Spatially-temporally online fault detection using timed multivariate statistical logic

This work develops an extension of temporal logic called timed multivariate statistical logic (TMSL) that can specify not only spatial features but also temporal dynamics of systems in a formal way. A purely data-based algorithm is presented to automatically learn the TMSL from process data. First, the principal component analysis (PCA) method is used to extract spatial features among all available process data. Next, based on these spatial features, a large margin fuzzy c-means method is developed to automatically discover a set of meaningful regions called Regions-of-Interest. As a result, the data space is partitioned into a set of Regions-of-Interest. Then, a temporally-annotated automaton for TMSL is generated with these discovered Regions-of-Interest. Finally, a PCA-based spatial monitor and a TMSL-based temporal monitor are further developed for online fault detection. For performance validation, the proposed online fault detection method is demonstrated in three application studies.