Name
Title | ||
|---|---|---|
Characterization of System Status Signals for Multivariate Time Series Discretization Based on Frequency and Amplitude Variation. |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18010154 | SENSORS |
Keywords | Field | DocType |
fault detection,sensor data,frequency domain | Frequency domain,Discretization,Pattern recognition,Fault detection and isolation,Multivariate statistics,Industrial systems,Electronic engineering,Artificial intelligence,Engineering,Amplitude | Journal |
Volume | Issue | ISSN |
18 | 1.0 | 1424-8220 |
Citations | PageRank | References |
2 | 0.36 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Woonsang Baek | 1 | 2 | 0.36 |
| Sujeong Baek | 2 | 5 | 1.77 |
| Duck Young Kim | 3 | 7 | 3.83 |