Probability Prediction Approach of Fatigue Failure for the Subsea Wellhead Using Bayesian Regularization Artificial Neural Network

Li, Jiayi; Chang, Yuanjiang; Shi, Jihao; Liu, Xiuquan; Chen, Guo-Ming; Zhang, Nan; Guan, Qingtao; Dai, Yongguo

doi:10.3390/jmse10111627

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…The failure load cycle distribution and the reliability-based performance assessment of API 5L X56 submarine pipelines, as a case study, were estimated for three different cases. Based on the Bayesian Regularization Artificial Neuron Network, Li et al [18] proposed an efficient probability approach that could be used to predict the fatigue failure probability of the subsea wellhead system during its entire life. This paper takes full advantage of Bayesian inference in order to establish the causal relationship between pressure self-enhancing parameters and the structural life, and to predict the RUL of structures under complex multi-factorial underwater conditions.…”

Section: Introductionmentioning

confidence: 99%

Prediction Method for RUL of Underwater Self-Enhancement Structure: Subsea Christmas Tree High-Pressure Valve Actuator as a Case Study

Liu,

Dai,

Zhao

et al. 2023

JMSE

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Underwater pressure-bearing structures are produced in practice by means of pressure self-enhancement methods in order to improve the stress distribution and enhance the pressure-bearing performance. On the other hand, the pairs equation shows that stress is an important factor influencing the degradation of the structure. In fact, improving the stress distribution will not only improve the pressure-bearing performance, but will have an impact on the life degradation trend. Thus, pressure self-enhancement affects the structural life by changing the stress distribution. With this in mind, this paper considers the effect of pressure self-enhancement on the service time of subsea structures, and a Bayesian network (BN)-based method that can be used to predict the remaining useful life (RUL) of underwater self-enhanced structures is proposed. The method also takes into account the influence of multiple sources of structural factors in order to predict the RUL of the structure more accurately. The life degradation process of an all-electric Christmas tree valve actuator is used as a case study. The prediction results are compared with data in the literature to verify the validity of the method. The results have implications for guidance on the O&M assurance of underwater production systems.

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Section: Introductionmentioning

confidence: 99%

Prediction Method for RUL of Underwater Self-Enhancement Structure: Subsea Christmas Tree High-Pressure Valve Actuator as a Case Study

Liu,

Dai,

Zhao

et al. 2023

JMSE

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“…The subsea wellhead (SW) system is one of the crucial components that connects between blowout preventors and subsea oil and gas wells. Li et al [7] proposed an efficient probability approach based on the Bayesian Regularization Artificial Neuron Network to predict the fatigue failure probability of SW during its entire life. They concluded that the fatigue failure probability of SW nonlinearly increased nonlinearly with the increase in fatigue damage, which is helpful in ensuring the operational safety of SW in deepwater oil and gas development.…”

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confidence: 99%

Theory, Method and Engineering Application of Computational Mechanics in Offshore Structures

Yan

et al. 2023

JMSE

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Yaghoubi,

Khamees

et al. 2024

Neural Comput & Applic

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Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.

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Probability Prediction Approach of Fatigue Failure for the Subsea Wellhead Using Bayesian Regularization Artificial Neural Network

Cited by 6 publications

References 40 publications

Prediction Method for RUL of Underwater Self-Enhancement Structure: Subsea Christmas Tree High-Pressure Valve Actuator as a Case Study

Prediction Method for RUL of Underwater Self-Enhancement Structure: Subsea Christmas Tree High-Pressure Valve Actuator as a Case Study

Theory, Method and Engineering Application of Computational Mechanics in Offshore Structures

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

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