Fault Detection Using Dynamic Principal Component Analysis and Statistical Parameters Estimation
نویسنده
چکیده
One of the most popular multivariate statistical methods used for signals based process monitoring and data compression is the Dynamic Principal Component Analysis. This method computes the orthogonal principal directions assuming stationarity in the time series of the process, however, if observations are not stationary, false alarms could be generated during the fault detection and isolation task. To reduce the false alarms rate, this paper extends the dynamic principal component analysis for the case on non stationary data. This is achieved including in the monitoring procedure an on-line mean estimator and standardizing the time series data of the process according to the values generated by the estimator. As study case the detection of faults in a flow control valve has been used, in which it is assumed that the control signal, stem displacement and flow are measured signals. Simulator data are used to adjust the procedure and show the improvement of the novel dynamical principal component analysis methodology.
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