Process operators often have process faults and alarms due to recurring failures on process equipment. It is also the case that some processes do not have enough input information or process models to use conventional modelling or machine learning techniques for early fault detection. A proof of concept for online streaming prediction software based on matching process behaviour to historical motifs has been developed, making use of the Basic Local Alignment Search Tool (BLAST) used in the Bioinformatics field. Execution times of as low as 1 second have been recorded, demonstrating that online matching is feasible. Three techniques have been tested and compared in terms of their computational effciency, robustness and selectivity, with results shown in Table 1: • Symbolic Aggregate Approximation combined with PSI-BLAST • Naive Triangular Representation with PSI-BLAST • Dynamic Time Warping Table 1: Properties of different motif-matching methods Property SAX-PSIBLAST TER-PSIBLAST DTW Noise tolerance (Selectivity) Acceptable Inconclusive Good Vertical Shift tolerance None Perfect Poor Matching speed Acceptable Acceptable Fast Match speed scaling O < O(mn) O < O(mn) O(mn) Dimensionality Reduction Tolerance Good Inconclusive Acceptable It is recommended that a method using a weighted confidence measure for each technique be investigated for the purpose of online process event handling and operator alerts. Keywords: SAX, BLAST, motif-matching, Dynamic Time Warping / Dissertation (MEng)--University of Pretoria, 2012. / Chemical Engineering / unrestricted