IMPLEMENTATION ISSUES OF TIME-SERIES BASED STATISTICAL PROCESS CONTROL

2009 ◽  
Vol 4 (3) ◽  
pp. 263-276 ◽  
Author(s):  
LAYTH C. ALWAN ◽  
DARRELL RADSON
Keyword(s):  
Time Series ◽  
Process Control ◽  
Statistical Process Control ◽  
Statistical Process

scholarly journals USAGE OF CONTROL CHARTS FOR TIME SERIES ANALYSIS IN FINANCIAL MANAGEMENT

2014 ◽  
Vol 16 (1) ◽  
pp. 138-158 ◽  
Author(s):  
Martin Kovářík ◽  
Libor Sarga ◽  
Petr Klímek
Keyword(s):  
Time Series ◽  
Process Control ◽  
Statistical Process Control ◽  
Financial Management ◽  
Financial Stability ◽  
Control Charts ◽  
Practical Interest ◽  
Cash Flows ◽  
Statistical Process ◽  
Financial Flows

We will deal with corporate financial proceeding using statistical process control, specifically time series control charts. The article outlines intersection of two disciplines, namely econometrics and statistical process control. Theoretical part discusses methodology of time series control charts, and in research part, the methodology is demonstrated on two case studies. The first focuses on analysis of Slovak currency from the perspective of its usefulness for generating profits through time series control charts. The second involves regulation of financial flows for a heteroskedastic financial process by EWMA and ARIMA control charts. We use Box-Jenkins methodology to find models of time series of annual Argentinian Gross Domestic Product available as a basic index from 1951–1998. We demonstrate the versatility of control charts not only in manufacturing but also in managing financial stability of cash flows. Specifically, we show their sensitivity in detecting even small shifts in mean which may indicate financial instability. This analytical approach is widely applicable and therefore of theoretical and practical interest.

Wind Turbine Gearbox Failure Prediction Based on Time Series Analysis and Statistical Process Control

2011 ◽  
Vol 347-353 ◽  
pp. 2236-2240 ◽  
Author(s):  
Fei Fei Wang ◽  
Xiao Qing Xiao ◽  
Hong Shan Zhao
Keyword(s):  
Time Series ◽  
Process Control ◽  
Wind Turbine ◽  
Statistical Process Control ◽  
Arima Model ◽  
Failure Prediction ◽  
Temperature Data ◽  
Operating Conditions ◽  
Statistical Process ◽  
Normal Operating

The Time Series method and Statistical Process Control strategy is applied to predict failures of wind turbine gearboxes. First, based on the real-time temperature data of gearboxes measured by temperature sensors, the temperature prediction model under normal operating conditions is established by ARIMA model. The analysis of the predicted values and the actual values of gearbox temperature is done, and proves that its residuals are normally distributed; then combined with statistical process control (SPC) methods, the big number of temperature data is used to calculate the standard deviation(σ) of residuals, and the gearbox failure threshold will be identified; Finally, the temperature data are analyzed both in normal operating condition and the failure condition to determine the operation status of the gearbox, statistical analysis and residual charts are carried out for gearbox failure prediction, verifying the feasibility and effectiveness of the proposed method.

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