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Updated: Feb 15, 2026

Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Characterization of System Status Signals for Multivariate Time Series Discretization Based on Frequency and
Woonsang Baek1, Sujeong Baek2, Duck Young Kim3
1Department of System Design and Control Engineering, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan 44919, Korea. wsbaek@unist.ac.kr.
Abstract:
Many fault detection methods have been proposed for monitoring the health of various industrial systems. Characterizing the monitored signals is a prerequisite for selecting an appropriate detection method. However, fault detection methods tend to be decided with user's subjective knowledge or their familiarity with the method, rather than following a predefined selection rule. This study investigates the performance sensitivity of two detection methods, with respect to status signal characteristics of given systems: abrupt variance, characteristic indicator, discernable frequency, and discernable index. Relation between key characteristics indicators from four different real-world systems and the performance of two fault detection methods using pattern recognition are evaluated.
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