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Martin Di Blasi
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Proceedings Papers

Machine Learning-Based Severity Assessment of Pipeline Dents

Huang Tang, Jialin Sun, Martin Di Blasi
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A019, September 26–30, 2022
Paper No: IPC2022-87211
DOI: https://doi.org/10.1115/IPC2022-87211
View Paper titled, Machine Learning-Based Severity Assessment of Pipeline Dents
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Topics: Finite element analysis, Machine learning, Machinery, Pipelines, Engineering prototypes, Failure, Maintenance, Reliability, Damage, Deformation
Proceedings Papers

Addressing Data Gaps for Facility Reliability Assessments Using Non-Hierarchical Cluster Analysis

Ryan Stewart, Martin Di Blasi, Thomas Dessein
Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A013, September 26–30, 2022
Paper No: IPC2022-87145
DOI: https://doi.org/10.1115/IPC2022-87145
View Paper titled, Addressing Data Gaps for Facility Reliability Assessments Using Non-Hierarchical Cluster Analysis
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Topics: Machine learning, Reliability, Risk assessment, Risk, Algorithms, Compressors, Data collection, Materials properties, Pipelines, Pressure
Proceedings Papers

Asset Complexity Based Benchmarks in Support of Reliability Improvement Program

Martin Di Blasi, Scott Denny
Proc. ASME. IPC2020, Volume 2: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain Based Design; Risk and Reliability; Northern, Offshore, and Production Pipelines, V002T07A021, September 28–30, 2020
Paper No: IPC2020-9738
DOI: https://doi.org/10.1115/IPC2020-9738
View Paper titled, Asset Complexity Based Benchmarks in Support of Reliability Improvement Program
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Topics: Decision making, Maintenance, Reliability
Proceedings Papers

Pipeline Rupture Detection Based on Machine Learning and Pattern Recognition

Martin Di Blasi, Zhan Li
Proc. ASME. IPC2016, Volume 3: Operations, Monitoring and Maintenance; Materials and Joining, V003T04A011, September 26–30, 2016
Paper No: IPC2016-64471
DOI: https://doi.org/10.1115/IPC2016-64471
View Paper titled, Pipeline Rupture Detection Based on Machine Learning and Pattern Recognition
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Topics: Machine learning, Pattern recognition, Pipelines, Rupture, Leakage, American Petroleum Institute, Cybernetics, Reliability
Journal Articles

Leak Detection in a Pipeline Using Modified Line Volume Balance and Sequential Probability Tests

Martín Di Blasi, Carlos Muravchik
Journal: Journal of Pressure Vessel Technology
Article Type: Research Papers
J. Pressure Vessel Technol. April 2009, 131(2): 021701.
DOI: https://doi.org/10.1115/1.3062936
Published Online: January 21, 2009
View Article titled, Leak Detection in a Pipeline Using Modified Line Volume Balance and Sequential Probability Tests
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Topics: Leakage, Pipelines, Flow (Dynamics), Probability, Pressure