Real time data anomaly detection in operating engines by statistical smoothing technique

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...

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Bibliographic Details
Published in2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) pp. 1 - 5
Main Authors Kumar, A., Srivastava, A., Bansal, N., Goel, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2012
Subjects
anomaly
Autoregressive processes
deviation
exceedance analysis
Smoothing methods
smoothing technique
Temperature
Temperature distribution
Temperature measurement
Temperature sensors
Time series analysis
Turbines
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ISBN1467314315
9781467314312
ISSN0840-7789
DOI10.1109/CCECE.2012.6334876

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Summary: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.
ISBN:1467314315
9781467314312
ISSN:0840-7789
DOI:10.1109/CCECE.2012.6334876