Fault Detection for MIMO Systems Integrating Multivariate Statistical Analysis and Identification Methods

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.