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Sensors 2018, 18(1), 154; https://doi.org/10.3390/s18010154

Characterization of System Status Signals for Multivariate Time Series Discretization Based on Frequency and Amplitude Variation

Department of System Design and Control Engineering, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan 44919, Korea
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Received: 4 December 2017 / Revised: 29 December 2017 / Accepted: 5 January 2018 / Published: 8 January 2018
(This article belongs to the Special Issue Sensors for Fault Detection)
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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. View Full-Text
Keywords: fault detection; sensor data; frequency domain fault detection; sensor data; frequency domain
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Baek, W.; Baek, S.; Kim, D.Y. Characterization of System Status Signals for Multivariate Time Series Discretization Based on Frequency and Amplitude Variation. Sensors 2018, 18, 154.

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