[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Soomro et al., 2024 - Google Patents

Analysis of machine learning models and data sources to forecast burst pressure of petroleum corroded pipelines: A comprehensive review

Soomro et al., 2024

Document ID
2817458777100032298
Author
Soomro A
Mokhtar A
Hussin H
Lashari N
Oladosu T
Jameel S
Inayat M
Publication year
Publication venue
Engineering Failure Analysis

External Links

Snippet

A comprehensive evaluation of the integrity of oil and gas pipelines subjected to corrosion defect is required for forecasting health & safety actions. If corrosion is ignored, it may have significant repercussions on a person's health, finances, and the environment. The …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/086—Learning methods using evolutionary programming, e.g. genetic algorithms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02—Banking, e.g. interest calculation, credit approval, mortgages, home banking or on-line banking
    • G06Q40/025—Credit processing or loan processing, e.g. risk analysis for mortgages
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G06Q10/0635—Risk analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G06Q10/0639—Performance analysis
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public service
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance, e.g. risk analysis or pensions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30289—Database design, administration or maintenance
    • G06F17/30303—Improving data quality; Data cleansing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis

Similar Documents

Publication Publication Date Title
Soomro et al. Integrity assessment of corroded oil and gas pipelines using machine learning: A systematic review
Li et al. A data-driven corrosion prediction model to support digitization of subsea operations
Dawood et al. Artificial intelligence for the modeling of water pipes deterioration mechanisms
Rachman et al. Applications of machine learning in pipeline integrity management: A state-of-the-art review
Liu et al. XGBoost algorithm-based prediction of safety assessment for pipelines
Ma et al. Advances in corrosion growth modeling for oil and gas pipelines: A review
El-Abbasy et al. Artificial neural network models for predicting condition of offshore oil and gas pipelines
Al-Sabaeei et al. Prediction of oil and gas pipeline failures through machine learning approaches: A systematic review
Soomro et al. Analysis of machine learning models and data sources to forecast burst pressure of petroleum corroded pipelines: A comprehensive review
Meng et al. A data-driven Bayesian network model integrating physical knowledge for prioritization of risk influencing factors
Soomro et al. A review on Bayesian modeling approach to quantify failure risk assessment of oil and gas pipelines due to corrosion
Jana et al. Novel type-2 fuzzy logic approach for inference of corrosion failure likelihood of oil and gas pipeline industry
Yin et al. An integrated framework for criticality evaluation of oil & gas pipelines based on fuzzy logic inference and machine learning
Yao et al. Novel intelligent diagnosis method of oil and gas pipeline defects with transfer deep learning and feature fusion
Li et al. A data-driven methodology for predicting residual strength of subsea pipeline with double corrosion defects
Aditiyawarman et al. The study of artificial intelligent in risk-based inspection assessment and screening: A study case of inline inspection
Aalirezaei et al. A bibliometric analysis on oil and gas pipeline failure consequence analysis
Song et al. Interpretable machine learning for maximum corrosion depth and influence factor analysis
Li et al. Intelligent reliability and maintainability of energy infrastructure assets
Sun et al. Leakage failure probability assessment of submarine pipelines using a novel pythagorean fuzzy bayesian network methodology
Sarwar et al. Enhancing pipeline integrity: a comprehensive review of deep learning-enabled finite element analysis for stress corrosion cracking prediction
Woldesellasse et al. Risk analysis of onshore oil and gas pipelines: Literature review and bibliometric analysis
Taiwo et al. Integrated intelligent models for predicting water pipe failure probability
Miao et al. Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model
Xiao et al. Predicting failure pressure of corroded gas pipelines: A data-driven approach using machine learning