Title :
Real time data anomaly detection in operating engines by statistical smoothing technique
Author :
Kumar, Ajit ; Srivastava, Anurag ; Bansal, N. ; Goel, Ankush
Author_Institution :
Tecsis Corp., Ottawa, ON, Canada
fDate :
April 29 2012-May 2 2012
Abstract :
Time series temperature data from an industrial steam turbine are used in the present analysis to develop methodology for anomaly detection. Simple and exponential smoothing techniques are used to study the effectiveness of the technique for prediction considering different periods for analysis. The analysis of the lags between the predicted and observed data is performed using associated parameters like average deviation, root mean square deviation and split error. Exceedance test is also applied to the data set and the results obtained are found to be consistent and satisfactory in identifying sharp anomaly in the observed real time data.
Keywords :
condition monitoring; fault diagnosis; mean square error methods; steam engines; steam turbines; time series; average deviation; exceedance test; industrial steam turbines; operating engines; real time data anomaly detection; root mean square deviation; split error; statistical smoothing technique; time series temperature data; Autoregressive processes; Smoothing methods; Temperature distribution; Temperature measurement; Temperature sensors; Time series analysis; Turbines; Temperature; anomaly; deviation; exceedance analysis; smoothing technique;
Conference_Titel :
Electrical & Computer Engineering (CCECE), 2012 25th IEEE Canadian Conference on
Conference_Location :
Montreal, QC
Print_ISBN :
978-1-4673-1431-2
Electronic_ISBN :
0840-7789
DOI :
10.1109/CCECE.2012.6334876
