Saddlepoint approximation for the generalized inverse Gaussian Lévy process

The generalized inverse Gaussian (GIG) Lévy process is a limit of compound Poisson processes, including the stationary gamma process and the stationary inverse Gaussian process as special cases. However, fitting the GIG Lévy process to data is computationally intractable due to the fact that the marginal distribution of the GIG Lévy process is not convolution-closed. The current work reveals that the marginal distribution of the GIG Lévy process admits a simple yet extremely accurate saddlepoint approximation. Particularly, we prove that if the order parameter of the GIG distribution is greater than or equal to −1, the marginal distribution can be approximated accurately — no need to normalize the saddlepoint density. Accordingly, maximum likelihood estimation is simple and quick, random number generation from the marginal distribution is straightforward by using Monte Carlo methods, and goodness-of-fit testing is undemanding to perform. Therefore, major numerical impediments to the application of the GIG Lévy process are removed. We demonstrate the accuracy of the saddlepoint approximation via various experimental setups

Application of Bayesian Networks to Integrity Management of Energy Pipelines

Metal-loss corrosion and third-party damage (TPD) are the leading threats to the integrity of buried oil and natural gas pipelines. This thesis employs Bayesian networks (BNs) and non-parametric Bayesian networks (NPBNs) to deal with four issues with regard to the reliability-based management program of corrosion and TPD. The first study integrates the quantification of measurement errors of the ILI tools, corrosion growth modeling and reliability analysis in a single dynamic Bayesian network (DBN) model, and employs the parameter learning technique to learn the parameters of the DBN model from the ILI-reported and filed-measured corrosion depths. The second study develops the BN model to estimate the probability of a given pipeline being hit by third-party excavations by taking into account common preventative and protective measures. The parameter learning technique is employed to learn the parameters of the BN model from datasets that consist of individual cases of third-party activities. The ILIs are infeasible for a portion of buried pipelines due to various reasons, which are known as unpiggable pipelines. To assist with the corrosion assessment for the unpiggable pipelines, the third study develops a non-parametric Bayesian network (NPBN) model to predict the corrosion depth on buried pipelines using the pipeline age and local soil properties as the predictors. The last study develops an optimal sample size determination method for collecting samples to reduce the epistemic uncertainties in the probabilistic distributions of basic random variables in the reliability analysis of corroded pipelines

Application of Wavelet Analysis and Random Field in Integrity Management of Pipelines Containing Dents and Corrosions

Metal loss corrosions and dents are two major threats to the integrity of oil and natural gas pipelines. In the pipeline industry, the Fitness-For-Service (FFS) assessment is commonly employed for pipelines containing these defects. However, FFS assessment usually assumes that a defect has a simple shape, and such a simplification may significantly affect the accuracy of the assessment. Therefore, retaining the actual shapes of defects and incorporating them into the FFS assessment can improve assessment accuracy. The main objective of the present thesis is to extract key information about the sizes, directions, and shapes of corrosions and dents from the measurement of in-service and excavated pipelines, and then improve the accuracy of FFS assessment based on the extracted information. The first study develops a wavelet transform-based denoising method for the measured inner surface of in-service dented pipelines obtained from caliper tools. Since the inner surface is differently sampled along the longitudinal and circumferential directions, the commonly used denoising methods cannot sufficiently remove measurement errors from the signal. The proposed method is based on overcomplete expansion, and the overcomplete dictionary is constructed from the hyperbolic wavelet transform and stationary transform. The strain estimated from the signal denoised by the proposed method is closer to the actual strain than the other denoising method. An overcomplete dictionary that can effectively denoise the dent signal is then constructed based on the statistics of the measurement of in-service dented pipelines. The second study explores the vital directional features and length scales of natural corrosion clusters that govern the burst capacity of corroded pipelines. The corrosion depths in a cluster are measured by high-resolution laser scans, and two-dimensional (2D) discrete wavelet transform (DWT) with a suitable wavelet function is employed to decompose the corrosion cluster. A methodology is proposed to determine level- and sub-band-dependent thresholds such that those wavelet coefficients below the thresholds have a negligible impact on the burst capacity predicted by the widely used RSTRENG model and can be ignored for the reconstruction of the cluster. The preserved wavelet coefficients show that longitudinally orientated features with 4 – 32 mm in length have a greater influence on the remaining burst capacity than other features. This facilitates FFS assessment of corroded pipelines. The third study aims to simulate the corrosion fields whose morphology and marginal distribution are close to the actual corrosion fields from limited information summarized from the ILI data. The corrosion field containing multiple corrosion anomalies is modelled as a nonhomogeneous non-Gaussian random field, where the spatial correlation and marginal distribution of anomalies are estimated from their sizes. The proposed methodology provides realizations of corrosion fields with the RSTRENG-predicted burst capacity closer to the actual burst capacity than the commonly used methodology that idealizes anomalies as cuboids. The fourth study presents a framework to analyze and simulate nonhomogeneous non-Gaussian corrosion fields on the external surface of buried in-service pipelines by using continuous and discrete wavelet transforms. Continuous wavelet transform (CWT), dual-tree complex discrete wavelet transform (DT-CDWT), and dual-tree complex discrete wavelet with hyperbolic wavelet transform scheme (DT-CHWT) are incorporated into the iterative power and amplitude correction (IPAC) algorithm to extract the features of the natural corrosion field measured by a high-resolution laser scan and generate synthetic corrosion fields. The results indicate that the proposed framework can generate synthetic corrosion fields that effectively capture probabilistic characteristics of the measured corrosion field in terms of the scalogram, textural features, and burst capacity of the pipe segment containing the corrosion field

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen