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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...
Data based approach and methodology for real time diagnosis and prognosis solutions for thermomechanical systems is discussed. Coated turbine blade operation is emulated to sensor online temperature data as the real-time inputs for the software code developed. An algorithm is presented first and extended sampling based statistical hypothesis tests are used for anomaly detection tests. Paired t-test...
This paper describes an approach and methodology for data anomaly detection and fault isolation for a diagnostic solution of hot section aeroengine components. The essence of the methodology is the combination of qualitative data exceedence check and quantitative data analysis. Three data sets, namely reference, operational and test data and two significance test methods are proposed and tested using...
Physical damages like thermally grown oxide (TGO) layer develop in the coated gas turbine blade during the aeroengine operation and severely limits the life and performance. This life critical damage data has been considered for the validation of a diagnostic fault analysis. High level architecture, statistical algorithm and experimental modeling of oxide growth data are discussed. Experimental measurement...
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