil well control equipment. The method includes the following steps: Step S1: Collecting multi-source data from the offshore oil well control equipment and preprocessing it; Step S2: Determining the resilience evaluation mechanism of the offshore oil well control equipment; Step S3: Constructing a fault prediction model for the offshore oil well control equipment; Step S4: Determining condition-based maintenance decisions for the offshore oil well control equipment; Step S5: Executing the condition-based maintenance decisions for the offshore oil well control equipment; updating the multi-source data of the repaired offshore oil well control equipment, calculating error assessment indicators, and fine-tuning the parameters of the fault prediction model based on the error magnitude.

[0055] The fault prediction and maintenance decision-making method for offshore oil well control equipment with the above-mentioned steps has at least the following technical advantages compared with existing technologies: 1) The present invention provides a fault prediction and maintenance decision-making method for offshore oil well control equipment. By constructing a systematic method framework of resilience assessment, causal modeling, degradation prediction and maintenance optimization, it effectively solves the technical bottlenecks of existing methods such as insufficient identification of the health status of offshore oil well control equipment under complex working conditions and lagging maintenance strategies.

[0056] 2) The present invention provides a fault prediction and maintenance decision-making method for offshore oil well control equipment. By quantifying the functional maintenance capability of the well control equipment in stages, a multi-dimensional and evolvable resilience evaluation mechanism is constructed. This clarifies the anti-disturbance, recoverability and adaptability characteristics of offshore oil well control equipment under disturbance conditions, and enhances the ability to identify and measure risk factors under complex working conditions. In addition, by establishing a resilience causal graph model, the structured logical relationship between resilience influencing factors is clarified, and the performance evolution path under the coupling effect of multiple factors is accurately reflected with the system function maintenance probability as the objective.

[0057] 3) This invention provides a fault prediction and maintenance decision-making method for offshore oil well control equipment. By comprehensively analyzing the fault evolution process and health index-driven prediction of offshore oil well control equipment, it achieves real-time tracking of the life degradation trajectory of key components of offshore oil well control equipment. Furthermore, by constructing an optimization function that includes fault delay losses and maintenance costs, it provides effective guidance for selecting maintenance timing.

[0058] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for fault prediction and maintenance decision-making of offshore oil well control equipment, characterized in that, The steps include the following: Step S1: Collect multi-source data from offshore oil well control equipment and preprocess it; Step S2: Determine the resilience evaluation mechanism for offshore oil well control equipment; Step S3: Construct a fault prediction model for offshore oil well control equipment; Step S4: Determine the condition-based maintenance decision for offshore oil well control equipment; Step S5: Execute condition-based maintenance decisions for offshore oil well control equipment; update the multi-source data of the offshore oil well control equipment after maintenance, calculate error assessment indicators, and fine-tune the fault prediction model parameters of the offshore oil well control equipment according to the error magnitude. The process of collecting multi-source data from offshore oil well control equipment and preprocessing it in step S1 specifically includes the following steps: Step S101: Collect operational status data of key components of offshore oil well control equipment and environmental parameters of offshore oil well control equipment to construct a time-series dataset of multi-source data for offshore oil well control equipment. D ={ x t 1 , x t 2 ,…, x t n }; Step S102: Perform noise filtering and signal enhancement processing on the raw data in the time series dataset of multi-source data of offshore oil well control equipment; The noise filtering and signal enhancement process satisfies: ; in, For wavelet basis functions, These are the corresponding wavelet coefficients; Step S103: Standardize the data of each channel in the original data; The standardization process satisfies: ; Where, μ i and σ i denoted as the mean and standard deviation of channel i, respectively.

2. The method for fault prediction and maintenance decision-making of offshore oil well control equipment according to claim 1, characterized in that, The process of determining the toughness evaluation mechanism of offshore oil well control equipment in step S2 specifically includes the following steps: Step S201: Construct a resilience index system for offshore oil well control equipment; The resilience index system of the offshore oil well control equipment is represented as R={Ra,Rs,Rr}; Ra is the disturbance absorption capacity, which represents the decrease in the function of the offshore oil well control equipment after the disturbance; Rs is the function maintenance capacity, which represents whether the key functions are maintained during the disturbance; Rr is the recovery capacity, which represents the time required for the offshore oil well control equipment to recover to a steady state after the disturbance is removed. Step S202: Determine the degradation path of offshore oil well control equipment; Step S203: Determine the logical relationship between the toughness factors of the offshore oil well control equipment, and construct the toughness causal network diagram G=(V,E) of the offshore oil well control equipment; Wherein, the node set V represents the influence factors of various resilience in offshore oil well control equipment, satisfying: V={v1,v2,…,v k The edge set E represents the directed causal dependency between the various resilience influencing factors.

3. The method for fault prediction and maintenance decision-making of offshore oil well control equipment according to claim 1, characterized in that, The process of constructing a fault prediction model for offshore oil well control equipment in step S3 specifically includes the following steps: Step S301: Construct a multi-label sample set of faults in offshore oil well control equipment; use the SMOTE method to form training sample pairs (X(t),y) of faults in offshore oil well control equipment; Step S302: Use a long short-term memory network to extract long-term dependencies in the time series and predict the degradation trend of offshore oil well control equipment; Step S303: Employ a multi-task learning strategy to predict the remaining service life of offshore oil well control equipment.

4. The method for fault prediction and maintenance decision-making of offshore oil well control equipment according to claim 3, characterized in that, The process of using a Long Short-Term Memory (LSTM) network to extract long-term dependencies in the time series and predict the degradation trend of offshore oil well control equipment in step S302 is specifically described as follows: Assume the input time series is Xt={xt-τ,…,xt}; Where τ is the length of the time window; Its input gate update process satisfies: ; Where σ() represents the sigmoid activation function, W i and U i These are the weight matrices for the input and the hidden state at the previous time step, respectively, b. i For the bias term, i t To control the degree to which the current input affects the state; Forgotten Gate The decision of how much of the previous moment's memory state to retain satisfies: ; The state update process satisfies: ; Where tanh() represents the hyperbolic tangent activation function, c t It integrates past information and the influence of current input to represent the current cell state; The output gate update process satisfies: ; The hidden state update process satisfies: ; Among them, the calculated network output h t The input is fed into the subsequent regression layer to calculate degradation metrics or predict failure time.

5. The method for fault prediction and maintenance decision-making of offshore oil well control equipment according to claim 1, characterized in that, The process of determining the condition-based maintenance decision for offshore oil well control equipment in step S4 specifically includes the following steps: Step S401: Assess the health status and risk of offshore oil well control equipment; Step S402: Construct a comprehensive maintenance value function; The comprehensive maintenance value function satisfies: ; Where E() represents the expectation, Represents the delay fault loss function, Indicates the first Step-by-step maintenance costs, This is the discount factor.

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