Bayesian framework for reliability prediction of subsea processing systems accounting for influencing factors uncertainty

Bhardwaj, U.; Teixeira, A.P.; Soares, C. Guedes

doi:10.1016/j.ress.2021.108143

Cited by 33 publications

(5 citation statements)

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“…Offshore accident scenarios have different characteristics, risks and consequences [13][14][15][16]. The greatest attention is paid to understanding and modelling fire and explosion risks in offshore facilities [5,[17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Analysis of Basic Causes of Vessel–Platform Allision Accidents

Bhardwaj,

Teixeira,

Guedes Soares

2024

JMSE

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This paper proposes a methodology to estimate the probability of basic causes of allision accidents between vessels and offshore platforms that overcomes the problem of data scarcity required for causal analysis. The approach uses information derived from incidental data and expert elicitation, processed by a multiple attribute utility method and hierarchical Bayesian analysis. First, the methodology is detailed, briefly describing the adopted approaches. A dataset of allision incidents provided mainly by the UK Health and Safety Executive and other agencies is prepared. The features of the incidents’ causation in terms of the causal factors and basic causes are presented and discussed. A novel scheme is proposed to evaluate the annual occurrence rates of basic causes of accidents from the relative importance of each basic cause derived by the Deck of Cards method. Then, a hierarchical Bayesian analysis is conducted to predict the posterior distribution of the occurrence rate of each basic cause in the time frame under analysis. The proposed holistic methodology provides transparent estimates of allision causation probabilities from limited and heterogeneous datasets.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Analysis of Basic Causes of Vessel–Platform Allision Accidents

Bhardwaj,

Teixeira,

Guedes Soares

2024

JMSE

Self Cite

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“…There are two main methods for RUL prediction: physical model-based methods and data-driven methods [3]. Physical model-based methods mainly constructs a parameterized mathematical model describing the degradation process of systems based on the failure mechanism, and updates the mechanism model parameters based on state monitoring data to achieve the RUL [4], [5]. However, due to the complex and diverse fault mechanism of complex systems, it is difficult to establish an accurate physical model [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Remaining Useful Life Prediction Approach Combined eXtreme Gradient Boosting and Multi-Quantile Recurrent Neural Network

Yin,

Zhao,

Zhang

et al. 2024

IEEE Access

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Remaining useful life (RUL) prediction is a key technology to ensure the reliability and safety of high-end equipment. Deep learning is widely used for RUL prediction due to the excellent feature extraction ability and nonlinear fitting ability. Traditional recurrent neural networks adopt recursive strategy, which easily lead to the problems of error accumulation and low stability. On the other hand, most deep learning methods are used for point prediction and cannot quantify uncertainty in the prediction results. Although some probability prediction methods based on deep learning can provide probability prediction results, it require the assumption that the prediction results follow a specific distribution in advance. However, the distribution of most prediction results does not match a suitable distribution function. To solve above problems, a novel RUL prediction approach based on eXtreme gradient boosting (XGBoost) and multiquantile recursive neural network (MQ-RNN) is proposed in this article. Specifically, XGBoost is used to select features closely related to RUL, and the selected features are fed into MQ-RNN to train the RUL prediction model. The advantage of MQ-RNN is that it has non-parametric framework, prediction results can be obtained by multi-quantile regression, which does not require prior assumptions about the distribution of predicted results. Furthermore, the proposed framework is verified by C-MAPSS dataset. Finally, comparative experiments are conducted. The experimental results show that the proposed method maintains good predictive performance in both point estimation and interval estimation.INDEX TERMS Remaining useful life, eXtreme gradient boosting, Multi-quantile recursive neural network, Quantile regression, Forking sequences training scheme.

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“…Bayesian Network (BN) is a probabilistic graph model that can effectively solve various uncertainty problems [6]. It has been introduced into the reliability analysis of complex systems and has achieved successful applications, such as subsea production systems [7][8][9], electronic systems [10,11], manufacturing systems [12][13][14], computer numerical control machine tools [15,16], wind turbines [17,18]. However, the traditional BN can only carry out static analysis, and it is difficult to model the transition process among multiple states.…”

Section: Introductionmentioning

confidence: 99%

Air conditioning reliability analysis based on dynamic Bayesian network and Markov model

Xu,

Wang,

Zhou

et al. 2024

Int. J. Metrol. Qual. Eng.

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With the popularization of the air conditioning, its reliability during operation has gradually become a focus of attention. However, due to the uncertainty in the reliability analysis process, the accuracy of the results will be affected. To overcome this challenge, a method for air conditioner reliability analysis combining Dynamic Bayesian Network (DBN) and Markov Model (MM) is proposed. Firstly, orthogonal defect classification (ODC) is used to statistic and analyze the defect data of the air conditioning system, and the network structure of the DBN is determined based on the results of the analysis. Then, the state transfer probability of each node is obtained by MM, and then the reliability, steady state availability, and maintainability of the air conditioning system are analyzed. Finally, the effectiveness of the method is verified by a case study of air conditioning failure data. The results show that the steady state availability of the air conditioning system in this case is 0.996.

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Bayesian framework for reliability prediction of subsea processing systems accounting for influencing factors uncertainty

Cited by 33 publications

References 50 publications

Probabilistic Analysis of Basic Causes of Vessel–Platform Allision Accidents

Probabilistic Analysis of Basic Causes of Vessel–Platform Allision Accidents

A Novel Remaining Useful Life Prediction Approach Combined eXtreme Gradient Boosting and Multi-Quantile Recurrent Neural Network

Air conditioning reliability analysis based on dynamic Bayesian network and Markov model

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