Bayesian Model for Calibration of ILI Tools

2012 ◽  
Author(s):  
M. Al-Amin ◽  
W. Zhou ◽  
S. Zhang ◽  
S. Kariyawasam ◽  
H. Wang
Keyword(s):  
Measurement Error ◽  
Linear Function ◽  
Bayesian Model ◽  
Measurement Data ◽  
Metal Loss ◽  
Bayesian Methodology ◽  
Field Measuring ◽  
Corrosion Defects ◽  
Measuring Tool ◽  
Actual Depth

The Bayesian methodology is employed to calibrate the accuracy of high-resolution ILI tools for sizing metal-loss corrosion defects on pipelines by comparing the field-measured depths and ILI-reported depths for a set of static defects, i.e. defects that are recoated and ceased growing. The measurement error associated with the field-measuring tool is found to be negligibly small; therefore, the field-measured depth is assumed to equal the actual depth of the defect. The depth of a corrosion defect reported by an ILI tool is assumed to be a linear function of the corresponding field-measured depth subjected to a random scattering error. The probabilistic characteristics of the intercept and slope in the linear function, i.e. the constant and non-constant biases of the measurement error, as well as the standard deviation of the random scattering error are then quantified using the Bayesian methodology. The proposed methodology is able to calibrate the accuracies of multiple ILI tools simultaneously and quantify the potential correlations between the accuracies of different ILI tools. The methodology is illustrated using real ILI and field measurement data obtained on two pipelines currently in service.

Quantification of Measurement Errors in the Lengths of Metal-Loss Corrosion Defects Reported by Inline Inspection Tools

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
T. Siraj ◽  
W. Zhou
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Logistic Model ◽  
Oil And Gas ◽  
Metal Loss ◽  
Type I ◽  
Large Set ◽  
Type Ii ◽  
Oil And Gas Pipelines ◽  
Corrosion Defects

Abstract This paper proposes a framework to quantify the measurement error associated with lengths of corrosion defects on oil and gas pipelines reported by inline inspection (ILI) tools based on a relatively large set of ILI-reported and field-measured defect data collected from different in-service pipelines in Canada. A log-logistic model is proposed to quantify the likelihood of a given ILI-reported defect being a type I defect (without clustering error) or a type II defect (with clustering error). The measurement error associated with the ILI-reported length of the defect is quantified as the average of those associated with the types I and II defects, weighted by the corresponding probabilities obtained from the log-logistic model. The implications of the proposed framework for the reliability analysis of corroded pipelines given the ILI information are investigated using a realistic pipeline example.

scholarly journals Comparison of videolaryngoscope & direct laryngoscope for intubation in critically ill patients: A Bayesian meta-analysis

2020 ◽  
Author(s):  
Souvik Maitra ◽  
Anirban Som ◽  
Sulagna Bhattacharjee
Keyword(s):  
Bayesian Model ◽  
Observational Studies ◽  
Meta Analysis ◽  
Credible Interval ◽  
Video Laryngoscope ◽  
Bayesian Methodology ◽  
Direct Laryngoscope ◽  
Randomized Controlled ◽  
Intubation Success ◽  
Intubation Success Rate

AbstractPurposeTo identify the benefit of video laryngoscope (VL) over direct laryngoscope (DL) for intubation in the intensive care unit (ICU)Material & MethodsRandomized controlled trials (RCTs) comparing VL with DL for intubation in ICU by was conducted in conventional frequentist methodology and also incorporated of the previous evidences from observational studies in Bayesian methodology.ResultsData of 1464 patients from six RCTs have been included in this meta-analysis. In conventional meta-analysis of RCTs, first attempt intubation success rate was similar between VL and DL group [p=0.39]. Rate of esophageal intubation was significantly less with VL [p=0.03] and glottic visualization was significantly improved with VL in comparison to DL [p=0.009]. Time to intubation was similar in both the group [p=0.48]. When evidences from a meta-analysis of observational studies incorporated in Bayesian model, first attempt intubation success is significantly higher with VL [posterior median log OR (95% credible interval) 0.50 (0.06, 1.00)].ConclusionEvidences from both observational studies and RCTs synthesized in Bayesian methodology suggest that use of VL for endotracheal intubation in critically patients may be associated with higher first intubation success when compared to DL.

Study on the Engine Speed Sensor Position Measuring Tool Based on Minitab

2013 ◽  
Vol 456 ◽  
pp. 211-215
Author(s):  
Wu Cen ◽  
Li Xia Xia
Keyword(s):  
Measurement Accuracy ◽  
Engine Speed ◽  
Measurement Data ◽  
Measuring Method ◽  
Speed Sensor ◽  
Sensor Position ◽  
Accuracy Of Measurement ◽  
Repeatability And Reproducibility ◽  
Measuring Tool ◽  
Engine Size

Research on key position speed sensor of automobile engine size measuring tool as an example, for studying the optimization of measuring tools through use the powerful Minitab software statistics, analysis and calculation functions. Randomly selected 10 parts as a measurement sample, then obtained the measurement data by experiment. Analysis of the measured data with Minitab and calculate the repeatability and reproducibility of the device. Find the causes of failure in measuring and explore how to optimize the measuring method. Improved measuring clamp structure, and through experiments verify its feasibility. Thereby solving the problem of measuring failure to ensure the accuracy of measurement data, and improve the measurement accuracy of the product.

Bayesian Estimation of Soil-Water Characteristic Curves

2021 ◽  
Author(s):  
Jie Zhang ◽  
Shuai Yang ◽  
Lulu Zhang ◽  
Mingliang Zhou
Keyword(s):  
Soil Water ◽  
Bayesian Model ◽  
Characteristic Curve ◽  
Measurement Data ◽  
Bayes Theorem ◽  
Empirical Method ◽  
Measured Data ◽  
Suggested Method ◽  
Empirical Methods ◽  
Soil Water Characteristic Curves

The soil-water characteristic curve (SWCC) is a significant prerequisite for studying the mechanical properties of unsaturated soil. As experimental measurement of the SWCC is time-consuming, empirical methods have been suggested to estimate the SWCC. However, the uncertainty associated with SWCC can be substantial. In this paper, a hybrid method based on Bayes’ theorem is suggested to estimate the SWCC, where an empirical method can be used to provide prior knowledge about the SWCC, and a limited quantity of measured data is used to update the SWCC. The Bayesian model is then solved with a Markov Chain Monte Carlo simulation. Through the suggested method, the valuable information provided by the empirical method can be combined with the measurement data. The suggested method can not only provide the best estimate about the SWCC, but also account for the associated uncertainty. Also, the effect of more measured points on the estimation of SWCC can be quantified. The suggested method provides a practical means to estimate the SWCC using a limited amount of data.

Electrical Characterization on a High-Speed Wafer-Level Package

2013 ◽  
Vol 2013 (DPC) ◽  
pp. 001937-001962
Author(s):  
Kai Liu ◽  
YongTaek Lee ◽  
HyunTai Kim ◽  
MaPhooPwint Hlaing ◽  
Susan Park ◽  
...  
Keyword(s):  
High Speed ◽  
Unit Length ◽  
Ground Plane ◽  
Measurement Data ◽  
Electrical Characterization ◽  
Electrical Performance ◽  
Metal Loss ◽  
Signal Loss ◽  
Wafer Level ◽  
Wafer Level Package

In this paper, a 2-layer eWLB (embedded Wafer-Level BGA) is studied and its performance is compared with an equivalent 4-layer laminate package. Since eWLB package is processed by using lithographical method, design rules on width (W) and spacing (S) are much finer (usually 2–3 times finer) than those for laminate package. In other words, signal traces can be implemented in smaller fan-out regions. The smaller feature sizes for signal traces would end up with more metal loss per unit length. But as the signal traces can be implemented in smaller fan-out regions, overall trace-routing may be shorter, and equivalent insertion-loss may be achieved. In eWLB, the ground plane is closer to the signal traces. This actually helps to reduce cross-talk between wide I/O buses, as the electrical field is contained in a smaller region by the closer ground plane. Another key advantage from wafer-level package is a smoother metal surface, which greatly reduces the extra signal loss, due to surface-roughness effect, especially for higher-frequency and higher-speed applications. In addition, through-via structures for wafer-level package are typically 2–3 times smaller. This allows to implement power/ground planes in a more continuous way, achieving better resistance and inductance for power/ground nets. Overall electrical performance, which takes into account of all the impacts above, can be evaluated by signal-integrity analysis (E-diagram). Measurement data of a 2-layer eWLB package for a LPDDR application will be presented, which shows the comparable performance typically obtained from a 4-layer laminate package

scholarly journals Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 890
Author(s):  
Sergey Oladyshkin ◽  
Farid Mohammadi ◽  
Ilja Kroeker ◽  
Wolfgang Nowak
Keyword(s):  
Bayesian Inference ◽  
Active Learning ◽  
Gaussian Process ◽  
Relative Entropy ◽  
Bayesian Model ◽  
Information Gain ◽  
Measurement Data ◽  
Superior Performance ◽  
Evidence Based ◽  
Model Evidence

Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training runs should be well invested. The current paper offers a fully Bayesian view on GPEs for Bayesian inference accompanied by Bayesian active learning (BAL). We introduce three BAL strategies that adaptively identify training sets for the GPE using information-theoretic arguments. The first strategy relies on Bayesian model evidence that indicates the GPE’s quality of matching the measurement data, the second strategy is based on relative entropy that indicates the relative information gain for the GPE, and the third is founded on information entropy that indicates the missing information in the GPE. We illustrate the performance of our three strategies using analytical- and carbon-dioxide benchmarks. The paper shows evidence of convergence against a reference solution and demonstrates quantification of post-calibration uncertainty by comparing the introduced three strategies. We conclude that Bayesian model evidence-based and relative entropy-based strategies outperform the entropy-based strategy because the latter can be misleading during the BAL. The relative entropy-based strategy demonstrates superior performance to the Bayesian model evidence-based strategy.

Assessment of Interacting Corrosion Defects in Thin-Walled Pipes

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Ahmed A. Soliman ◽  
Mohammad M. Megahed ◽  
Ch. A. Saleh ◽  
Mostafa Shazly
Keyword(s):  
Nonlinear Finite Element ◽  
Metal Loss ◽  
Test Results ◽  
Defect Depth ◽  
Element Analysis ◽  
Shell Elements ◽  
Codes Of Practice ◽  
Useful Life ◽  
Corrosion Defects ◽  
Interaction Rule

Abstract Corrosion in pipes is usually found in the form of closely spaced defects, which eventually reduce the pipe pressure carrying capacity and piping planned useful life. Codes and standards have been developed to evaluate the effect of such form of metal loss on the piping pressure carrying capacities. However, predictions of such codes are usually conservative, and hence, there is a need to assess their degree of conservatism. The present paper utilizes nonlinear finite element analysis (FEA) in estimating pressure carrying capacities of defective pipes, and hence provides an evaluation of codes degree of conservatism. Shell elements with reduced thickness at the corrosion defect are adopted and their accuracy is assessed by comparison with those of solid elements as well as experimental test results. The influence of defects interaction is investigated by considering two neighboring defects in an inclined direction to each other. The influence of inclination angle, inclined proximity distance between the two defects, and the defect depth to wall thickness ratio are investigated. Comparisons were made with predictions of codes of practice in all cases. Code predictions were found to be conservative compared to FEA results. Furthermore, the interaction rule embedded in the codes for checking for interaction leads to inaccurate predictions for closely spaced defects as it does not include the effect of defect depth.

Quantifying similarities between computational expensive reactive transport models

2020 ◽  
Author(s):  
Farid Mohammadi ◽  
Stefania Scheurer ◽  
Aline Schäfer Rodrigues Silva ◽  
Sergey Oladyshkin ◽  
Johannes Hommel ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Reactive Transport ◽  
Prediction Accuracy ◽  
Measurement Data ◽  
Approximation Error ◽  
Model Complexity ◽  
Observation Data ◽  
Calcite Precipitation ◽  
Biofilm Growth ◽  
Porosity And Permeability

<p><span>The microbially induced calcite precipitation (MICP) process is a reactive transport, which consists of various important biogeochemical processes, namely precipitation, and dissolution of calcite, adhesion of the biomass on surfaces, detachment of the biomass from the biofilm as well as growth and decay of the biomass. Due to the accumulation of the biofilm and especially the calcite precipitation, the flow conditions in the subsurface can be modified and especially the porosity and permeability can be reduced, so that the existing leakages are sealed. This sealing property of MICP is of interest in different applications, such as sealing cracks in gas tanks or in a cap rock for CO</span><sub><span>2</span></sub><span> underground storage</span></p><p><span>The process of biofilm growth in porous media using MICP can be described by many models with different complexity and assumptions. Typically, complex models require more measurement data to constrain their parameters. Therefore, there is a need to seek a balance between model complexity and efforts for acquiring field data. To do so, the modelers are interested in assessing the similarities among these models and their prediction accuracy by comparing them with field observation data. </span></p><p><span>In this study, we perform a Bayesian model legitimacy analysis to investigate the similarities among different MICP models and their prediction accuracy. Moreover, this analysis provides a model ranking based on computed model weights, achieved within the framework of Bayesian model selection (BMS). This framework requires many model evaluations, which makes the analysis intractable for computationally expensive MICP models. To overcome this issue, we use surrogate models that are constructed using arbitrary polynomial chaos expansion (aPCE). To account for the approximation error, we introduce a correction factor that compensates the inaccuracies due to replacing the original models by the surrogates. </span></p>

Hierarchical Bayesian Corrosion Growth Model Based on In-Line Inspection Data

2012 ◽  
Author(s):  
M. Al-Amin ◽  
W. Zhou ◽  
S. Zhang ◽  
S. Kariyawasam ◽  
H. Wang
Keyword(s):  
Growth Model ◽  
Power Law ◽  
Management Program ◽  
Metal Loss ◽  
Hierarchical Bayesian ◽  
Natural Gas Pipelines ◽  
Mcmc Simulation ◽  
Corrosion Defects ◽  
External Corrosion ◽  
Inspection Data

A hierarchical Bayesian growth model is presented in this paper to characterize and predict the growth of individual metal-loss corrosion defects on pipelines. The depth of the corrosion defects is assumed to be a power-law function of time characterized by two power-law coefficients and the corrosion initiation time, and the probabilistic characteristics of the parameters involved in the growth model are evaluated using Markov Chain Monte Carlo (MCMC) simulation technique based on ILI data collected at different times for a given pipeline. The model accounts for the constant and non-constant biases and random scattering errors of the ILI data, as well as the potential correlation between the random scattering errors associated with different ILI tools. The model is validated by comparing the predicted depths with the field-measured depths of two sets of external corrosion defects identified on two real natural gas pipelines. The results suggest that the growth model is able to predict the growth of active corrosion defects with a reasonable degree of accuracy. The developed model can facilitate the pipeline corrosion management program.

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