R
Rebecca Martina Kohl
Publications - 13
Citations - 172
Rebecca Martina Kohl is an academic researcher. The author has contributed to research in topics: Computer science & Domino effect. The author has an hindex of 8, co-authored 13 publications receiving 172 citations. Previous affiliations of Rebecca Martina Kohl include Texas A&M University.
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Operational subsea pipeline assessment affected by multiple defects of microbiologically influenced corrosion
TL;DR: In this paper , the authors present a systematic approach to evaluate the time interval of optimal maintenance strategy for the subsea process system influenced by Microbiological Influenced Corrosion (MIC) within multiple defects.
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Operational Subsea pipeline Assessment Affected by Multiple Defects of Microbiologically Influenced Corrosion
Mohammad Reza Hairi Yazdi,Mohammad Reza Hairi Yazdi,Rebecca Martina Kohl,Faisal Khan,Faisal Khan,Rouzbeh Abbassi,Rouzbeh Abbassi +6 more
TL;DR: In this paper, the authors present a systematic approach to evaluate the time interval of optimal maintenance strategy for the subsea process system influenced by Microbiological Influenced Corrosion (MIC) within multiple defects.
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A novel vulnerability model considering synergistic effect of fire and overpressure in chemical processing facilities
TL;DR: In this article , a novel vulnerability model called the "fire and explosion synergistic effect model" (FESEM) is proposed to model equipment vulnerability under the spatial-temporal synergistic of heat radiation and overpressure.
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A novel vulnerability model considering synergistic effect of fire and overpressure in chemical processing facilities
Long Ding,Long Ding,Rebecca Martina Kohl,Faisal Khan,Santiago Cuéllar Rodríguez,Faisal Khan,Jie Ji +6 more
TL;DR: In this article, a novel vulnerability model called the "fire and explosion synergistic effect model" (FESEM) is proposed to model equipment vulnerability under the spatial-temporal synergistic of heat radiation and overpressure.
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Autonomous Fault Diagnosis and Root Cause Analysis for the Processing System Using One-Class SVM and NN Permutation Algorithm
TL;DR: In this paper , the root cause analysis of a detected fault in a complex processing system is performed by using a neural network and a permutation algorithm to extract the variable's contribution to the classified fault condition.