TY - JOUR
T1 - A Recent Review of Risk-Based Inspection Development to Support Service Excellence in the Oil and Gas Industry
T2 - An Artificial Intelligence Perspective
AU - Aditiyawarman, Taufik
AU - Kaban, Agus Paul Setiawan
AU - Soedarsono, Johny Wahyuadi
N1 - Funding Information:
Direktorat Jenderal Pendidikan Tinggi (Funder ID: 10.13039/501100005981).
Publisher Copyright:
Copyright © 2023 by ASME.
PY - 2023/3
Y1 - 2023/3
N2 - Inspection and Maintenance methods development have a pivotal role in preventing the uncertainty-induced risks in the oil and gas industry. A key aspect of inspection is evaluating the risk of equipment from the scheduled and monitored assessment in the dynamic system. This activity includes assessing the modification factor's probability of failure and calculating the equipment's remaining useful life (RUL). The traditional inspection model constitutes a partial solution to grouping the vast amount of real-data inspection and observations at equal intervals. This literature review aims to offer a comprehensive review concerning the benefit of machine learning in managing the risk while incorporating time-series forecasting studies and an overview of risk-based inspection methods (e.g., quantitative, semiquantitative, and qualitative). A literature review with a deductive approach is used to discuss the improvement of the clustering Gaussian mixture model to overcome the noncircular shape data that may show in the K-Means models. Machine learning classifiers such as Decision Trees, Logistic Regression, Support Vector Machines, K-nearest neighbors, and Random Forests were selected to provide a platform for risk assessment and give a promising prediction toward the actual condition and the severity level of equipment. This work approaches complementary tools and grows interest in embedded artificial intelligence in Risk Management systems and can be used as the basis of more robust guidance to organize complexity in handling inspection data, but further and future research is required.
AB - Inspection and Maintenance methods development have a pivotal role in preventing the uncertainty-induced risks in the oil and gas industry. A key aspect of inspection is evaluating the risk of equipment from the scheduled and monitored assessment in the dynamic system. This activity includes assessing the modification factor's probability of failure and calculating the equipment's remaining useful life (RUL). The traditional inspection model constitutes a partial solution to grouping the vast amount of real-data inspection and observations at equal intervals. This literature review aims to offer a comprehensive review concerning the benefit of machine learning in managing the risk while incorporating time-series forecasting studies and an overview of risk-based inspection methods (e.g., quantitative, semiquantitative, and qualitative). A literature review with a deductive approach is used to discuss the improvement of the clustering Gaussian mixture model to overcome the noncircular shape data that may show in the K-Means models. Machine learning classifiers such as Decision Trees, Logistic Regression, Support Vector Machines, K-nearest neighbors, and Random Forests were selected to provide a platform for risk assessment and give a promising prediction toward the actual condition and the severity level of equipment. This work approaches complementary tools and grows interest in embedded artificial intelligence in Risk Management systems and can be used as the basis of more robust guidance to organize complexity in handling inspection data, but further and future research is required.
KW - artificial intelligence
KW - risk-based inspection
KW - risk-management
KW - Supervised machine learning
KW - Unsupervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=85133751319&partnerID=8YFLogxK
U2 - 10.1115/1.4054558
DO - 10.1115/1.4054558
M3 - Article
AN - SCOPUS:85133751319
SN - 2332-9017
VL - 9
JO - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
JF - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
IS - 1
M1 - 010801
ER -