EMD Based on Time-Sequence and Window Function and its Application in Diagnosis of Machinery Faults

2013 ◽  
Vol 470 ◽  
pp. 353-356
Author(s):  
Ren Ze Luo ◽  
Peng Cao ◽  
Yuan Hua Fu ◽  
Ya Bin Huang ◽  
Qing Yang
Keyword(s):  
Signal Analysis ◽  
Moving Average ◽  
Time Sequence ◽  
Window Function ◽  
Autoregressive Moving Average ◽  
End Effect ◽  
Improved Method ◽  
Mode Decomposition ◽  
Time Varying Parameter ◽  
The Impact

To solve the end effect occurring in empirical mode decomposition adopted in the course of decomposition, we propose an improved method on the basis of time-sequence analysis and cosine window function. First, the ARMA (Autoregressive Moving Average) of time-varying parameter is adopted to extend signals, and thus the extended data can be smoothly connected with the original signal at the end. Second, the extended signals are processed with cosine window, so that the extended errors will exert no impact on the existing data. Finally, the signals processed as above mentioned will be decomposed with EMD to confine the end effect to the ends of the signal. The simulation and fault signal analysis prove that the proposed method can effectively reduce the impact of the end effect and be applied in rotating machinery fault diagnosis.

scholarly journals The time series regression analysis in evaluating the economic impact of COVID-19 cases in Indonesia

2021 ◽  
Vol 16 (3) ◽  
pp. 197-210
Author(s):  
Utriweni Mukhaiyar ◽  
Devina Widyanti ◽  
Sandy Vantika
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Transfer Function Model ◽  
Function Model ◽  
Time Series Regression ◽  
Vector Autoregressive ◽  
Daily Data ◽  
The Impact

This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.

CUSUM chart to monitor autocorrelated counts using Negative Binomial GARMA model

2017 ◽  
Vol 27 (9) ◽  
pp. 2859-2871 ◽  
Author(s):  
Orlando Yesid Esparza Albarracin ◽  
Airlane Pereira Alencar ◽  
Linda Lee Ho
Keyword(s):  
Control Charts ◽  
Negative Binomial ◽  
Moving Average ◽  
Health Surveillance ◽  
Autoregressive Moving Average ◽  
Cumulative Sum ◽  
Moving Average Models ◽  
Cumulative Sum Control ◽  
The Impact ◽  
Autoregressive Moving Average Models

Cumulative sum control charts have been used for health surveillance due to its efficiency to detect soon small shifts in the monitored series. However, these charts may fail when data are autocorrelated. An alternative procedure is to build a control chart based on the residuals after fitting autoregressive moving average models, but these models usually assume Gaussian distribution for the residuals. In practical health surveillance, count series can be modeled by Poisson or Negative Binomial regression, this last to control overdispersion. To include serial correlations, generalized autoregressive moving average models are proposed. The main contribution of the current article is to measure the impact, in terms of average run length on the performance of cumulative sum charts when the serial correlation is neglected in the regression model. Different statistics based on transformations, the deviance residual, and the likelihood ratio are used to build cumulative sum control charts to monitor counts with time varying means, including trend and seasonal effects. The monitoring of the weekly number of hospital admissions due to respiratory diseases for people aged over 65 years in the city São Paulo-Brazil is considered as an illustration of the current method.

Role of Exposure in Analysis of Road Accidents

2005 ◽  
Vol 1908 (1) ◽  
pp. 96-103 ◽  
Author(s):  
Filip Van den Bossche ◽  
Geert Wets ◽  
Tom Brijs
Keyword(s):  
Traffic Safety ◽  
Moving Average ◽  
Added Value ◽  
Autoregressive Moving Average ◽  
Road Accidents ◽  
Safety Research ◽  
Primary Determinant ◽  
Missing Variables ◽  
The Impact

Exposure is a key variable in traffic safety research. In the literature, it is noted as the first and primary determinant of traffic safety. In many cases, however, no valid exposure measure is available. In Belgium, monthly traffic counts for 12 years are available. This offers the opportunity to investigate the added value of exposure in models, next to legal, economic, and climatologic variables. Multiple regression with autoregressive moving average (ARMA) errors is used to quantify the impact of these factors on aggregated traffic safety. For each dependent variable, a model with and without exposure is constructed. The models show that exposure is significantly related to the number of accidents with persons killed and seriously injured and to the corresponding victims, but not to the lightly injured outcomes. Moreover, the addition or deletion of exposure does not influence the effects of the remaining variables in the model. The effects of exposure clearly depend on the type of measure used and on the time horizon considered. The framework of a regression model with ARMA errors allows for missing variables being accounted for by the error term. Even without a variable such as exposure, valid models can be constructed.

scholarly journals Risk Prediction and Assessment: Duration, Infections, and Death Toll of the COVID-19 and Its Impact on China’s Economy

2020 ◽  
Vol 13 (4) ◽  
pp. 66 ◽  
Author(s):  
Xiao-Guang Yue ◽  
Xue-Feng Shao ◽  
Rita Yi Man Li ◽  
M. James C. Crabbe ◽  
Lili Mi ◽  
...  
Keyword(s):  
Inflection Point ◽  
Moving Average ◽  
Comparison Method ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model ◽  
The Impact ◽  
China’S Economy

This study first analyzes the national and global infection status of the Coronavirus Disease that emerged in 2019 (COVID-19). It then uses the trend comparison method to predict the inflection point and Key Point of the COVID-19 virus by comparison with the severe acute respiratory syndrome (SARS) graphs, followed by using the Autoregressive Integrated Moving Average model, Autoregressive Moving Average model, Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors, and Holt Winter’s Exponential Smoothing to predict infections, deaths, and GDP in China. Finally, it discusses and assesses the impact of these results. This study argues that even if the risks and impacts of the epidemic are significant, China’s economy will continue to maintain steady development.

The Impact of Aggregate Level Alcohol Consumption on Homicide Rates: A Time Series Analysis

2021 ◽  
pp. 0306624X2110287
Author(s):  
TK Vinod Kumar
Keyword(s):  
Time Series ◽  
Alcohol Consumption ◽  
Moving Average ◽  
Policy Implications ◽  
Autoregressive Moving Average ◽  
Aggregate Level ◽  
Homicide Rates ◽  
Time Series Analyses ◽  
The Impact ◽  
The Relationship

Consumption of alcohol has an impact on violent crimes and homicides. The study examines the association between aggregate level consumption of spirit and homicide rates in the State of Kerala in India. Time-series analyses were conducted by building Autoregressive Moving Average with Exogenous Variables (ARMAX) models and OLS Regression models to explain the relationship between the monthly rate of consumption of alcoholic spirits and homicide rates. The study concludes that consumption of alcoholic spirits has a statistically significant impact on the total homicide rates and the male and female homicide rates. The study has significant policy implications being one of the first studies examining the relationship between alcohol consumption and homicide rates in India and suggesting methods to address challenges of adverse public health consequences associated with alcohol consumption.

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