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Reliability estimation and stochastic modeling of corrosion growth
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Wang, Changxi. Reliability estimation and stochastic modeling of corrosion growth. Retrieved from https://doi.org/doi:10.7282/t3-yfn7-w424

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TitleReliability estimation and stochastic modeling of corrosion growth
NameWang, Changxi (author); Elsayed, Elsayed A. (chair); Albin, Susan L. (internal member); Pham, Hoang (internal member); Cabrera, Javier (outside member); Rutgers University; School of Graduate Studies
Date Created2021
Other Date2021-01 (degree)
SubjectDegradation branching, Industrial and Systems Engineering
Extent1 online resource (xviii, 283 pages) : illustrations
DescriptionCorrosion and corrosion-related problems are the major factors leading to the age-related structural degradation of infrastructures such as pipelines and pressure vessels. Corrosion defects may result in severe damages such as thickness penetration, fatigue cracks, brittle fracture, rupture and burst. Quantifying the growth of corrosion is critically important for the risk and reliability analysis of structures, planning for corrosion mitigation, repairs and determination of time intervals for the corrosion inspections and monitoring.

Pitting corrosion growth has been a focus of research, and a depth threshold of corrosion defect has been widely used for estimating the residual life of structures being monitored. However, corrosion volume loss of materials may also lead to failures such as pipeline bursts, which is more harmful but overlooked. In this dissertation, we develop a degradation model that characterizes both corrosion maximum depth growth, corrosion volume growth and corrosion propagation. We propose an improved inverse Gaussian (IIG) process to model the corrosion depth growth and demonstrate that it captures the dependency between the corrosion growth rate and the corrosion depth. We develop a corrosion pit volume growth model assuming that both the corrosion pit growth in the depth directions and radial directions follow IIG processes. Compared with other existing corrosion models, the proposed model captures the phenomenon where a critical amount of volume loss of materials leads to the failure of a component even though the corrosion defect’s depth has not reached its failure threshold. A physics-based model that incorporates factors including the spatial and size distributions of the material particles and the influence of corroded pits is developed to capture the corrosion propagation.

Degradation branching stochastic models are developed to describe the corrosion pit propagation. They are general models and can be applied to cracks in materials and systems that consist of multiple units where the degradation of one unit may affect adjacent units and the failure occurs when the total degradation reaches an unacceptable amount. The models capture the phenomenon that a growing degradation branch may initiate new branches when a certain criterion is met, where the criterion may be a branch’s degradation amount threshold or other physical processes. The effect of the random branching angles and the random number of branches initiated in each branching on the total degradation is investigated, where the physics-based models are proposed to describe the relationship between the branching angles and the total degradation. The branching continues until the total degradation of all the branches reaches a threshold. Statistics of the degradation branching processes such as the mean and the variance of the total degradation, the expected number of branches initiated, the reliability and distribution of residual life are obtained.

The measurements of corrosion growth are continuously monitored and recorded. The rapid development of sensing and computing technologies has enabled the use of multiple sensors to monitor the degradation indicator (or its surrogate) of a component simultaneously. However, there are challenges in integrating the measurements from multiple sensors. First, missing data arises due to data transmission failures and manipulation errors. We propose a variety of stochastic bridges to deal with the missing data. Second, different sensors may capture different aspects of the degradation process and may be sensitive in different stages of the degradation process. We propose a non-parametric model that assigns a sensor’s weight (contribution) based on its performance in the previous time instants. It utilizes a moving time window to determine the switching of the sensors between clusters with time so that the weights are adjusted accordingly. The advantage of the proposed approach is that no specific distribution of degradation data or underlying degradation models are required.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-yfn7-w424
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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