Application of Bayesian network and artificial intelligence to reduce accident/incident rates in oil &amp; gas companies

Sattari, Fereshteh; Macciotta, Renato; Kurian, Daniel; Lefsrud, Lianne

doi:10.1016/j.ssci.2020.104981

Cited by 35 publications

(15 citation statements)

References 38 publications

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“…According to this literature review in the asset maintenance research field, the research focus is mainly on predicting the occurrence of component failures in order to reduce unexpected events and the consequent stoppage of the production processes. Thus, awareness in the decision-making process is mandatory for achieving satisfying levels of reliability and avoiding the waste of resources (Sattari et al, 2021). The SNA is even rarer in this field of research: to the best of the authors' knowledge, only Kim et al (2019) apply such technique to define the optimal maintenance schedule in a cost reduction perspective, even though they do not employ the ARM, neither optimize the component selection taking into account time, budget and personnel constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

Antomarioni

Ciarapica

2022

IJQRM

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PurposeThe research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.Design/methodology/approachAssets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).FindingsA case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.Originality/valueThe novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.

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Section: Literature Reviewmentioning

confidence: 99%

Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

Antomarioni

Ciarapica

2022

IJQRM

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“…In addition, one of the most critical conditions for industrial intelligence involves the skills of workers. Recent research argues that industrial intelligence creates better technology that reduces occupational injuries [ 16 ].…”

Section: Overviewmentioning

confidence: 99%

“…Recent research argues that robots could replace workers in dangerous work environments, for instance, in chemical and mining industries, with a strong impact on the reduction of occupational injuries [ 16 , 17 ]. However, according to Yang et al [ 18 ], robot applications do not have a persistent impact on occupational injuries and can even increase the rate of occupational injuries in developing countries.…”

Section: Overviewmentioning

confidence: 99%

Frontiers in Occupational Health and Safety Management

Ramos

Cotrim

Arezes

et al. 2022

IJERPH

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“…This can be realized using ML for activities such as process safety management (PSM), risk-based inspection (RBI), disaster assessments and production and maintenance related tasks. In these cases, ML has helped to expedite data collection and analysis processes in PSM, predict the vulnerability and disruption in disaster assessments, improve quality of conventional RBI by reducing output variability and increasing precision and accuracy [6,[45][46][47]. [48] discussed the use of natural language processing, to help with the acquisition of knowledge related to accidents throughout the supply chain.…”

Section: Other Applications Of Ai Along the Whole Oil And Gas Supply ...mentioning

confidence: 99%

“…NLP was used to improve data acquisition, to help discover causal accident relations from databases. Offshore Robotics Shukla and Karki (2016b) [37] Predict and assess disasters Machine learning -Supervised learning Sattari et al (2021) [45] Predict and assess disasters Machine learning -Supervised learning Sakib et al (2021) [46] Oil and Gas industry tasks Machine learning; Multi-agent Systems Hanga and Kovalchuk (2019) [6] Risk based inspection Machine learning Rachman and Ratnayake (2019) [47] Accident exploration Natural language processing Single et al (2020) [48]…”

Section: Descriptive Analysis For Ai Location and Technology Typementioning

confidence: 99%

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Deif

Vivek

2022

HighTech. Innov. J.

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The purpose of this paper is to gain a better understanding of Artificial Intelligence (AI) application dynamics in the oil and gas supply chain. A location perspective is used to explore the opportunities and challenges of specific AI technologies from upstream to downstream of the oil and gas supply chain. A literature review approach is adopted to capture representative research along these locations. This was followed by descriptive and comparative analysis for the reviewed literature. Results from the conducted analysis revealed important insights about AI implementation dynamics in the oil and gas industry. Furthermore, various recommendations for technology managers, policymakers, practitioners, and industry leaders in the oil and gas industry to ensure successful AI implementation were outlined. Doi: 10.28991/HIJ-SP2022-03-01 Full Text: PDF

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Application of Bayesian network and artificial intelligence to reduce accident/incident rates in oil & gas companies

Cited by 35 publications

References 38 publications

Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

Data-driven approach to predict the sequence of component failures: a framework and a case study on a process industry

Frontiers in Occupational Health and Safety Management

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

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