Data-driven fault detection in a reusable rocket engine using bivariate time-series analysis

2021 ◽  
Vol 179 ◽  
pp. 685-694
Author(s):  
Seiji Tsutsumi ◽  
Miki Hirabayashi ◽  
Daiwa Sato ◽  
Kaname Kawatsu ◽  
Masaki Sato ◽  
...  
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Time Series Analysis ◽  
Rocket Engine ◽  
Data Driven ◽  
Series Analysis ◽  
Bivariate Time Series

Data-driven robot gait modeling via symbolic time series analysis

2016 ◽  
Author(s):  
Yusuke Seto ◽  
Noboru Takahashi ◽  
Devesh K. Jha ◽  
Nurali Virani ◽  
Asok Ray
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Data Driven ◽  
Series Analysis ◽  
Symbolic Time Series Analysis

scholarly journals Study on Time Series Analysis of Some Hydrological Quantities. (2). The Proposal of the Bivariate Time Series Analysis.

2002 ◽  
Vol 15 (1) ◽  
pp. 23-38
Author(s):  
Mitsuya ENOKIDA ◽  
Mututoshi FUKUDA ◽  
Hiroshi SHIMIZU ◽  
Risaku FUKUI ◽  
Hitoshi ICHIKAWA ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis ◽  
Bivariate Time Series

scholarly journals Wavelet Cross-correlation in Bivariate Time-Series Analysis

2018 ◽  
Vol 19 (3) ◽  
pp. 391
Author(s):  
Eniuce Menezes de Souza ◽  
Vinícius Basseto Félix
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Multiple Scales ◽  
Statistical Significance ◽  
Dependent Data ◽  
Data Sets ◽  
Translation Invariant ◽  
Series Analysis ◽  
Bivariate Time Series

The estimation of the correlation between independent data sets using classical estimators, such as the Pearson coefficient, is well established in the literature. However, such estimators are inadequate for analyzing the correlation among dependent data. There are several types of dependence, the most common being the serial (temporal) and spatial dependence, which are inherent to the data sets from several fields. Using a bivariate time-series analysis, the relation between two series can be assessed. Further, as one time series may be related to an other with a time offset (either to the past or to the future), it is essential to also consider lagged correlations. The cross-correlation function (CCF), which assumes that each series has a normal distribution and is not autocorrelated, is used frequently. However, even when a time series is normally distributed, the autocorrelation is still inherent to one or both time series, compromising the estimates obtained using the CCF and their interpretations. To address this issue, analysis using the wavelet cross-correlation (WCC) has been proposed. WCC is based on the non-decimated wavelet transform (NDWT), which is translation invariant and decomposes dependent data into multiple scales, each representing the behavior of a different frequency band. To demonstrate the applicability of this method, we analyze simulated and real time series from different stochastic processes. The results demonstrated that analyses based on the CCF can be misleading; however, WCC can be used to correctly identify the correlation on each scale. Furthermore, the confidence interval (CI) for the results of the WCC analysis was estimated to determine the statistical significance.

scholarly journals svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis

2021 ◽  
Vol 97 (5) ◽  
Author(s):  
Alexander Lange ◽  
Bernhard Dalheimer ◽  
Helmut Herwartz ◽  
Simone Maxand
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Multivariate Time Series ◽  
R Package ◽  
Data Driven ◽  
Series Analysis ◽  
Multivariate Time Series Analysis

Minor fault detection of thermocouple sensor in nuclear power plants using time series analysis

2019 ◽  
Vol 134 ◽  
pp. 383-389 ◽  
Author(s):  
Shyamapada Mandal ◽  
B. Santhi ◽  
S. Sridhar ◽  
K. Vinolia ◽  
P. Swaminathan
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Time Series Analysis ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Series Analysis
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