Christmas tree fault propagation path development trajectory prediction method based on deep learning

Abstract

The invention belongs to the field of ocean engineering, and discloses a Christmas tree fault propagation path development trajectory prediction method based on deep learning. The method comprises the following steps: determining a key fault mode and a dependency relationship of an underwater Christmas tree, cleaning and processing data, constructing a fault propagation path development trajectory prediction model based on deep learning, verifying, evaluating and optimizing the prediction model, and visualizing a fault propagation path development trajectory prediction result. According to the novel method for predicting the development trajectory of the fault propagation path of the key component of the underwater Christmas tree, the state change of multiple components, the development trend of the fault propagation path and probability and the like can be comprehensively predicted in real time.

Description

Technical Field

[0001] The present invention belongs to the field of marine engineering and relates to a method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning. Background Art

[0002] Subsea Christmas trees are key facilities in underwater production systems and are widely used in offshore oil production. Deepwater Christmas tree accidents are a global problem that threatens the safety of oil and gas production. As the complexity of Christmas trees continues to increase, there is a fault dependency between components in the system, that is, the failure of one unit can easily lead to the failure of other units, causing the consequences of the accident to escalate exponentially. Fault prediction can predict and warn in advance, allowing maintenance personnel to solve the fault in a targeted manner, which can effectively avoid the escalation of accidents.

[0003] At present, three main methods are used for fault prediction: model-driven methods, data-driven methods, and hybrid approaches. In practical applications, these methods still face many challenges, especially when sample data is limited or facing high-dimensional input space, such as scenarios where time series data is constantly accumulated. Fault prediction based on time series analysis aims to build a model that can reflect the evolution of data over time and make predictions for the future based on it. In the prior art, a remaining useful life (RUL) prediction method based on hybrid DBN-KF has been developed. In the dynamic expansion of DBN, the changes in the valve parameters of the underwater oil production tree are updated by specifying the state transition model. This research has made some progress in the field of fault prediction based on time series analysis, but when considering the multi-dimensional superposition of sample data caused by fault dependence, this method is still inadequate.

[0004] With the increasing complexity of systems and the development of big data technology, domestic and foreign researchers have proposed many methods to improve fault prediction models to solve the problem of fault propagation time series prediction with integrated fault dependency, such as the deep integration of deep learning models and neural network models, including long short-term memory neural network (LSTM), feedforward neural network (FNN), graph neural network (GNN), etc. Such models are mostly used in fault propagation scenarios in fields such as road networks or power grids, and can analyze the development trajectory of fault propagation paths in time series. However, given the high similarity of the structure and function of system nodes, the models often significantly simplify the node characteristics and fault dependencies. This simplified method makes these models usually lack universality and generalization capabilities, so they are only applicable to specific situations or research objects. When dealing with underwater production systems with complex fault dependencies such as underwater Christmas trees, their applicability is significantly limited and needs further optimization and improvement. For underwater production systems with fault dependencies, most researchers currently focus on the impact of fault dependencies and cascading failures on the system RUL, and predict the health index and RUL of underwater systems affected by fault dependencies based on BN. In view of the interaction of degradation and parameter uncertainty of subsea tree components, the "interaction coefficient" is introduced, and a multi-stage RUL prediction model based on DBN is proposed. In view of the dependence of internal degradation and external shock faced by the hydraulic control system of subsea tree when operating in complex environments, the concept of "dependency factor" is introduced, and a system RUL prediction model that comprehensively considers the dependence of degradation shock is constructed. In view of the cascading failure problem of multi-stage subsea transmission system, a cascading failure modeling and RUL prediction method based on location and functional importance is proposed. Research on Fault Diagnosis and Preventive Maintenance Methods of Key Equipment of Electro-hydraulic Composite Subsea Tree System published by Yuan Xiaobing. 2020, China University of Petroleum (East China). Considering the failure modes of multiple modules of the subsea tree system and the influence of their interdependence, the fault prediction of three different modules with interdependence in the subsea tree system is carried out based on BNs, and the system RUL is predicted according to the performance degradation index. In addition to the limitation of simplifying the fault dependency, the above studies are also limited to a local perspective, only predicting faults for a single node affected by the fault dependency, or evaluating the RUL of the system affected by the fault dependency, and failing to explore the propagation dynamics and evolution of the fault from a global perspective to predict faults. Specifically, they rarely comprehensively examine the evolution prediction of the cascading fault propagation process over time, including the prediction of the initial fault development trajectory, the path analysis prediction of fault propagation, the prediction of the spread of the fault in the potentially affected units, and the quantitative analysis prediction of the propagation probability and other key aspects.

[0005] The propagation of cascading faults leads to dynamic changes in the status of the entire system and key components, which puts forward comprehensive and real-time requirements for fault prediction models. Most of the current research is still based on the prediction of single-point faults of components considering fault dependence, and the research on the development trajectory of fault propagation paths and dynamic prediction methods under the dominance of time series is still insufficient. Considering the prediction of fault propagation trajectories in the spatial dimension and the prediction of fault propagation trajectories in the time dimension that change with the time series, how to start from a global perspective and follow the inherent laws of the dynamic evolution of multi-level fault propagation scenarios over time to achieve accurate prediction of the development trajectory of underwater oil tree fault propagation paths is an important issue that needs to be solved urgently. Summary of the invention

[0006] In order to overcome the defects of the prior art, the present invention provides a method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning, comprising the steps of determining key failure modes and dependencies of underwater Christmas trees, data cleaning and processing, constructing a fault propagation path development trajectory prediction model based on deep learning, verifying, evaluating and optimizing the prediction model, and visualizing the prediction results of the fault propagation path development trajectory.

[0007] The technical solution of the present invention:

[0008] A method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning, the steps are as follows:

[0009] S1. Identify critical failure modes and dependencies of subsea trees

[0010] Determine the operating indicators of the subsea tree, including pressure, temperature, flow, determine the operating range of key components of the subsea tree, and define the boundary conditions for the normal operation of the subsea tree;

[0011] Through comprehensive analysis of the subsea Christmas tree's operating history data, maintenance records, and expert experience, we identify failure modes, including leakage, blockage, corrosion, and wear, and describe each failure mode, including its manifestation, possible causes, and impact range;

[0012] Use fault tree analysis and event tree analysis to analyze the dependencies between various fault modes, build a fault propagation network, and clarify the connection relationship and propagation path between various fault nodes;

[0013] According to the frequency of occurrence, impact and maintenance difficulty of faults, the key fault modes and main fault dependencies that have the greatest impact on the operation safety and production efficiency of subsea Christmas trees are screened out;

[0014] Based on the identified key failure modes and main failure dependencies, a failure dependency model of the system is constructed using a Bayesian network, and the dependencies between key components are clarified in a matrix form;

[0015] S2. Data Collection and Processing

[0016] Collect historical fault data of underwater oil trees, including the failure time, fault type, fault impact, fault propagation scenario of each component, as well as the operation log and maintenance record of underwater oil trees, and collect the operation data of underwater oil trees from various sensors of underwater oil trees, including pressure, temperature, flow, and vibration parameters; process the collected historical fault data and operation data, first clean the collected data to remove duplicates, missing values, and outliers, and then normalize and standardize them to improve the consistency and comparability of the data; use the processed data to simulate the fault propagation process under different environmental conditions through the Monte Carlo method to generate a large amount of simulated virtual fault sample data; fuse the processed historical fault data and simulated virtual sample data to generate fault data sets for different cascading fault scenarios under different operating conditions; use the time series analysis ARIMA model to model the fault data set and obtain linear prediction values ; The linear prediction value As additional input features, they are embedded in the input layer of the deep learning prediction model to be built together with the fault dataset;

[0017] S3. Construct a fault propagation path development trajectory prediction model based on deep learning

[0018] According to the complexity of the fault propagation path of the underwater oil tree and the data characteristics, the deep learning prediction model is selected as the convolutional neural network model and the long short-term memory network model, and a deep learning-based fault propagation path development trajectory prediction model of the time series underwater oil tree is constructed. This fault propagation path development trajectory prediction model uses the convolutional neural network model to extract the spatial features of the fault data set, see formula (1), and the extracted spatial features are input into the long short-term memory network model as a time series to extract the final time dynamic spatial features, see formula (2); at the same time, the linear prediction value in step S2 is used as an additional input feature and compared with the fault data The set is embedded into the input layer of the deep learning prediction model to enhance the interpretation ability and prediction performance of the deep learning prediction model; the output layer of the deep learning prediction model is the prediction target, including the fault type, fault propagation probability, fault propagation direction and component state change; adjustable input parameters are designed, including the dependency strength between nodes, environmental factors, and fault type, to adapt to the fault propagation characteristics under different working conditions; the fault data set preprocessed in step S2 is divided into a training set, a validation set and a test set, and the deep learning prediction model is trained and cross-validated; the mean square error, root mean square error, mean absolute error and R² statistic indicators are used to evaluate the prediction performance of the deep learning prediction model, and the prediction accuracy of the deep learning prediction model is further improved through parameter tuning;

[0019] (1)

[0020] (2)

[0021] S4. Prediction model validation, evaluation and optimization

[0022] The constructed deep learning prediction model is used to predict the fault propagation path of the underwater oil tree. The prediction results include fault type, fault propagation probability, fault propagation direction and component state change. The prediction ability of the deep learning prediction model in predicting the above results is evaluated through prediction accuracy, prediction efficiency and robustness indicators to compare and verify the performance of the deep learning prediction model under different acceleration conditions and extreme conditions. The deep learning prediction model is retrained and optimized according to the verification results, including adjusting the model structure, optimizing parameter settings, and improving data preprocessing methods to improve the prediction accuracy and generalization ability of the deep learning prediction model. The prediction results of the optimized deep learning prediction model are compared and verified with the actual situation again to evaluate the prediction performance of the model, and the model is corrected and optimized according to the verification results.

[0023] S5. Visualization of prediction results of fault propagation path development trajectory

[0024] Import the prediction results into the NetworkX library to build a complex network graph model of deepwater oil production tree fault propagation based on nodes and edges; create component nodes and status nodes, and create edges according to the predicted fault propagation path to indicate the direction of fault propagation from one unit to another; dynamically update the node color or size according to the prediction results of the deep learning prediction model to indicate the degree of unit degradation, equipment availability or system health status; dynamically update the edge width according to the fault propagation path predicted by the deep learning prediction model to indicate the severity of fault propagation; create a dynamically updated chart to show the changes in the status of each component over time, and realize a visual presentation that can reflect and predict the propagation of deepwater oil production tree faults in real time; develop user interaction functions to allow selection of time periods and units, view detailed information, collect user feedback, and continuously optimize models and visualization effects.

[0025] The beneficial effects of the present invention are as follows: In view of the problem that it is difficult to comprehensively and in real time track the changes in the system state that the integrated fault depends on in single-point fault prediction, a new method for predicting the development trajectory of the fault propagation path of the key components of the underwater oil production tree is proposed, which can comprehensively and in real time predict the state changes of multiple components and the development trend of the fault propagation path and probability. The time series analysis method is used to capture the changing trend of the system operation state and the dynamic evolution process of potential faults, and a fault propagation path development trajectory prediction model is established based on deep learning. The model can be used to provide comprehensive and real-time fault prediction results and provide fault propagation path prediction within the next 24 hours. By importing the prediction model into the NetworkX library to create a complex network diagram of the system, and studying the real-time presentation method of the prediction results such as the direction and probability of the fault propagation path on the network diagram, user visualization and interactive functions can be developed. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 The basic flow chart of the trajectory prediction method for underwater oil tree fault propagation path development based on deep learning.

[0027] Figure 2 This is an illustration of the fault dependency matrix.

[0028] Figure 3 Flow chart of data collection and processing.

[0029] Figure 4 Construct a flow chart for the deep learning based fault propagation path development trajectory prediction model. DETAILED DESCRIPTION

[0030] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings and technical solutions.

[0031] The fault propagation path of the underwater Christmas tree of an offshore oil field is predicted so that maintenance measures can be taken in time. The underwater Christmas tree of the oil field is mainly composed of wellhead connectors, tubing hangers, plugs, tree caps, tree bodies, valves and various passages.

[0032] S1: Construction of subsea tree failure dependency model

[0033] Analyze the working principle of the underwater oil tree: crude oil enters the tubing hanger from the wellhead connector, reaches the valve through the passage inside the tree body, and is then transported to the platform through the passage; identify the system structure: key components including the wellhead connector, tubing hanger, plug, tree cap, tree body, valves and various passages.

[0034] Identify key components and their functions: Wellhead connector connects the wellhead and the tubing hanger; Tubing hanger: supports the tubing and controls fluid flow; Plug: used to temporarily close the passage; Tree cap: protects the top of the tree; Tree: contains various passages and valves; Valve: controls fluid flow; Passage: pipes connecting various components.

[0035] Identify dependencies between key components: structural dependencies such as tubing hangers relying on wellhead connectors for support; functional dependencies such as valves relying on tubing hangers for fluid control.

[0036] Define operating indicators: pressure, temperature, flow, and determine normal operating boundary conditions, such as pressure not exceeding 20 MPa and temperature not exceeding 80°C.

[0037] Extract key failure modes and influencing factors: Based on the OREDA database and historical failure data, key failure modes such as pipeline leakage, filter blockage, crack propagation, and seal failure are identified; through historical data and expert experience, leakage and corrosion are identified as key failure modes. Leakage is mainly manifested as fluid seeping out of the pipe or valve, which may cause pressure drop and environmental pollution; corrosion is mainly manifested as the gradual destruction of the material surface, which may cause pipe rupture or valve failure;

[0038] Use FMEA and FTA methods to build fault trees and identify common cause failures and cascading failures. For example, a leakage failure may cause a pressure drop, which in turn leads to increased corrosion of other components; at the same time, a corrosion failure may also cause the pipe wall to become thinner, increasing the risk of leakage.

[0039] Based on the failure frequency, impact degree and maintenance difficulty, we screened out leakage and corrosion as the key failure modes that have the greatest impact on the operational safety and production efficiency of the subsea oil production tree. According to the key failure modes and the main failure dependencies, we constructed a Bayesian network model to clarify the failure dependency matrix between components.

[0040] S2: Data collection and processing

[0041] Collect fault records from the past three years, including fault time, type, impact, and propagation scenarios. For example, a leakage fault occurred at a valve, causing a 20 psi pressure drop and causing corrosion in adjacent pipes. Collect real-time operating data from sensors, including pressure, temperature, flow, and vibration parameters. For example, at a certain moment, the pressure was 300 psi, the temperature was 100°C, and the flow was 3000 m³ / h.

[0042] Clean the collected data, remove duplications, missing values ​​and outliers, and normalize and standardize the data to improve the consistency and comparability of the data;

[0043] Monte Carlo method is used to simulate the fault propagation process under different environmental conditions and generate virtual fault samples. For example, in the simulation, different pressure, temperature and flow conditions are set to observe the fault propagation path and impact.

[0044] The historical fault data, simulation data and linear prediction values ​​(obtained using the ARIMA model) are combined to form a fault data set, which contains rich fault information and operation data;

[0045] S3: Construction of a prediction model for fault propagation path development trajectory based on deep learning

[0046] Model selection: Convolutional neural network (CNN) and long short-term memory network (LSTM) are selected as the basis of deep learning models. CNN is used to extract spatial features of fault data, and LSTM is used to extract temporal dynamic features.

[0047] Model construction: Build a CNN-LSTM deep learning model, use the output of CNN as the input of LSTM, and use the linear prediction value of the ARIMA model as an additional input feature, and embed it in the input layer of the deep learning model together with the fault dataset;

[0048] Model training: Use the preprocessed data set to train the model and perform cross-validation. During the training process, continuously adjust the model parameters and structure to improve the model's predictive performance;

[0049] Performance evaluation: The prediction performance of the model was evaluated using indicators such as mean square error, root mean square error, and mean absolute error. The results showed that the model had high prediction accuracy and could accurately predict future fault types, propagation probabilities, directions, and component status changes.

[0050] S4: Prediction model validation, evaluation and optimization

[0051] The model was predicted using the test set, and the prediction results such as fault type, propagation probability, direction, and component status change were obtained. The model predicted that a valve might have a leakage fault in the next month, and gave the fault propagation path and impact range.

[0052] The prediction ability of the model was evaluated through prediction accuracy, efficiency and robustness indicators. The results showed that the model performed well in prediction accuracy, efficiency and robustness and could meet the needs of practical applications.

[0053] Based on the verification results, we optimized the model, including adjusting the model structure, optimizing parameter settings and improving data preprocessing methods. The optimized model has further improved its prediction performance.

[0054] S5: Visualization of prediction results of fault propagation path development trajectory

[0055] A complex network graph model of fault propagation was constructed using the NetworkX library, showing the fault propagation path and the dependencies between components. In the network graph, we can see how the leakage fault propagates from the valve to the adjacent pipes and sensors.

[0056] The node color, size, and edge width are dynamically updated according to the prediction results to indicate the degradation degree of the unit, the equipment availability status, or the system health status. When the predicted failure probability of a valve increases, its node color can be changed to red in the network diagram, and its node size can be increased to indicate its degradation degree.

[0057] Create dynamically updated charts that show how the status of components changes over time. For example, you can see the pressure value of a pressure sensor changing over time and the sudden drop in pressure when a fault occurs.

[0058] Develop a user interaction interface that allows users to select time periods and units to view detailed information. For example, users can choose to view fault records within the past month, or choose to view the fault prediction results of a specific component. At the same time, collect user feedback and continuously optimize the model and visualization effects.

[0059] Results presentation and analysis

[0060] 1. Prediction result display: Through visual charts, the fault propagation path of the underwater oil tree in different time periods is displayed; the fault type and fault propagation probability of each component, as well as the fault propagation direction and component status change are predicted.

[0061] 2. Model performance evaluation: It has been verified that the prediction accuracy of the deep learning model has reached more than 90%, with high prediction efficiency and good robustness. Under different acceleration conditions and extreme conditions, the model performs stably and can accurately predict the fault type and fault propagation path.

[0062] 3. Optimization effect: After the optimization, the prediction performance of the deep learning model is further improved, and it can better adapt to the fault propagation characteristics under different working conditions.

[0063] 4. Practical application effect: Through this prediction method, the oil field can timely detect and deal with potential cascading failures, improve production efficiency and safety, reduce downtime and maintenance costs caused by failures, and bring significant economic benefits to the oil field.

[0064] The deep learning-based prediction method for the development trajectory of the Christmas tree fault propagation path realizes accurate prediction of the underwater Christmas tree fault propagation path by constructing a fault dependency model and a fault dependency evolution model and combining deep learning technology. The method has high prediction accuracy and robustness, and can provide strong support for the safe production and efficient operation of oil fields.

Claims

1. A method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning, characterized in that: Here are the steps: S1. Identify critical failure modes and dependencies of subsea trees Based on the identified key failure modes and main failure dependencies, a failure dependency model of the system is constructed using a Bayesian network, and the dependencies between key components are clarified in a matrix form; S2. Data Collection and Processing Collect historical fault data of the underwater oil tree, including the fault time, fault type, fault impact, fault propagation scenario of each component, as well as the operation log and maintenance record of the underwater oil tree, and collect the operation data of the underwater oil tree from various sensors of the underwater oil tree, including pressure, temperature, flow, and vibration parameters; process the collected historical fault data and operation data; S3. Construct a fault propagation path development trajectory prediction model based on deep learning According to the complexity of the fault propagation path of the underwater oil tree and the data characteristics, the deep learning prediction model is selected as the convolutional neural network model and the long short-term memory network model, and a deep learning-based fault propagation path development trajectory prediction model of the time series underwater oil tree is constructed; S4. Prediction model validation, evaluation and optimization S5. Visualization of prediction results of fault propagation path development trajectory Develop user interaction functions to allow selection of time periods and units, viewing of detailed information, collection of user feedback, and continuous optimization of models and visualizations.

2. The method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning according to claim 1, characterized in that: In step S1, the specific implementation process of determining the key failure modes and dependencies of the subsea Christmas tree is as follows: Determine the operating indicators of the subsea tree, including pressure, temperature, flow, determine the operating range of key components of the subsea tree, and define the boundary conditions for the normal operation of the subsea tree; Through comprehensive analysis of the subsea Christmas tree's operating history data, maintenance records, and expert experience, we identify failure modes, including leakage, blockage, corrosion, and wear, and describe each failure mode, including its manifestation, possible causes, and impact range; Use fault tree analysis and event tree analysis to analyze the dependencies between various fault modes, build a fault propagation network, and clarify the connection relationship and propagation path between various fault nodes; According to the frequency of occurrence, impact and maintenance difficulty of faults, the key fault modes and main fault dependencies that have the greatest impact on the operation safety and production efficiency of subsea Christmas trees are screened out; Based on the identified key failure modes and main failure dependencies, a failure dependency model of the system is constructed using a Bayesian network, and the dependencies between key components are clarified in matrix form.

3. The method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning according to claim 2, characterized in that: In step S2, the specific implementation process of data collection and processing is as follows: The collected historical fault data and operation data are processed. First, the collected data is cleaned to remove duplicates, missing values, and outliers, and then normalized and standardized to improve the consistency and comparability of the data. The processed data is used to simulate the fault propagation process under different environmental conditions through the Monte Carlo method to generate a large number of simulated virtual fault sample data; The processed historical fault data and simulated virtual sample data are integrated to generate fault data sets with different cascading fault scenarios under different operating conditions; the time series analysis ARIMA model is used to model the fault data set to obtain linear prediction values ; The linear prediction value As additional input features, they are embedded in the input layer of the deep learning prediction model to be built together with the fault dataset.

4. The method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning according to claim 3 is characterized in that: In step S3, the specific implementation process of constructing a fault propagation path development trajectory prediction model based on deep learning is as follows: The fault propagation path development trajectory prediction model uses a convolutional neural network model to extract the spatial features of the fault data set, see formula (1), and inputs the extracted spatial features into the long short-term memory network model as a time series to extract the final time dynamic spatial features, see formula (2); at the same time, the linear prediction value in step S2 is used as an additional input feature and embedded into the input layer of the deep learning prediction model together with the fault data set to enhance the interpretation ability and prediction performance of the deep learning prediction model; the output layer of the deep learning prediction model is the prediction target, including fault type, fault propagation probability, fault propagation direction and component state change; adjustable input parameters are designed, including node dependency strength, environmental factors, and fault type, to adapt to the fault propagation characteristics under different working conditions; The fault data set preprocessed in step S2 is divided into a training set, a validation set and a test set, and the deep learning prediction model is trained and cross-validated; the prediction performance of the deep learning prediction model is evaluated by using mean square error, root mean square error, mean absolute error and R² statistical indicators, and the prediction accuracy of the deep learning prediction model is further improved by parameter tuning; （1） （2）。 5. The method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning according to claim 4, characterized in that: In step S4, the specific implementation process of prediction model verification, evaluation and optimization is as follows: The constructed deep learning prediction model is used to predict the fault propagation path of the underwater oil tree. The prediction results include fault type, fault propagation probability, fault propagation direction and component status change. The prediction accuracy, prediction efficiency and robustness indicators are used to evaluate the prediction ability of the deep learning prediction model in predicting the above results, and to compare and verify the performance of the deep learning prediction model under different acceleration conditions and extreme conditions. The deep learning prediction model is retrained and optimized based on the verification results, including adjusting the model structure, optimizing parameter settings, and improving data preprocessing methods to improve the prediction accuracy and generalization ability of the deep learning prediction model. The prediction results of the optimized deep learning prediction model are compared and verified with the actual situation, the prediction performance of the model is evaluated, and the model is corrected and optimized based on the verification results.

6. The method for predicting the development trajectory of a Christmas tree fault propagation path based on deep learning according to claim 5, characterized in that: In step S6, the specific implementation process of visualizing the prediction result of the fault propagation path development trajectory is as follows: Import the prediction results into the NetworkX library to build a complex network graph model of deepwater oil production tree fault propagation based on nodes and edges; create component nodes and status nodes, and create edges based on the predicted fault propagation path to indicate the direction of fault propagation from one unit to another; dynamically update the node color or size based on the prediction results of the deep learning prediction model to indicate the degree of unit degradation, equipment availability or system health status; dynamically update the edge width based on the fault propagation path predicted by the deep learning prediction model to indicate the severity of fault propagation; Create dynamically updated charts that show component status changes over time, enabling real-time visualization and prediction of deepwater tree fault propagation.

Images