Hydraulic Actuator Internal Leakage Detection Using Cross-Correlation Time Series Analysis

2014 ◽  
Author(s):  
Morgan May ◽  
Nariman Sepehri ◽  
Witold Kinsner
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Correlation Time ◽  
Cross Correlation ◽  
Dynamic Performance ◽  
Cylinder Wall ◽  
Hydraulic Actuator ◽  
Series Analysis ◽  
Piston Seal ◽  
The Cross

As a hydraulic actuator is used over time, the piston seal that closes the gap between the moveable piston and the cylinder wall wears. This results in the fluid to eventually being able to flow across the piston and between the cylinder chambers. Although no fluid is lost from the circuit, the dynamic performance of the system is affected since only a portion of the flow delivered to the actuator is available to move the piston against the load. Leakage across the piston introduces a flow deadband, which can lead to servoing errors. This paper presents an approach based on the cross-correlation time-series analysis to determine the health of hydraulic actuator internal seals. Pressure signals obtained directly from routine operations are analyzed using the cross-correlation function. The method presented in this paper is shown to be capable of detecting leakages as small as 0.069 l/min.

Provincial Level Time Series Analysis : Community Mobility and COVID-19 Daily Cases in Turin, Italy (Preprint)

2021 ◽  
Author(s):  
Dayana Benny
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Provincial Level ◽  
Epidemic Curve ◽  
The Past ◽  
Series Analysis ◽  
Community Mobility ◽  
Cross Correlation Analysis ◽  
Restrictive Measures

BACKGROUND Turin, a province in the Piedmont region sees second highest new COVID-19 infections in Northern part of Italy as of March 31, 2021. During the first wave of pandemic, many restrictive measures were introduced in this province. There are many studies that conducted time series analysis of various regions in Italy, but studies that are analysing the data in province level are limited. Also, no applications of Cross Correlation Function(CCF) have been proposed to analyse relationships between COVID-19 new cases and community mobility at the provincial level in Italy. OBJECTIVE The goal of this time series analysis is to find how the restrictive measures in Turin province, Italy impacted community mobility and helped in flattening the epidemic curve during the first wave of the pandemic. METHODS A simple time series analysis is conducted in this study to analyse whether there is an association between COVID-19 daily cases and community mobility. In this study, we analysed whether the time series of the parameter that estimates the reproduction of infection in the outbreak is related to the past lags of community mobility time series by performing cross-correlation analysis. RESULTS Multiple regression is carried out in which the R0 variable is a linear function of past lags 6, 7, 8, and 1 of the community mobility variable and all coefficients are statistically significant where P = 0.024043, 2.69e-05, 0.045350 and 0.000117 respectively. The cross-correlation between data fitted from the significant past lags of community mobility and transformed basic reproduction number (R0) time-series is obtained in such a manner that the R0 of a day is related to the past lags of community mobility in Turin province. CONCLUSIONS Our analysis shows that the restrictive measures are having an impact on community mobility during the first wave of COVID-19 and it can be related to the reported secondary cases of COVID-19 in Turin province at that time. Through further improvement, this simple model could serve as preliminary research for developing right preventive methods during the early stages of an epidemic.

Provincial Level Time Series Analysis : Community Mobility and COVID-19 Daily Cases in Turin, Italy (Preprint)

2021 ◽  
Author(s):  
Dayana Benny
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Provincial Level ◽  
Epidemic Curve ◽  
The Past ◽  
Series Analysis ◽  
Community Mobility ◽  
Cross Correlation Analysis ◽  
Restrictive Measures

BACKGROUND Turin, a province in the Piedmont region sees second highest new COVID-19 infections in Northern part of Italy as of March 31, 2021. During the first wave of pandemic, many restrictive measures were introduced in this province. There are many studies that conducted time series analysis of various regions in Italy, but studies that are analysing the data in province level are limited. Also, no applications of Cross Correlation Function(CCF) have been proposed to analyse relationships between COVID-19 new cases and community mobility at the provincial level in Italy. OBJECTIVE The goal of this time series analysis is to find how the restrictive measures in Turin province, Italy impacted community mobility and helped in flattening the epidemic curve during the first wave of the pandemic. METHODS A simple time series analysis is conducted in this study to analyse whether there is an association between COVID-19 daily cases and community mobility. In this study, we analysed whether the time series of the parameter that estimates the reproduction of infection in the outbreak is related to the past lags of community mobility time series by performing cross-correlation analysis. RESULTS Multiple regression is carried out in which the R0 variable is a linear function of past lags 6, 7, 8, and 1 of the community mobility variable and all coefficients are statistically significant where P = 0.024043, 2.69e-05, 0.045350 and 0.000117 respectively. The cross-correlation between data fitted from the significant past lags of community mobility and transformed basic reproduction number (R0) time-series is obtained in such a manner that the R0 of a day is related to the past lags of community mobility in Turin province. CONCLUSIONS Our analysis shows that the restrictive measures are having an impact on community mobility during the first wave of COVID-19 and it can be related to the reported secondary cases of COVID-19 in Turin province at that time. Through further improvement, this simple model could serve as preliminary research for developing right preventive methods during the early stages of an epidemic.

scholarly journals APLIKASI TIME SERIES ANALYSIS UNTUK KARAKTERISASI AKUIFER KARST

2018 ◽  
Author(s):  
Afid Nurkholis ◽  
Tjahyo Nugroho Adji ◽  
Ahmad Cahyadi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spectral Density ◽  
Phase Function ◽  
Cross Correlation ◽  
Black Box ◽  
Gain Function ◽  
Coherency Function ◽  
Series Analysis

Time series analysis merupakan suatu analisis statistik yang mencerminkan respons sistem karst terhadap curah hujan. Konsep dari metode ini adalah menganggap sistem akuifer karst sebagai black box yang tidak diketahui kinerjanya. Time series analysis menggunakan perhitungan statistik berupa univariate (autocorrelation) dan bivariate (cross-correlation). Kedua metode menganalisis data berdasarkan waktu dan dapat diubah bentuk menjadi analisis frekuensi. Autocorrellation dapat diubah menjadi spectral density. Cross-correlation dapat diubah menjadi cross-amplitude, phase function, coherency function, dan gain function. Tulisan ini akan menjelaskan langkah-langkah perhitungan seluruh metode time series analysis tersebut untuk melakukan karakterisasi akuifer karst. Data yang digunakanadalah pasangan debit aliran dan curah hujan selama 6 bulan (1 Januari 2017 – 30 Juni 2017). Kedua data dicatat pada Gua Pindul (sebagai outlet sistem akuifer karst)dan Sinking Stream Kedungbuntung. Hasil perhitungan menunjukkan bahwa time series analysis dapat diklasifikan menjadi pelepasan aliran conduit, fissure, dandiffuse.

Error Bounds for Estimates of Entrained Ichthyoplankton

1983 ◽  
Vol 40 (1) ◽  
pp. 10-16 ◽  
Author(s):  
C. P. Madenjian ◽  
D. J. Jude
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Error Bound ◽  
Error Bounds ◽  
Correlation Time ◽  
Serial Correlation ◽  
Lake Michigan ◽  
Fish Larvae ◽  
Independent Component ◽  
Series Analysis

Entrainment of fish larvae and eggs was monitored at the J. H. Campbell Plant, eastern Lake Michigan, from 1977 to 1979. A procedure for calculating error bounds for estimated number of fish larvae (or eggs) entrained by the plant for each year of operation, assuming independence among observations, was outlined. A new method for calculating these bounds was devised by adjusting the variance for its non-independent component, using time series analysis. Serial correlation in the data was accounted for by modeling the sequence of entrainment data for a year as a time series. For many taxa of fish larvae the entrainment observations were determined to be independent and no adjustment was necessary. The width of the adjusted error-bound intervals ranged from 4.8 to 87.9% greater than unadjusted ones for those taxa for which adjustment was required, thus assuming independence among observations could result in serious underestimation of the error-bound interval.Key words: error bounds, variance, entrainment, Lake Michigan, serial correlation, time series analysis

scholarly journals Performance Consistency of International Soccer Teams in Euro 2012: a Time Series Analysis

2013 ◽  
Vol 38 ◽  
pp. 213-226 ◽  
Author(s):  
Mohsen Shafizadeh ◽  
Marc Taylor ◽  
Carlos Lago Peñas
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Performance Indicators ◽  
Final Stage ◽  
Cross Correlation ◽  
Positive Correlation ◽  
Series Analysis ◽  
Cross Correlation Analysis ◽  
Soccer Teams ◽  
Consistent Performance

Abstract The purpose of this study was to examine the consistency of performance in successive matches for international soccer teams from Europe which qualified for the quarter final stage of EURO 2012 in Poland and Ukraine. The eight teams that reached the quarter final stage and beyond were the sample teams for this time series analysis. The autocorrelation and cross-correlation functions were used to analyze the consistency of play and its association with the result of match in sixteen performance indicators of each team. The results of autocorrelation function showed that based on the number of consistent performance indicators, Spain and Italy demonstrated more consistency in successive matches in relation to other teams. This appears intuitive given that Spain played Italy in the final. However, it is arguable that other teams played at a higher performance levels at various parts of the competition, as opposed to performing consistently throughout the tournament. The results of the cross-correlation analysis showed that in relation to goal-related indicators, these had higher associations with the match results of Spain and France. In relation to the offensive-related indicators, France, England, Portugal, Greece, Czech Republic and Spain showed a positive correlation with the match result. In relation to the defensive-related indicators, France, England, Greece and Portugal showed a positive correlation with match results. In conclusion, in an international soccer tournament, the successful teams displayed a greater degree of performance consistency across all indicators in comparison to their competitors who occasionally would show higher levels of performance in individual games, yet not consistently across the overall tournament. The authors therefore conclude that performance consistency is more significant in international tournament soccer, versus occasionally excelling in some metrics and indicators in particular games.

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 Impact of the Ebola virus disease outbreak (2014-2016) on Tuberculosis Surveillance Activities in Guinea's National Tuberculosis Program: A Time Series Analysis

2019 ◽  
Author(s):  
Magassouba Aboubacar Sidiki ◽  
Boubacar Djelo Diallo ◽  
Lansana Mady Camara ◽  
Kadiatou Sow ◽  
Souleymane Camara ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surveillance System ◽  
Cross Correlation ◽  
Ebola Virus ◽  
Virus Disease ◽  
Disease Outbreak ◽  
Ebola Virus Disease ◽  
World Health ◽  
Series Analysis

Abstract Background Tuberculosis (TB) is a major cause of disease and death worldwide. According to estimates published by WHO, Guinea is one of the countries with a high incidence of tuberculosis and tuberculosis / HIV co-infection. In March 2014, the World Health Organization (WHO) announced the Ebola virus disease outbreak in Guinea that caused a health system disruption. Our study aimed to assess the impact of the Ebola virus disease outbreak on the TB surveillance system through the main indicators of TB-related morbidity and mortality.Methods This is a retrospective cohort study by comparing TB trends through TB surveillance data from periods before (2011-2013), during (2014-2015) and after (2016-2018) the Ebola virus disease outbreak. A time-series analysis was conducted to investigate the link between the decrease in TB incidence and the Ebola virus disease through cross-correlation. We evaluated the surveillance system to compare its current status with that of the Ebola virus disease outbreak period.Results The reporting rate for TB cases has decreased from 120 cases per 100,000 people reported in 2011 to 100 cases in 2014. The cross-correlation test between TB and the Ebola virus disease incidents shows a significant lag of -0.6 (60%) this corresponds to the drop in TB incidence observed when Ebola virus disease was at its peak in 2014. Concerning the surveillance system, of the 13 standards, only five are reached in 2019 compared to 3 in 2015.Conclusion The Ebola virus disease outbreak has had a severe impact on TB surveillance in Guinea. The introduction of an early warning system would preserve the TB surveillance system; which could encourage the implementation of set stakes to ensure access to diagnosis, treatment for enhanced surveillance of tuberculosis.

scholarly journals A Time-Series Analysis of Clostridium difficile and Its Seasonal Association with Influenza

2010 ◽  
Vol 31 (4) ◽  
pp. 382-387 ◽  
Author(s):  
Philip M. Polgreen ◽  
Ming Yang ◽  
Lucas C. Bohnett ◽  
Joseph E. Cavanaugh
Keyword(s):  
Time Series ◽  
Clostridium Difficile ◽  
Time Series Analysis ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Linear Trend ◽  
International Classification Of Diseases ◽  
Series Data ◽  
Series Analysis ◽  
Classification Of Diseases

Objective.To characterize the temporal progression of the monthly incidence of Clostridium difficile infections (CDIs) and to determine whether the incidence of CDI is related to the incidence of seasonal influenza.Design.A retrospective study of patients in the Nationwide Inpatient Sample during the period from 1998 through 2005.Methods.We identified all hospitalizations with a primary or secondary diagnosis of CDI with use of International Classification of Diseases, 9th Revision, Clinical Modification codes, and we did the same for influenza. The incidence of CDI was modeled as an autoregression about a linear trend. To investigate the association of CDI with influenza, we compared national and regional CDI and influenza series data and calculated cross-correlation functions with data that had been prewhitened (filtered to remove temporal patterns common to both series). To estimate the burden of seasonal CDI, we developed a proportional measure of seasonal CDI.Results.Time-series analysis of the monthly number of CDI cases reveals a distinct positive linear trend and a clear pattern of seasonal variation (R2 = 0.98). The cross-correlation functions indicate that influenza activity precedes CDI activity on both a national and regional basis. The average burden of seasonal (ie, winter) CDI is 23%.Conclusions.The epidemiologic characteristics of CDI follow a pattern that is seasonal and associated with influenza, which is likely due to antimicrobial use during influenza seasons. Approximately 23% of average monthly CDI during the peak 3 winter months could be eliminated if CDI remained at summer levels.

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