Fault Detection for Large Scale Systems Using Dynamic Principal Components Analysis with Adaptation

Jesús Mina, Cristina Verde


The Dynamic Principal Component Analysis is an adequate tool for the monitoring of large scale systems based on the model of multivariate historical data under the assumption of stationarity, however, false alarms occur for non-stationary new observations during the monitoring phase. In order to reduce the false alarms rate, this paper extends the DPCA based monitoring for non-stationary data of linear dynamic systems, including an on-line means estimator to standardize new observations according to the estimated means. The effectiveness of the proposed methodology is evaluated for fault detection in a interconnected tanks system.


Fault Detection, Statistical Analysis, Dynamic Principal Component Analysis, Time Series Analysis, Non-Stationary Signals

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DOI: https://doi.org/10.15837/ijccc.2007.2.2351

Copyright (c) 2017 Jesús Mina, Cristina Verde

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