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Reliability analysis and anomaly detection considering long-range dependence effects
Shao, Yunfei
Shao, Yunfei
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2023-05
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Dissertation
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Abstract
Reliability analysis and anomaly detection are crucial to the Prognostics and Health
Management (PHM) of modern complex systems. Recently, with the advancement of
measurement technology, a Long-Range Dependence/Long-Term Memory (LRD/LTM) effect has
recently been detected in reliability and quality monitoring fields. The LRD effect is a type of non-
Markovian property and refers to the high dependence between two measurements across a longtime
interval or a long-distance range. In mathematics, the LRD effect indicates that the
autocorrelation of the metrics is non-summable. In reliability and anomaly detection fields, most
studies have been conducted ignoring the LRD effect, which could incur some serious issues, such
as biased lifetime prediction or inaccurate anomaly detection. To overcome these challenges, we
propose novel reliability analysis and anomaly detection approaches to integrate the LRD effect.
Specifically, in Chapter 2 we propose a reliability analysis considering the LRD effect and random
errors simultaneously under normal operating conditions. In Chapter 3, we develop a reliability
analysis integrating the LRD effect under accelerated conditions. In Chapter 4, we propose a
quality control analysis on surface anomaly detection considering LRD. In Chapter 5, we develop
an LRD-integrated anomaly detection using 3D composite structure information. Results show
that the proposed LRD-integrated approaches, by considering the LRD effect, significantly
outperforms the conventional models that ignore the LRD effect.
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Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of of Industrial, Systems and Manufacturing Engineering
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Wichita State University
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