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Reliability estimation and stochastic modeling of corrosion growth

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TitleInfo
Title
Reliability estimation and stochastic modeling of corrosion growth
Name (type = personal)
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Wang
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Changxi
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Changxi Wang
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author
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Elsayed A. Elsayed
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chair
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Albin
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Susan L.
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Susan L. Albin
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Advisory Committee
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internal member
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Pham
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Hoang
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Hoang Pham
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Advisory Committee
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internal member
Name (type = personal)
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Cabrera
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Javier
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Javier Cabrera
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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School of Graduate Studies
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school
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Text
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theses
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ETD doctoral
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2021
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2021-01
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2021
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English
Abstract (type = abstract)
Corrosion 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.
Subject (authority = local)
Topic
Degradation branching
Subject (authority = RUETD)
Topic
Industrial and Systems Engineering
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_11288
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1 online resource (xviii, 283 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-yfn7-w424
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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Wang
GivenName
Changxi
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Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2020-10-06 16:17:06
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Name
Changxi Wang
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Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Open
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