scholarly journals A Model-Based Bayesian Framework for Pipeline Leakage Enumeration and Location Estimation

2021 ◽  
Author(s):  
Juan Li ◽  
Ying Wu ◽  
Changgang Lu
Keyword(s):  
Bayesian Analysis ◽  
Probabilistic Method ◽  
Wave Theory ◽  
Measured Data ◽  
Location Estimation ◽  
Transient Wave ◽  
Model Based ◽  
Pipeline Valve ◽  
The Given ◽  
Specific Number

Abstract Leak detection in pipelines is an important issue, because leakages pose financial losses, environmental pollution and even health risks. The paper considers the problem of detecting multiple leaks based on transient wave theory for a reservoir pipeline valve system. The given measured data under consideration may contain one or multiple leaks originating from different locations when estimating the leak locations. This leads to two problems to be solved: first determine the correct number of leaks, and then identify the actual location of each leak. Thus, a probabilistic method of model-based Bayesian analysis is applied to this paper. This work employs a model to describe various scenes, individually defined by a specific number of leaks and their locations. Bayesian inference is used to select which model that is the most appropriate to fit the measured data. Through the process, the number of leaks is first estimated, and then the leak locations are extracted from the model that the measured data prefers. This paper presents different experimental setups and scenarios to demonstrate the availability of the proposed method, demonstrating that this model-based Bayesian analysis is an accurate tool for leakage enumeration and location estimation.

scholarly journals Optimization of Vehicle-to-Vehicle Frontal Crash Model Based on Measured Data Using Genetic Algorithm

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 3131-3138 ◽  
Author(s):  
Bernard B. Munyazikwiye ◽  
Hamid Reza Karimi ◽  
Kjell G. Robbersmyr
Keyword(s):  
Genetic Algorithm ◽  
Measured Data ◽  
Frontal Crash ◽  
Model Based ◽  
Vehicle To Vehicle ◽  
Crash Model

scholarly journals ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Yibo Li ◽  
Hang Li ◽  
Xiaonan Guo
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Output Data ◽  
Swarm Optimization ◽  
Model Based ◽  
Simulation Results ◽  
Filtering Effect ◽  
Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

Pressure Prediction in Natural Gas Desulfurization Process Based on PCA and SVR

2014 ◽  
Vol 962-965 ◽  
pp. 564-569 ◽  
Author(s):  
Yan Chao Shao ◽  
Liang Jun Xu ◽  
Yan Zhu Hu ◽  
Xin Bo Ai
Keyword(s):  
Natural Gas ◽  
Prediction Model ◽  
Measured Data ◽  
Support Vector ◽  
Trend Prediction ◽  
Generalization Ability ◽  
Model Based ◽  
Scada System ◽  
Desulfurization Process ◽  
Natural Gas Desulfurization

Pressure monitoring is an important means to reflect the running status of the natural gas desulphurization process. By using the data mining technology, the interaction relationships between the pressure and other monitoring parameters are analyzed in this paper. A pressure trend prediction model is established to show the pressure status in the natural gas desulfurization process. Firstly, the theory of Principal Component Analysis (PCA) is used to reduce the dimensions of measured data from traditional Supervisory Control and Data Acquisition (SCADA) system. Secondly the principal components are taken as input data into the pressure trend prediction model based on multiple regression theory of Support Vector Regression (SVR). Finally the accuracy and the generalization ability of the model are tested by the measured data obtained from SCADA system. Compared with other prediction models, pressure trend prediction model based on PCA and SVR gets smaller MSE and higher correlation. The pressure trend prediction model gets better generalization ability and stronger robustness, and is an effective complement to SCADA system in the natural gas desulphurization process.

Comparison of model-based results with measured data for metal buried mines

1998 ◽  
Author(s):  
Barbara L. Merchant ◽  
Ravinder Kapoor ◽  
Lawrence Carin
Keyword(s):  
Measured Data ◽  
Model Based

Advanced optical simulation of scintillation detectors in GATE V8.0: first implementation of a reflectance model based on measured data

2017 ◽  
Vol 62 (12) ◽  
pp. L1-L8 ◽  
Author(s):  
Mariele Stockhoff ◽  
Sebastien Jan ◽  
Albertine Dubois ◽  
Simon R Cherry ◽  
Emilie Roncali
Keyword(s):  
Measured Data ◽  
Optical Simulation ◽  
Scintillation Detectors ◽  
Model Based ◽  
Reflectance Model

Reliability Analysis and Design for Pipe-in-Pipe Pipelines With Centralizers

2015 ◽  
Author(s):  
Steve Mao ◽  
Muhammad Kamal ◽  
Wei Qiao ◽  
Gang Dong ◽  
Brian Duffy
Keyword(s):  
Fatigue Life ◽  
Reliability Analysis ◽  
Fatigue Damage ◽  
Failure Probability ◽  
Probabilistic Method ◽  
Pipeline System ◽  
Limit Function ◽  
Analysis And Design ◽  
Manufacturing Tolerances ◽  
The Given

In this paper, a simplified reliability model is developed to identify how the pipe-in-pipe component uncertainties (manufacturing tolerances of centralizer thickness) influence the fatigue life of the system. The focus is on the reliability analysis with respect to the centralizer thickness. In order to reduce the complexity of the problem, only the centralizer thickness is considered to be a random variable. A limit function is formulated based on the three dimension (3D) finite element analysis. With the help of the probabilistic method, the correlation between the centralizer thickness and the failure probability is investigated. Two examples on pipe-in-pipe pipeline system are analyzed. The first one presents the relationship between centralizer thickness and failure probability for inner and outer pipes. The second one is an application of six mile pipe-in-pipe pipeline system. The failure probability of the fatigue is estimated. The influence of the centralizer thickness decreasing with time due to the abrasion, creep wear and elastic deformation is also considered when computing fatigue life and failure probability. The maximum fatigue damage ratio is calculated based on all trial samples generated considering manufacturing tolerances. If the maximum fatigue damage ratio is less than or equal to the allowable fatigue damage ratio, the failure probabilities with respect to the given centralizer thickness is negligible and the design is acceptable if only considering the influence of the given centralizer thickness. In addition, numerical results show that the maximum fatigue damage ratio possibly exceeds the allowable fatigue damage ratio considering manufacturing tolerances although the deterministic fatigue damage ratio is less than the allowable fatigue damage ratio.

scholarly journals STOCHASTIC ANALYSIS OF TEMPERATURE FLUCTUATIONS IN FORESTS

2020 ◽  
Vol 8 (1) ◽  
pp. 48-52
Author(s):  
Natal'ya Evsikova ◽  
V. Lisitsyn ◽  
I. Terekhina
Keyword(s):  
Probability Distribution ◽  
Forest Fires ◽  
Woody Plants ◽  
Gaussian Function ◽  
Temperature Fluctuations ◽  
Measured Data ◽  
Temperature Changes ◽  
Degree Of Certainty ◽  
Sufficient Degree ◽  
The Given

When forecasting forest fires, particular importance is attached to the analysis of air temperature changes. Along with data such as precipitation and wind speed, temperature is a basic parameter for determining the drying rate of combustible materials and determining the parameters of the threat of fires (Nesterov index). At the same time, little importance is attached to studies of the patterns of changes in the probability distribution of the temperature fluctuations magnitude during the year. Temperature studies are descriptive, as a rule, estimates diverge, and the boundaries of the given intervals are blurred. The paper has analyzed the nature of the probability distribution of daily temperature fluctuations in the period from January to May. The analysis is based on known and regularly measured data. The results of the analysis of the data of measurements of the temperature of the day and evening showed that the probability distribution can be modeled by the Gaussian function with a sufficient degree of certainty. The parameters of Gaussian function characterize the features of the processes stimulated by temperature changes in the trunks of woody plants. In addition, it turned out that the most likely value of fluctuations is steadily increasing from January to March. It is known that in March intense sap flow occurs in the absence of transpiration currents. Therefore, we can confidently assume that at this time the upward currents in the tree trunks are thermally stimulated.

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