Safety of Pipelines Subjected to Deterioration Processes Modeled Through Dynamic Bayesian Networks

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
O. G. Palencia ◽  
A. P. Teixeira ◽  
C. Guedes Soares
Keyword(s):  
Bayesian Networks ◽  
Safety Assessment ◽  
Oil And Gas ◽  
Design Method ◽  
Dynamic Bayesian Networks ◽  
Burst Pressure ◽  
Probabilistic Framework ◽  
Empirical Formulas ◽  
Model Predictions ◽  
Parametric Studies

This paper studies the application of Dynamic Bayesian Networks (DBNs) for modeling degradation processes in oil and gas pipelines. A DBN tool consisting of a matlab code has been developed for performing inference on models. The tool is then applied for probabilistic modeling of the burst pressure of a pipe subjected to corrosion degradation and for safety assessment. The burst pressure is evaluated using the ASME B31G design method and other empirical formulas. A model for corrosion prediction in pipelines and its governing parameters are explicitly included into the probabilistic framework. Different sets of simulated corrosion measurements are used to increase the accuracy of the model predictions. Several parametric studies are conducted to investigate how changes in the observed corrosion (depth and length) and in the frequency of inspections affect the pipe reliability.

Safety of Pipelines Subjected to Deterioration Processes Modelled Through Dynamic Bayesian Networks

2017 ◽  
Author(s):  
O. G. Palencia ◽  
A. P. Teixeira ◽  
C. Guedes Soares
Keyword(s):  
Bayesian Networks ◽  
Safety Assessment ◽  
Oil And Gas ◽  
Design Method ◽  
Dynamic Bayesian Networks ◽  
Burst Pressure ◽  
Probabilistic Framework ◽  
Empirical Formulas ◽  
Model Predictions ◽  
Parametric Studies

The paper studies the application of Dynamic Bayesian Networks for modelling degradation processes in oil and gas pipelines. A DBN tool consisting of a Matlab code has been developed for performing inference on models. The tool is then applied for probabilistic modelling of the burst pressure of a pipe subjected to corrosion degradation and for safety assessment. The burst pressure is evaluated using the ASME B31G design method and other empirical formulas. A model for corrosion prediction in pipelines and its governing parameters are explicitly included into the probabilistic framework. Different sets of simulated corrosion measurements are used to increase the accuracy of the model predictions. Several parametric studies are conducted to investigate how changes in the observed corrosion (depth and length) and in the frequency of inspections affect the pipe reliability.

scholarly journals A Dynamic-Bayesian-Networks-Based Resilience Assessment Approach of Structure Systems: Subsea Oil and Gas Pipelines as A Case Study

2020 ◽  
Vol 34 (5) ◽  
pp. 597-607
Author(s):  
Bao-ping Cai ◽  
Yan-ping Zhang ◽  
Xiao-bing Yuan ◽  
Chun-tan Gao ◽  
Yong-hong Liu ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Oil And Gas ◽  
Dynamic Bayesian Networks ◽  
Gas Pipelines ◽  
Oil And Gas Pipelines ◽  
Assessment Approach ◽  
Resilience Assessment

Multiple small objects tracking based on dynamic Bayesian networks with spatial prior

Optik ◽  
2014 ◽  
Vol 125 (10) ◽  
pp. 2243-2247 ◽  
Author(s):  
Rui Yao ◽  
Yanning Zhang ◽  
Yong Zhou ◽  
Shixiong Xia
Keyword(s):  
Bayesian Networks ◽  
Dynamic Bayesian Networks ◽  
Objects Tracking

Multimodal Sensor Fusion for Indoor Occupancy Determination

2015 ◽  
Vol 764-765 ◽  
pp. 1319-1323
Author(s):  
Rong Shue Hsiao ◽  
Ding Bing Lin ◽  
Hsin Piao Lin ◽  
Jin Wang Zhou
Keyword(s):  
Bayesian Networks ◽  
Sensor Fusion ◽  
Dynamic Bayesian Networks ◽  
Sensor Nodes ◽  
Detection Accuracy ◽  
Multisensor Fusion ◽  
Fusion Algorithm ◽  
Improve Energy Efficiency ◽  
Occupancy Detection ◽  
Automation Systems

Pyroelectric infrared (PIR) sensors can detect the presence of human without the need to carry any device, which are widely used for human presence detection in home/office automation systems in order to improve energy efficiency. However, PIR detection is based on the movement of occupants. For occupancy detection, PIR sensors have inherent limitation when occupants remain relatively still. Multisensor fusion technology takes advantage of redundant, complementary, or more timely information from different modal sensors, which is considered an effective approach for solving the uncertainty and unreliability problems of sensing. In this paper, we proposed a simple multimodal sensor fusion algorithm, which is very suitable to be manipulated by the sensor nodes of wireless sensor networks. The inference algorithm was evaluated for the sensor detection accuracy and compared to the multisensor fusion using dynamic Bayesian networks. The experimental results showed that a detection accuracy of 97% in room occupancy can be achieved. The accuracy of occupancy detection is very close to that of the dynamic Bayesian networks.

A novel brace with partial buckling restraint: Parametric studies and design method

Structures ◽  
2021 ◽  
Vol 34 ◽  
pp. 1734-1745
Author(s):  
Yiyi Zhou ◽  
Yuan Gao ◽  
Chun-Lin Wang ◽  
Lian-meng Chen
Keyword(s):  
Design Method ◽  
Parametric Studies

The Failure Window Method and Its Application in Pipeline Burst

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
Zhanfeng Chen ◽  
Hao Ye ◽  
Sunting Yan ◽  
Xiaoli Shen ◽  
Zhijiang Jin
Keyword(s):  
Internal Pressure ◽  
Maximum Stress ◽  
Oil And Gas ◽  
Maximum Shear Stress ◽  
High Accuracy ◽  
Burst Pressure ◽  
Empirical Equations ◽  
Integrity Assessment ◽  
Wall Thinning ◽  
Window Method

Accurate prediction of the burst pressure is indispensible for the engineering design and integrity assessment of the oil and gas pipelines. A plenty of analytical and empirical equations have been proposed to predict the burst pressures of the pipelines; however, it is difficult to accurately predict the burst pressures and evaluate the accuracy of these equations. In this paper, a failure window method was presented to predict the burst pressure of the pipes. First, the security of the steel pipelines under the internal pressure can be assessed. And then the accuracy of the previous analytical and empirical equations can also be generally evaluated. Finally, the effect of the wall thinning of the pipes on the failure window was systemically investigated. The results indicate that it is extremely formidable to establish an equation to predict the burst pressure with a high accuracy and a broad application, while it is feasible to create a failure window to determine the range of the dangerous internal pressure. Calculations reveal that some predictions of the burst pressure equations like Faupel, Soderberg, Maximum stress, and Nadai (1) are overestimated to some extent; some like ASME, maximum shear stress, Turner, Klever and Zhu–Leis and Baily–Nadai (2) basically reliable; the rest like API and Nadai (3) slightly conservative. With the wall thinning of the steel pipelines, the failure window is gradually lowered and narrowed.

Modified Posicast Control Design Method Based on the Parameters of a Fractional Order System

2021 ◽  
Vol 143 (4) ◽  
Author(s):  
Erhan Yumuk ◽  
Müjde Güzelkaya ◽  
İbrahim Eksin
Keyword(s):  
Real Time ◽  
Fractional Order ◽  
Control Method ◽  
Design Method ◽  
Order System ◽  
Half Cycle ◽  
Empirical Formulas ◽  
Real Time System ◽  
Novel Design ◽  
Better Than

Abstract In this study, a novel design method for half-cycle and modified posicast controller structures is proposed for a class of the fractional order systems. In this method, all required design variable values, namely, the input step magnitudes and their application times are obtained as functions of fractional system parameters. Moreover, empirical formulas are obtained for the overshoot values of the compensated system with half-cycle and modified posicast controllers designed utilizing this method. The proposed design methodology has been tested via simulations and ball balancing real-time system. It is observed that the derived formulas are in coherence with outcomes of the simulation and real-time application. Furthermore, the performance of modified posicast controller designed using proposed method is much better than other posicast control method.

scholarly journals A dynamic Bayesian network approach for prognosis computations on discrete state systems

2017 ◽  
Vol 231 (5) ◽  
pp. 516-533 ◽  
Author(s):  
Josquin Foulliaron ◽  
Laurent Bouillaut ◽  
Patrice Aknin ◽  
Anne Barros
Keyword(s):  
Complex Systems ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Multicomponent System ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Discrete State ◽  
Virtual Maintenance

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

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