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Feature and Statistical Model Development in Structural Health Monitoring

Abstract All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development.

The first part of this dissertation, the char... (more)
Created Date 2016
Contributor Kim, Inho (Author) / Chattopadhyay, Aditi (Advisor) / Jiang, Hanqing (Committee member) / Liu, Yongming (Committee member) / Mignolet, Marc (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Subject Mechanical engineering / Aerospace engineering / Civil engineering / Machine learning / Optimization / Structural Health Monitoring / Wave propagation
Type Doctoral Dissertation
Extent 179 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Mechanical Engineering 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis