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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.

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In this paper the false alarms issue, generated by a fault detection scheme based in a Dynamic Principal Components Analysis (DPCA) is tackled. This problem occurs if the nominal operation point of the system changes. It is shown how input-output models can be identified simultaneously from the DPCA based statistical modeling, and how this deterministic models can be used in a complementary way in order to develop a fault detection scheme able to distinguish between the variations due to changes in the operation point and those due to faults. During the monitoring stage, we propose to use a fixed statistical model and an adaptive standardization of the input and output signals. The effectiveness of the proposed methodology is evaluated for fault detection in an interconnected tanks system.