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Addressing Data Quality Issues in Pipeline Condition Assessment Using Machine Learning

https://doi.org/10.2139/ssrn.4672478
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inferredcites →https://doi.org/10.1002/9781119019213.ch01openalex-extracted
Pipeline Integrity Management Systems (PIMS)
inferredcites →https://doi.org/10.1007/978-3-319-25307-7openalex-extracted
Diagnostics and Reliability of Pipeline Systems
inferredcites →https://doi.org/10.1007/978-3-319-55944-5openalex-extracted
Modeling and Monitoring of Pipelines and Networks
inferredcites →https://doi.org/10.1007/s10115-018-1248-0openalex-extracted
The big data system, components, tools, and technologies: a survey
inferredcites →https://doi.org/10.1007/s10115-023-01870-1openalex-extracted
Data management in digital twins: a systematic literature review
inferredcites →https://doi.org/10.1016/j.apenergy.2012.01.015openalex-extracted
Development of approach for reliability assessment of pipeline network systems
inferredcites →https://doi.org/10.1016/j.compind.2019.06.007openalex-extracted
Oil and Gas 4.0 era: A systematic review and outlook
inferredcites →https://doi.org/10.1016/j.egyai.2020.100041openalex-extracted
Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future
inferredcites →https://doi.org/10.1016/j.engfailanal.2019.01.071openalex-extracted
Selection of key indicators for reputation loss in oil and gas pipeline failure event
inferredcites →https://doi.org/10.1016/j.inffus.2021.11.006openalex-extracted
Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges
inferredcites →https://doi.org/10.1016/j.jbusres.2016.08.007openalex-extracted
Factors influencing big data decision-making quality
inferredcites →https://doi.org/10.1016/j.jclepro.2021.125834openalex-extracted
Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
inferredcites →https://doi.org/10.1016/j.jnca.2016.08.002openalex-extracted
Data quality in internet of things: A state-of-the-art survey
inferredcites →https://doi.org/10.1016/j.neucom.2017.01.026openalex-extracted
Machine learning on big data: Opportunities and challenges
inferredcites →https://doi.org/10.1016/j.ptlrs.2021.05.009openalex-extracted
Application of machine learning and artificial intelligence in oil and gas industry
inferredcites →https://doi.org/10.1016/j.ress.2021.108271openalex-extracted
Integrated approach for pipe failure prediction and condition scoring in water infrastructure systems
inferredcites →https://doi.org/10.1016/j.rser.2012.02.047openalex-extracted
A review of urban energy system models: Approaches, challenges and opportunities
inferredcites →https://doi.org/10.1016/j.watres.2019.114926openalex-extracted
Improving pipe failure predictions: Factors affecting pipe failure in drinking water networks
inferredcites →https://doi.org/10.1080/08982112.2014.846119openalex-extracted
Reliability Meets Big Data: Opportunities and Challenges
inferredcites →https://doi.org/10.1080/09377255.2020.1815140openalex-extracted
Challenges when creating a cohesive digital twin ship: a data modelling perspective
inferredcites →https://doi.org/10.1108/13552510110386874openalex-extracted
A risk‐based model for inspection and maintenance of cross‐country petroleum pipeline
inferredcites →https://doi.org/10.1109/access.2014.2325029openalex-extracted
Big Data Deep Learning: Challenges and Perspectives
inferredcites →https://doi.org/10.1109/access.2021.3101284openalex-extracted
Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions
inferredcites →https://doi.org/10.1115/ipc2010-31306openalex-extracted
A Practical Application to Calculating Corrosion Growth Rates by Comparing Successive ILI Runs From Different ILI Vendors
inferredcites →https://doi.org/10.1186/s40537-015-0034-zopenalex-extracted
An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities
inferredcites →https://doi.org/10.1201/b23013-3openalex-extracted
Big Data Analytics in Oil and Gas Industry
inferredcites →https://doi.org/10.14778/2367502.2367572openalex-extracted
Challenges and opportunities with big data
inferredcites →https://doi.org/10.1504/ejie.2016.078805openalex-extracted
Maintaining pipeline integrity through holistic asset management
inferredcites →https://doi.org/10.2118/211089-msopenalex-extracted
Pipeline Integrty Management and the Invent of Analytics for Corrosion Threats
inferredcites →https://doi.org/10.2118/211225-msopenalex-extracted
Digitalization and Data Automation: The Path for a More Efficient Well Integrity Data Management and Cost-Effective Responses
inferredcites →https://doi.org/10.3390/rs13193936openalex-extracted
Close-Range Sensing and Data Fusion for Built Heritage Inspection and Monitoring—A Review
inferredcites →https://doi.org/10.3390/s19163556openalex-extracted
Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
inferredcites →https://doi.org/10.3390/s21113862openalex-extracted
Pipeline In-Line Inspection Method, Instrumentation and Data Management
inferredcites →https://doi.org/10.5006/c2012-01383openalex-extracted
Development and Application of Local Corrosion Growth Rates for Pipeline Integrity Assessments
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