Sequence Mining based Alarm Suppression
Post date: Feb 10, 2018 8:45:27 PM
To provide more insight into the process dynamics and represent the temporal relationships among faults, control actions and process variables we propose of a multi-temporal sequence mining based algorithm. The methodology starts with the generation of frequent temporal patterns of the alarm signals. We transformed the multi-temporal sequences into Bayes classifiers. The obtained association rules can be used to define alarm suppression rules. We analyzed the dataset of a laboratory-scale water treatment testbed to illustrate that multi-temporal sequences are applicable for the description of operation patterns. We extended the benchmark simulator of a vinyl acetate production technology to generate easily reproducible results and stimulate the development of alarm management algorithms. The results of detailed sensitivity analyses confirm the benefits of the application of temporal alarm suppression rules which are reflecting the dynamical behaviour of the process.
The files are the supplementary materials of our paper will be published in IEEE Access, 2018 For the extended simulator of the vinyl acetate production technology and the source codes of the Bayes’ theorem-based evaluation of sequences see: https://github.com/abonyilab/VACsimulator
The MATLAB implementation of the sequence mining algorithm is available at: https://github.com/abonyilab/Multi-temporal-sequence-mining