Abstract:
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
