Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Ankit, Kumar
- Creators: Ju, Feng
Description
Sequential event prediction or sequential pattern mining is a well-studied topic in the literature. There are a lot of real-world scenarios where the data is released sequentially. People believe that there exist repetitive patterns of event sequences so that the future events can be predicted. For example, many companies build their recommender system to predict the next possible product for the users according to their purchase history. The healthcare system discovers the relationships among patients’ sequential symptoms to mitigate the adverse effect of a treatment (drugs or surgery). Modern engineering systems like aviation/distributed computing/energy systems diagnosed failure event logs and took prompt actions to avoid disaster when a similar failure pattern occurs. In this dissertation, I specifically focus on building a scalable algorithm for event prediction and extraction in the aviation domain. Understanding the accident event is always the major concern of the safety issue in the aviation system. A flight accident is often caused by a sequence of failure events. Accurate modeling of the failure event sequence and how it leads to the final accident is important for aviation safety. This work aims to study the relationship of the failure event sequence and evaluate the risk of the final accident according to these failure events. There are three major challenges I am trying to deal with. (1) Modeling Sequential Events with Hierarchical Structure: I aim to improve the prediction accuracy by taking advantage of the multi-level or hierarchical representation of these rare events. Specifically, I proposed to build a sequential Encoder-Decoder framework with a hierarchical embedding representation of the events. (2) Lack of high-quality and consistent event log data: In order to acquire more accurate event data from aviation accident reports, I convert the problem into a multi-label classification. An attention-based Bidirectional Encoder Representations from Transformers model is developed to achieve good performance and interpretability. (3) Ontology-based event extraction: In order to extract detailed events, I proposed to solve the problem as a hierarchical classification task. I improve the model performance by incorporating event ontology. By solving these three challenges, I provide a framework to extract events from narrative reports and estimate the risk level of aviation accidents through event sequence modeling.
ContributorsZhao, Xinyu (Author) / Yan, Hao (Thesis advisor) / Liu, Yongming (Committee member) / Ju, Feng (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2022
Description
AA 7XXX alloys are used extensively in aircraft and naval structures due to their excellent strength to weight ratio. These alloys are often exposed to harsh corrosive environments and mechanical stresses that can compromise their reliability in service. They are also coupled with fasteners that are composed of different materials such as Titanium alloys. Such dissimilar metal contact facilitates galvanic and crevice corrosion, which can further reduce their lifetimes. Despite decades of research in the area, the confluence of mechanical, microstructural, and electrochemical aspects of damage is still unclear. Traditionally, 2D and destructive methods have often been employed to study the corrosion and cracking behavior in these systems which can be severely limiting and lead to inaccurate conclusions. This dissertation is aimed at comprehensively studying the corrosion and cracking behavior of these systems using time-dependent 3D microstructural characterization, as well as correlative microscopy. The microstructural evolution of corrosion in AA 7075 was studied using a combination of potentiodynamic polarization, X-ray Computed Tomography (XCT) and Transmission X-ray Microscopy (TXM). In both experiments, a strong emphasis was placed on studying localized corrosion attack at constituent particles and intergranular corrosion. With an understanding of the alloy’s corrosion behavior, a dissimilar alloy couple comprising AA 7075 / Ti-6Al-4V was then investigated. Ex situ and in situ x-ray microtomography was used extensively to investigate the evolution of pitting corrosion and corrosion fatigue in AA 7075 plates fastened separately with Ti-6Al-4V screws and rivets. The 4D tomography combined with the extensive fractography yielded valuable information pertaining the preferred sites of pit initiation, crack initiation and growth in these complex geometries. The use of correlative microscopy-based methodologies yielded multimodal characterization results that provided a unique and seminal insight on corrosion mechanisms in these materials.
ContributorsNiverty, Sridhar (Author) / Chawla, Nikhilesh (Thesis advisor) / Liu, Yongming (Committee member) / Ankit, Kumar (Committee member) / Xiao, Xianghui (Committee member) / Arizona State University (Publisher)
Created2020