Intelligent Health Monitoring and Fault Diagnosis for Offshore Oil and Gas Platform Equipment: A Systematic Review

Payam PourSheikh Ali Asghari¹ Alireza Mojtahedi² Mohammed Ali Lotfollahi Yagin³

1) Misters
2) DOCTOR
3) DOCTOR

IOI Code : ABGG-ACAAHK
Publication : 11th.International Conference on Researches in Science & Engineering & 8th.International Congress on Civil, Architecture and Urbanism in Asia(11icrsie.ir)
Abstract :
    condensed Offshore oil and gas platforms operate in corrosive and fluctuating environments, and the failure of their equipment has serious safety, environmental, and economic consequences. For this reason, intelligent health monitoring and fault diagnosis (FDI) has become a key solution for asset integrity management (AIM) and prevention of unintended downtime. With the aim of identifying dominant methods, areas of application, challenges and research gaps, this research has investigated the use of artificial intelligence and new technologies in monitoring the health of offshore platforms equipment with a systematic review approach based on the PRISMA standard protocol. A systematic search was conducted in Google Scholar, Scopus, ScienceDirect, OnePetro scientific databases for articles published from 2020 to 2026. The process of evaluation and screening of articles was applied based on the inclusion criteria (explicit focus on artificial intelligence and machine learning, directly addressing health monitoring or fault diagnosis in oil and gas) and exclusion (studies solely focused on onshore facilities, and studies lacking quantitative data). Among the 104 initially identified articles, 18 eligible articles were selected for the final qualitative analysis. The findings showed that researches are categorized in four main axes: (1) signal-based fault diagnosis; (2) asset integrity management and predictive maintenance; (3) digital monitoring systems based on Internet of Things (IoT) and edge computing; and (4) safety, environmental monitoring and cyber security applications. Common challenges include lack of real data, standardization problems, interoperability and lack of field validation. The final conclusion shows that despite the high potential of smart technologies, their industrial implementation requires passing from simulated data to field validation, development of explainable models (XAI) and integration with digital twins in real operational conditions.

Keywords : smart health monitoring fault diagnosis asset integrity management offshore platforms artificial intelligence