Weighted Data-Driven Fault Detection and Isolation: A Subspace-Based Approach and Algorithms

Well-established theory of subspace system identification and model-based fault detection and isolation (FDI) enable the birth of subspace-based data-driven FDI approach. In this paper, we develop subspace-based FDI approach with a scheme of weighted historical and operating data. We propose two kinds of weighted data-driven fault detection algorithms and present fault isolation algorithm and its modified version incorporated with forgetting factors. Analysis of sensitivity and precision shows the weighted algorithms can obtain more accurate results without loss of sensitivity. Effectiveness and improvements of the proposed algorithms are validated on the widely used benchmark platform of Tennessee-Eastman process (TEP).